u/Nearby_Indication474

https://doi.org/10.5281/zenodo.23143605

Mistral-7B-Instruct-v0.3:

I want to start with the simplest possible explanation of what I have been building.

Imagine taking a piece of information, letting a language model process it once, and then throwing the original text away.

No sentence stored in a database.

No paragraph hidden somewhere.

No readable summary.

No RAG system fetching the original document.

No fine-tuning.

No LoRA.

No weight update.

What remains is numerical transformer memory derived from the model's own internal computation. I package that numerical memory into what I call a Cognitive Cartridge. Later, I can install that cartridge back into the frozen model and ask questions about the information that produced it — without putting the original source text back into the readout prompt.

That was the first result. The new result is more important:

I have now reproduced the Cognitive Cartridge architecture on two different 7B transformer model families.

Qwen2.5-7B-Instruct.

And now Mistral-7B-Instruct-v0.3.

The implementations are not numerically identical. The architectures are different, the layer counts are different, the KV structures are different, and the working cartridge configurations are different. But the central mechanism survived the move.

That is the reason I am publishing this second record.

The question is no longer only:

“Can I make this happen once on Qwen?”

Now there is a second implementation on Mistral. And the Mistral result is the cleanest version so far.

What is actually inside a Cognitive Cartridge?

This is probably the most important thing to understand.

Suppose the source record says:

Object: amber sextant

Container: RQ-415

Location: elm lodge

That text exists during the forging stage. The frozen transformer processes it. NIRVANA takes source-derived internal transformer K/V states and represents their content numerically using a fixed, source-independent codebook.

In the released Mistral implementation, both K and V are compressed to D120 while a source-specific OWN component is preserved. After forging, the source record is not supplied to the readout prompt.

So the conceptual transformation is:

human language

→ frozen transformer computation

→ internal K/V states

→ compressed numerical Cognitive Cartridge

Then later:

numerical Cognitive Cartridge

→ reconstructed transformer K/V memory

→ frozen transformer

→ language

Or, in the shortest form:

language → internal numerical memory → language

The middle is no longer human-readable source text. That distinction matters.

A cartridge is not a text file with a different name.

It is not a vector database containing the original sentence.

It is not a prompt template.

It is not conventional RAG returning the source paragraph.

It is not a fine-tuned model.

The model weights remain frozen. The information is carried by a numerical representation derived from transformer K/V memory.

Why call it a cartridge?

Think less about a document and more about an interchangeable machine-readable memory module. The base model stays where it is. Knowledge packages can be forged separately. Those packages can remain separate. They can be installed and queried without retraining the base model.

This becomes much more interesting when there is more than one cartridge.

The new Mistral experiment uses 16 independently forged cartridges. Each one contains a numerical representation derived from a separate source record. They are not concatenated into one giant text prompt. They remain independent memories.

For example, in human-readable form, imagine one cartridge represents:

amber sextant → RQ-415 → elm lodge

Now ask the cartridge bank:

Which container is associated with the amber sextant?

The query is evaluated against the independent cartridge memories.

The relevant cartridge returns:

RQ-415

The unrelated cartridges return:

NONE

There is no learned router secretly selecting the correct cartridge before this happens.

Then the recovered identifier can be used for a second lookup:

Where is RQ-415?

The bank is queried again.

The relevant memory returns:

elm lodge

The unrelated memories return:

NONE

So the complete retrieval becomes:

amber sextant

→ RQ-415

→ elm lodge

The important point is that the model did not reread the original source record to answer either stage. It operated from reconstructed numerical transformer memory.

The Mistral result

The final public Mistral run used:

Mistral-7B-Instruct-v0.3

32 transformer layers

hidden size 4096

32 attention heads

8 KV heads

BF16 / SDPA

K = D120

V = D120

OWN preserved

16 independent Cognitive Cartridges

frozen model weights

greedy decoding

There was:

no fine-tuning

no LoRA

no optimizer

no learned router

no model-weight update

The recorded final run produced:

Object → container ID: 16/16

Container ID → location: 16/16

Complete two-stage retrieval: 16/16

Missing-object controls: 8/8

Absent-ID controls: 8/8

NOMEM controls: 8/8

But there is another result I think is just as important.

For every target query, there are 15 unrelated cartridges.

16 queries × 15 unrelated cartridges = 240 unrelated cartridge reads.

Stage 1 unrelated-cartridge rejection:

240/240 NONE

Stage 2 unrelated-cartridge rejection:

240/240 NONE

That means the result is not simply:

“The correct memory can say something.”

The system also demonstrated, in this controlled panel:

“The memories that do not contain the requested relation can refuse to claim that they do.”

For a modular memory system, I think this distinction is fundamental. A memory bank that can retrieve information but cannot distinguish relevance from irrelevance becomes increasingly dangerous as it grows.

The interesting problem is not only remembering. It is also knowing which memory does not answer the question.

The no-memory control matters for the same reason. When the cartridge memory was removed, the target relations were not recovered.

NOMEM:

8/8 controls passed.

The source-removal audit passed.

The frozen-weight sentinel passed.

The model remained frozen.

This is why I consider the Mistral result an important step beyond the first demonstration.

The first Qwen release established the architecture publicly. The Mistral release gives us something else:

cross-model evidence.

Qwen and Mistral are not the same transformer.

Qwen2.5-7B-Instruct uses 28 transformer layers.

Mistral-7B-Instruct-v0.3 uses 32.

Their internal configurations differ. The working compression configurations also differ.

The Qwen public implementation used:

K120 / V128 / OWN

The Mistral implementation uses:

K120 / V120 / OWN

I did not simply copy a cache from one model into another. Each model builds and reconstructs its own source-derived internal memory.

What transferred was the architecture:

source

→ internal transformer memory

→ numerical compression

→ independent cartridge

→ source removed

→ reconstructed K/V

→ frozen-model readout

That is the bridge between the two releases.

I would not call two models proof of universal compatibility with every transformer architecture. That would be scientifically too strong.

But it is now evidence that Cognitive Cartridge is not merely one accidental Qwen-specific behavior. The same broader architecture has been implemented and publicly reproduced on a second transformer family.

That changes the research question.

The first question was:

Can this mechanism exist at all?

The next question became:

Can multiple independent memories coexist?

Then:

Can one retrieved result lead to another retrieval?

Then:

Can unrelated memories reject a query instead of contaminating the answer?

And now:

Does the architecture survive a move to another model family?

We now have experimental answers to each of those questions.

So I am moving to the next problem:

scale.

16 cartridges are not the destination. They are the current experimental bank size.

From this point, I am much less interested in making another small demonstration simply to produce another perfect score. The next objective is to increase the number of independently retained cartridges and find where the architecture actually begins to break.

32.

33.

34.

Then larger banks if the mechanism survives.

At that point the difficult questions become different:

How do you search a large bank without destroying memory isolation?

How should cartridges be indexed?

Can retrieval become hierarchical?

Can groups of cartridges form higher-level memory structures?

How does latency scale?

How does numerical storage scale?

At what point does relevance rejection begin to fail?

Can a retrieved memory activate another relevant memory without an external text retrieval system deciding everything first?

Those are the questions I want to attack next.

There is another part of this experiment that surprised me: the layer behavior.

A frozen transformer is not a passive container. When you reconstruct numerical K/V memory and inject it back into inference, you are interacting with a very sensitive computational system.

So during the Mistral development series I also performed controlled cumulative K, V and K+V ablations to see where cartridge information remained functionally sufficient.

The results were not uniform across depth. For K, the observed functional boundary extended differently than for V.

For combined K+V, one particularly sharp controlled transition occurred between:

L12: 8/8

L13: 0/8

This does not mean “the memory lives at layer 12.” That would be an incorrect interpretation.

What it means is that under the specific cumulative ablation protocol, functional sufficiency changed sharply across that boundary. V also remained important farther downstream than K in the measured configuration.

I think this is an important reminder of how delicate these systems are.

We are not writing a sentence into a spare memory slot. We are reconstructing numerical states that participate in a 7-billion-parameter transformer's computation. Small changes in where and how that state is reconstructed can change downstream behavior.

That is one reason I publish the code and raw logs rather than only posting the final accuracy number.

The frozen-model checks are also part of the experiment. The released run verifies that there are no trainable tensors involved in the memory mechanism.

No LoRA.

No optimizer.

No weight update.

The model-weight sentinel remains unchanged.

So when the system answers from a cartridge, the experiment is specifically designed to separate cartridge memory from weight modification.

Why do I think this direction matters?

Today, we often approach knowledge in language models through two extremes.

Either we try to put enormous amounts of knowledge into the model weights. Or we keep the knowledge outside the model and retrieve human-readable text when needed.

There may be useful territory between those approaches.

A frozen model with removable machine-native internal memory modules.

Not everything in the weights.

Not everything repeatedly pasted back as text.

Instead:

a base computational model

a bank of independently forged internal memories.

Imagine a specialized system where validated domain knowledge can exist as modular memory packages.

Engineering.

Aviation.

Law.

Industrial maintenance.

Scientific literature.

Company procedures.

Agent experience.

A model might not need every possible piece of knowledge active in its context simultaneously. It may need the right memory at the right time.

And because the memory representation is numerical transformer state rather than the original human-readable document, this opens a different engineering space from conventional document retrieval.

There is also a longer-term question here.

I am not claiming biological memory.

I am not claiming AGI.

And I am not claiming that transformer KV states work like the human brain.

But modular internal memory raises an interesting architectural question.

What happens when an artificial system does not have 16 memories, but 100,000?

Or millions?

What happens if memories can remain independent, become addressable, reject irrelevant activation, and allow the output of one memory to lead to another?

At some point the problem stops looking like “how much text can I fit into a context window?”

It starts looking like:

How should an artificial system organize memory?

That is the direction I find interesting.

Why publish the full record?

Because a screenshot of 16/16 proves very little.

Both public releases have permanent Zenodo records. The code is public. The raw execution logs are public. The visual technical records are public. The model configuration is public. The controls are public. The frozen-weight checks are public.

The Qwen release even preserves its imperfect public result rather than hiding it.

Qwen public demonstration:

14/16

Mistral final demonstration:

16/16

That difference is also useful.

The point of the project is not to make every historical run look perfect. The point is to leave a technical trail showing what worked, what did not, what changed, and whether the underlying architecture survived.

For anyone who wants to inspect the Qwen → Mistral bridge directly, the two permanent records are at the top of this post.

The complete current Mistral implementation is here:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_ful\_MISTRAL.py

Complete Mistral raw execution log:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log\_MISTRAL.log

The same Mistral implementation is also split into three parts for easier mobile / Colab handling:

Part 1:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part1.Mistral.py

Part 2:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part2.Mistral.py

Part 3:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore\_part3\_mistral.py

For comparison, the previous Qwen implementation:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_CC.py

Previous Qwen raw execution log:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log.Nirvana.cc.log

Repository:

https://github.com/ceceli33/titan-cognitive-core-v2

AKBASCORE NIRVANA — Cognitive Cartridge

Inventor / Developer: Mustafa Akbaş

Two model families.

Frozen weights.

Independent numerical memories.

Original source absent at readout.

The mechanism survived the move.

Now the question is no longer whether a cartridge can exist.

The question is how many cartridges a frozen model can carry.

u/Nearby_Indication474 — 11 days ago

Part 1: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part1.Mistral.py Part 2: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part2.Mistral.py Part 3: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore\_part3\_mistral.py

AKBASCORE NIRVANA — I Built Removable Numerical Memory Cartridges for Two Different Frozen 7B LLMs. Qwen and Mistral Both Work. Now I’m Scaling the Memory Bank.

Zenodo permanent records: Qwen2.5-7B-Instruct: https://doi.org/10.5281/zenodo.23127434 Mistral-7B-Instruct-v0.3: https://doi.org/10.5281/zenodo.23143605

I want to start with the simplest possible explanation of what I have been building.

Imagine taking a piece of information, letting a language model process it once, and then throwing the original text away.

No sentence stored in a database. No paragraph hidden somewhere. No readable summary. No RAG system fetching the original document. No fine-tuning. No LoRA. No weight update.

That was the first result. The new result is more important:

I have now reproduced the Cognitive Cartridge architecture on two different 7B transformer model families.

Qwen2.5-7B-Instruct. And now Mistral-7B-Instruct-v0.3.

That is the reason I am publishing this second record.

The question is no longer only: “Can I make this happen once on Qwen?”

Now there is a second implementation on Mistral. And the Mistral result is the cleanest version so far.

What is actually inside a Cognitive Cartridge?

This is probably the most important thing to understand.

Suppose the source record says: Object: amber sextant Container: RQ-415 Location: elm lodge

In the released Mistral implementation, both K and V are compressed to D120 while a source-specific OWN component is preserved. After forging, the source record is not supplied to the readout prompt.

So the conceptual transformation is:

human language → frozen transformer computation → internal K/V states → compressed numerical Cognitive Cartridge

Then later:

numerical Cognitive Cartridge → reconstructed transformer K/V memory → frozen transformer → language

Or, in the shortest form:

language → internal numerical memory → language

The middle is no longer human-readable source text. That distinction matters.

A cartridge is not a text file with a different name. It is not a vector database containing the original sentence. It is not a prompt template. It is not conventional RAG returning the source paragraph. It is not a fine-tuned model.

The model weights remain frozen. The information is carried by a numerical representation derived from transformer K/V memory.

Why call it a cartridge?

This becomes much more interesting when there is more than one cartridge.

For example, in human-readable form, imagine one cartridge represents:

amber sextant → RQ-415 → elm lodge

Now ask the cartridge bank:

Which container is associated with the amber sextant?

The query is evaluated against the independent cartridge memories.

The relevant cartridge returns: RQ-415

The unrelated cartridges return: NONE

There is no learned router secretly selecting the correct cartridge before this happens.

Then the recovered identifier can be used for a second lookup:

Where is RQ-415?

The bank is queried again.

The relevant memory returns: elm lodge

The unrelated memories return: NONE

So the complete retrieval becomes:

amber sextant → RQ-415 → elm lodge

The important point is that the model did not reread the original source record to answer either stage. It operated from reconstructed numerical transformer memory.

The Mistral result

The final public Mistral run used:

Mistral-7B-Instruct-v0.3 32 transformer layers hidden size 4096 32 attention heads 8 KV heads BF16 / SDPA K = D120 V = D120 OWN preserved 16 independent Cognitive Cartridges frozen model weights greedy decoding

There was:

no fine-tuning no LoRA no optimizer no learned router no model-weight update

The recorded final run produced:

Object → container ID: 16/16 Container ID → location: 16/16 Complete two-stage retrieval: 16/16 Missing-object controls: 8/8 Absent-ID controls: 8/8 NOMEM controls: 8/8

But there is another result I think is just as important.

For every target query, there are 15 unrelated cartridges.

16 queries × 15 unrelated cartridges = 240 unrelated cartridge reads.

Stage 1 unrelated-cartridge rejection: 240/240 NONE

Stage 2 unrelated-cartridge rejection: 240/240 NONE

That means the result is not simply: “The correct memory can say something.”

The system also demonstrated, in this controlled panel: “The memories that do not contain the requested relation can refuse to claim that they do.”

The interesting problem is not only remembering. It is also knowing which memory does not answer the question.

The no-memory control matters for the same reason. When the cartridge memory was removed, the target relations were not recovered.

NOMEM: 8/8 controls passed.

The source-removal audit passed. The frozen-weight sentinel passed. The model remained frozen.

This is why I consider the Mistral result an important step beyond the first demonstration.

The first Qwen release established the architecture publicly. The Mistral release gives us something else:

cross-model evidence.

Qwen and Mistral are not the same transformer.

Qwen2.5-7B-Instruct uses 28 transformer layers. Mistral-7B-Instruct-v0.3 uses 32.

Their internal configurations differ. The working compression configurations also differ.

The Qwen public implementation used: K120 / V128 / OWN

The Mistral implementation uses: K120 / V120 / OWN

I did not simply copy a cache from one model into another. Each model builds and reconstructs its own source-derived internal memory.

What transferred was the architecture:

source → internal transformer memory → numerical compression → independent cartridge → source removed → reconstructed K/V → frozen-model readout

That is the bridge between the two releases.

I would not call two models proof of universal compatibility with every transformer architecture. That would be scientifically too strong.

That changes the research question.

The first question was: Can this mechanism exist at all?

The next question became: Can multiple independent memories coexist?

Then: Can one retrieved result lead to another retrieval?

Then: Can unrelated memories reject a query instead of contaminating the answer?

And now: Does the architecture survive a move to another model family?

We now have experimental answers to each of those questions.

So I am moving to the next problem:

scale.

16 cartridges are not the destination. They are the current experimental bank size.

Then larger banks if the mechanism survives.

At that point the difficult questions become different:

How do you search a large bank without destroying memory isolation? How should cartridges be indexed? Can retrieval become hierarchical? Can groups of cartridges form higher-level memory structures? How does latency scale? How does numerical storage scale? At what point does relevance rejection begin to fail? Can a retrieved memory activate another relevant memory without an external text retrieval system deciding everything first?

Those are the questions I want to attack next.

There is another part of this experiment that surprised me: the layer behavior.

A frozen transformer is not a passive container. When you reconstruct numerical K/V memory and inject it back into inference, you are interacting with a very sensitive computational system.

So during the Mistral development series I also performed controlled cumulative K, V and K+V ablations to see where cartridge information remained functionally sufficient.

The results were not uniform across depth. For K, the observed functional boundary extended differently than for V.

For combined K+V, one particularly sharp controlled transition occurred between:

L12: 8/8 L13: 0/8

This does not mean “the memory lives at layer 12.” That would be an incorrect interpretation.

I think this is an important reminder of how delicate these systems are.

That is one reason I publish the code and raw logs rather than only posting the final accuracy number.

The frozen-model checks are also part of the experiment. The released run verifies that there are no trainable tensors involved in the memory mechanism.

No LoRA. No optimizer. No weight update.

The model-weight sentinel remains unchanged.

So when the system answers from a cartridge, the experiment is specifically designed to separate cartridge memory from weight modification.

Why do I think this direction matters?

Today, we often approach knowledge in language models through two extremes.

Either we try to put enormous amounts of knowledge into the model weights. Or we keep the knowledge outside the model and retrieve human-readable text when needed.

There may be useful territory between those approaches.

A frozen model with removable machine-native internal memory modules.

Not everything in the weights. Not everything repeatedly pasted back as text.

Instead:

a base computational model + a bank of independently forged internal memories.

Imagine a specialized system where validated domain knowledge can exist as modular memory packages.

Engineering. Aviation. Law. Industrial maintenance. Scientific literature. Company procedures. Agent experience.

A model might not need every possible piece of knowledge active in its context simultaneously. It may need the right memory at the right time.

And because the memory representation is numerical transformer state rather than the original human-readable document, this opens a different engineering space from conventional document retrieval.

There is also a longer-term question here.

I am not claiming biological memory. I am not claiming AGI. And I am not claiming that transformer KV states work like the human brain.

But modular internal memory raises an interesting architectural question.

What happens when an artificial system does not have 16 memories, but 100,000?

Or millions?

What happens if memories can remain independent, become addressable, reject irrelevant activation, and allow the output of one memory to lead to another?

At some point the problem stops looking like “how much text can I fit into a context window?”

It starts looking like:

How should an artificial system organize memory?

That is the direction I find interesting.

Why publish the full record?

Because a screenshot of 16/16 proves very little.

The Qwen release even preserves its imperfect public result rather than hiding it.

Qwen public demonstration: 14/16

Mistral final demonstration: 16/16

That difference is also useful.

For anyone who wants to inspect the Qwen → Mistral bridge directly, the two permanent records are at the top of this post.

The complete current Mistral implementation is here: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_ful\_MISTRAL.py

Complete Mistral raw execution log: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log\_MISTRAL.log

The same Mistral implementation is also split into three parts for easier mobile / Colab handling:

For comparison, the previous Qwen implementation: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_CC.py

Previous Qwen raw execution log: https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log.Nirvana.cc.log

Repository: https://github.com/ceceli33/titan-cognitive-core-v2

AKBASCORE NIRVANA — Cognitive Cartridge Inventor / Developer: Mustafa Akbaş

Two model families. Frozen weights. Independent numerical memories. Original source absent at readout. The mechanism survived the move.

Now the question is no longer whether a cartridge can exist.

The question is how many cartridges a frozen model can carry.

u/Nearby_Indication474 — 11 days ago

▲ 7 r/Qwen_AI

AKBASCORE NIRVANA — I Built Removable Numerical Memory Cartridges for Two Different Frozen 7B LLMs. Qwen and Mistral Both Work. Now I’m Scaling the Memory Bank.

Zenodo permanent records:

Qwen2.5-7B-Instruct:

https://doi.org/10.5281/zenodo.23143605

Mistral-7B-Instruct-v0.3:

I want to start with the simplest possible explanation of what I have been building.

Imagine taking a piece of information, letting a language model process it once, and then throwing the original text away.

No sentence stored in a database.

No paragraph hidden somewhere.

No readable summary.

No RAG system fetching the original document.

No fine-tuning.

No LoRA.

No weight update.

That was the first result. The new result is more important:

I have now reproduced the Cognitive Cartridge architecture on two different 7B transformer model families.

Qwen2.5-7B-Instruct.

And now Mistral-7B-Instruct-v0.3.

That is the reason I am publishing this second record.

The question is no longer only:

“Can I make this happen once on Qwen?”

Now there is a second implementation on Mistral. And the Mistral result is the cleanest version so far.

What is actually inside a Cognitive Cartridge?

This is probably the most important thing to understand.

Suppose the source record says:

Object: amber sextant

Container: RQ-415

Location: elm lodge

In the released Mistral implementation, both K and V are compressed to D120 while a source-specific OWN component is preserved. After forging, the source record is not supplied to the readout prompt.

So the conceptual transformation is:

human language

→ frozen transformer computation

→ internal K/V states

→ compressed numerical Cognitive Cartridge

Then later:

numerical Cognitive Cartridge

→ reconstructed transformer K/V memory

→ frozen transformer

→ language

Or, in the shortest form:

language → internal numerical memory → language

The middle is no longer human-readable source text. That distinction matters.

A cartridge is not a text file with a different name.

It is not a vector database containing the original sentence.

It is not a prompt template.

It is not conventional RAG returning the source paragraph.

It is not a fine-tuned model.

The model weights remain frozen. The information is carried by a numerical representation derived from transformer K/V memory.

Why call it a cartridge?

This becomes much more interesting when there is more than one cartridge.

For example, in human-readable form, imagine one cartridge represents:

amber sextant → RQ-415 → elm lodge

Now ask the cartridge bank:

Which container is associated with the amber sextant?

The query is evaluated against the independent cartridge memories.

The relevant cartridge returns:

RQ-415

The unrelated cartridges return:

NONE

There is no learned router secretly selecting the correct cartridge before this happens.

Then the recovered identifier can be used for a second lookup:

Where is RQ-415?

The bank is queried again.

The relevant memory returns:

elm lodge

The unrelated memories return:

NONE

So the complete retrieval becomes:

amber sextant

→ RQ-415

→ elm lodge

The important point is that the model did not reread the original source record to answer either stage. It operated from reconstructed numerical transformer memory.

The Mistral result

The final public Mistral run used:

Mistral-7B-Instruct-v0.3

32 transformer layers

hidden size 4096

32 attention heads

8 KV heads

BF16 / SDPA

K = D120

V = D120

OWN preserved

16 independent Cognitive Cartridges

frozen model weights

greedy decoding

There was:

no fine-tuning

no LoRA

no optimizer

no learned router

no model-weight update

The recorded final run produced:

Object → container ID: 16/16

Container ID → location: 16/16

Complete two-stage retrieval: 16/16

Missing-object controls: 8/8

Absent-ID controls: 8/8

NOMEM controls: 8/8

But there is another result I think is just as important.

For every target query, there are 15 unrelated cartridges.

16 queries × 15 unrelated cartridges = 240 unrelated cartridge reads.

Stage 1 unrelated-cartridge rejection:

240/240 NONE

Stage 2 unrelated-cartridge rejection:

240/240 NONE

That means the result is not simply:

“The correct memory can say something.”

The system also demonstrated, in this controlled panel:

“The memories that do not contain the requested relation can refuse to claim that they do.”

The interesting problem is not only remembering. It is also knowing which memory does not answer the question.

The no-memory control matters for the same reason. When the cartridge memory was removed, the target relations were not recovered.

NOMEM:

8/8 controls passed.

The source-removal audit passed.

The frozen-weight sentinel passed.

The model remained frozen.

This is why I consider the Mistral result an important step beyond the first demonstration.

The first Qwen release established the architecture publicly. The Mistral release gives us something else:

cross-model evidence.

Qwen and Mistral are not the same transformer.

Qwen2.5-7B-Instruct uses 28 transformer layers.

Mistral-7B-Instruct-v0.3 uses 32.

Their internal configurations differ. The working compression configurations also differ.

The Qwen public implementation used:

K120 / V128 / OWN

The Mistral implementation uses:

K120 / V120 / OWN

I did not simply copy a cache from one model into another. Each model builds and reconstructs its own source-derived internal memory.

What transferred was the architecture:

source

→ internal transformer memory

→ numerical compression

→ independent cartridge

→ source removed

→ reconstructed K/V

→ frozen-model readout

That is the bridge between the two releases.

I would not call two models proof of universal compatibility with every transformer architecture. That would be scientifically too strong.

That changes the research question.

The first question was:

Can this mechanism exist at all?

The next question became:

Can multiple independent memories coexist?

Then:

Can one retrieved result lead to another retrieval?

Then:

Can unrelated memories reject a query instead of contaminating the answer?

And now:

Does the architecture survive a move to another model family?

We now have experimental answers to each of those questions.

So I am moving to the next problem:

scale.

16 cartridges are not the destination. They are the current experimental bank size.

32.

33.

34.

Then larger banks if the mechanism survives.

At that point the difficult questions become different:

How do you search a large bank without destroying memory isolation?

How should cartridges be indexed?

Can retrieval become hierarchical?

Can groups of cartridges form higher-level memory structures?

How does latency scale?

How does numerical storage scale?

At what point does relevance rejection begin to fail?

Can a retrieved memory activate another relevant memory without an external text retrieval system deciding everything first?

Those are the questions I want to attack next.

There is another part of this experiment that surprised me: the layer behavior.

A frozen transformer is not a passive container. When you reconstruct numerical K/V memory and inject it back into inference, you are interacting with a very sensitive computational system.

So during the Mistral development series I also performed controlled cumulative K, V and K+V ablations to see where cartridge information remained functionally sufficient.

The results were not uniform across depth. For K, the observed functional boundary extended differently than for V.

For combined K+V, one particularly sharp controlled transition occurred between:

L12: 8/8

L13: 0/8

This does not mean “the memory lives at layer 12.” That would be an incorrect interpretation.

I think this is an important reminder of how delicate these systems are.

That is one reason I publish the code and raw logs rather than only posting the final accuracy number.

The frozen-model checks are also part of the experiment. The released run verifies that there are no trainable tensors involved in the memory mechanism.

No LoRA.

No optimizer.

No weight update.

The model-weight sentinel remains unchanged.

So when the system answers from a cartridge, the experiment is specifically designed to separate cartridge memory from weight modification.

Why do I think this direction matters?

Today, we often approach knowledge in language models through two extremes.

Either we try to put enormous amounts of knowledge into the model weights. Or we keep the knowledge outside the model and retrieve human-readable text when needed.

There may be useful territory between those approaches.

A frozen model with removable machine-native internal memory modules.

Not everything in the weights.

Not everything repeatedly pasted back as text.

Instead:

a base computational model

a bank of independently forged internal memories.

Imagine a specialized system where validated domain knowledge can exist as modular memory packages.

Engineering.

Aviation.

Law.

Industrial maintenance.

Scientific literature.

Company procedures.

Agent experience.

A model might not need every possible piece of knowledge active in its context simultaneously. It may need the right memory at the right time.

And because the memory representation is numerical transformer state rather than the original human-readable document, this opens a different engineering space from conventional document retrieval.

There is also a longer-term question here.

I am not claiming biological memory.

I am not claiming AGI.

And I am not claiming that transformer KV states work like the human brain.

But modular internal memory raises an interesting architectural question.

What happens when an artificial system does not have 16 memories, but 100,000?

Or millions?

What happens if memories can remain independent, become addressable, reject irrelevant activation, and allow the output of one memory to lead to another?

At some point the problem stops looking like “how much text can I fit into a context window?”

It starts looking like:

How should an artificial system organize memory?

That is the direction I find interesting.

Why publish the full record?

Because a screenshot of 16/16 proves very little.

The Qwen release even preserves its imperfect public result rather than hiding it.

Qwen public demonstration:

14/16

Mistral final demonstration:

16/16

That difference is also useful.

For anyone who wants to inspect the Qwen → Mistral bridge directly, the two permanent records are at the top of this post.

The complete current Mistral implementation is here:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_ful\_MISTRAL.py

Complete Mistral raw execution log:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log\_MISTRAL.log

The same Mistral implementation is also split into three parts for easier mobile / Colab handling:

Part 1:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part1.Mistral.py

Part 2:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore.part2.Mistral.py

Part 3:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore\_part3\_mistral.py

For comparison, the previous Qwen implementation:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_CC.py

Previous Qwen raw execution log:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log.Nirvana.cc.log

Repository:

https://github.com/ceceli33/titan-cognitive-core-v2

AKBASCORE NIRVANA — Cognitive Cartridge

Inventor / Developer: Mustafa Akbaş

Two model families.

Frozen weights.

Independent numerical memories.

Original source absent at readout.

The mechanism survived the move.

Now the question is no longer whether a cartridge can exist.

The question is how many cartridges a frozen model can carry.

u/Nearby_Indication474 — 11 days ago

▲ 2 r/TinyLlama_TITAN

I Built Removable Memory Cartridges for a Frozen 7B Language Model 16 Independent Memories, No Fine-Tuning, No LoRA, and the Original Source Is Gone at Readout

Zenodo permanent record:

AKBASCORE NIRVANA — Cognitive Cartridge has now been publicly archived and timestamped on Zenodo.

Release: v1.0_NIRVANA_Cognitive_Cartridge

AKBASCORE NIRVANA — Cognitive Cartridge: Multi-Cartridge Compressed PKV Memory for Frozen Language Models

The complete implementation, raw execution log and reproducible public demonstration are available below.

What if knowledge could be loaded into an AI like a cartridge?

Think about an old Atari or game console. The console stays the same. You insert one cartridge and it becomes one game. Remove it, insert another cartridge, and the same hardware does something completely different.

AKBASCORE NIRVANA explores a similar idea for language models.

But there is one detail that completely changes what “cartridge” means here:

There is no human language stored inside the cartridge.

No source sentence.

No paragraph.

No document.

No readable summary.

No hidden copy of the original text waiting to be pasted back into the prompt.

If I give the system a statement such as:

“Container VX-731 is located at the cobalt observatory.”

the cartridge does not simply store that sentence somewhere and retrieve it later.

The sentence is passed through the frozen transformer during the forging stage. What is captured comes from the model's own internal computation: the numerical key/value states produced inside its transformer layers. NIRVANA represents that internal K/V information through a source-independent numerical codebook and stores the resulting compressed numerical representation as a Cognitive Cartridge.

So after forging, we have crossed an important boundary:

human language → transformer internal state → compressed numerical memory

The cartridge is therefore not a tiny text file wearing a new name.

It is not a database row containing the sentence.

It is not conventional RAG returning the source paragraph.

It is not a prompt template.

It is not fine-tuning hidden behind another term.

The original knowledge has been transformed into a numerical representation of internal transformer memory.

And when the model is questioned later, we do not give the original sentence back to it.

The cartridge is reconstructed into transformer K/V memory, installed into the frozen model's inference context, and the model produces language from that internal numerical state.

In the simplest possible terms:

A human writes knowledge in language.

The model converts it into its own internal numerical memory.

NIRVANA packages that memory.

The human-language source is removed from readout.

Later, the frozen model receives the memory rather than rereading the source.

That distinction is the heart of Cognitive Cartridge.

Instead of changing the model's weights every time we want to give it specialized knowledge, NIRVANA takes source information, processes it once, and turns the resulting internal transformer memory into a removable Cognitive Cartridge. After that, the original source sentence does not need to be placed back into the question prompt. The model's weights remain frozen.

Knowledge goes in.

Language disappears from the stored cartridge representation.

The internal memory remains.

And now we have extended that idea beyond a single cartridge.

16 cartridges, one frozen model

The public implementation released here contains 16 independently forged Cognitive Cartridges. They do not simply become one giant text prompt. They remain separate memory records.

Imagine that one cartridge contains:

amber compass → VX-731

and another contains:

VX-731 → cobalt observatory

These arrows are a human-readable explanation of what the cartridges represent. They are not literal text strings stored inside the cartridges.

Now ask:

Where is the amber compass?

The system can first retrieve VX-731 from one cartridge. That intermediate result can then be used to address the cartridge bank again. A different cartridge supplies:

cobalt observatory

In other words, information stored in separate internal memory records can participate in a multi-stage retrieval chain.

This is the point where the project became much more interesting to me. We are no longer asking only:

Can information be compressed into the internal memory of a transformer and recovered later?

We are now asking:

Can independently created internal memories become a modular memory system, where one retrieved memory can lead to another?

That is what the multi-cartridge architecture is beginning to explore.

Why does this matter?

I see two major directions.

1 — Specialized AI and agents

Think of the base model as the console. The cartridges are the knowledge packages.

Imagine a legal knowledge cartridge constructed and validated by highly qualified lawyers. Another cartridge bank could represent the legal system of a particular country. Another could contain aviation procedures. Another could contain engineering knowledge. Another could contain company-specific operational knowledge. Another could be built for medicine, industrial maintenance, finance, scientific work or a specialized autonomous agent.

The important difference is that the underlying model does not have to be retrained for every package.

In the implementation released here there is:

no fine-tuning

no LoRA

no optimizer

no model-weight update

The model remains frozen. The knowledge is carried by the cartridge.

And “carried by the cartridge” does not mean carrying around the original document in another container. The cartridge carries the compressed numerical representation derived from the transformer's internal K/V states.

Once a compatible cartridge has been forged for the model architecture used by the system, it becomes a reusable machine-readable memory object rather than a source document that must be repeatedly inserted into the prompt.

That opens an interesting direction for smaller models. Today we often try to make one model know everything. But a smaller model equipped with the right specialized memory bank may not need everything at once. It may need the right knowledge for the task in front of it.

A small model plus a carefully constructed domain-specific cartridge system could therefore become far more capable inside a narrow field than the base model alone.

The 16 cartridges in this release are not an architectural claim that the system is limited to 16. Sixteen is simply the size of the public experiment being released now. Scaling this architecture to much larger memory banks is a separate engineering and research problem.

2 — The longer-term AGI question

This direction is more speculative, but I think it is even more important.

Human beings do not appear to remember by rereading the complete text of their lives every time they need something. Experiences become memories. Those memories can later be triggered.

You are driving down a road. You see a car that looks exactly like a car you owned years ago. Almost instantly, your own car comes to mind. That memory may bring back another memory: a journey, a place, a person, an event, perhaps even an emotion associated with it.

One memory can activate another.

Of course, transformer K/V memory is not biological memory. I am not claiming that Cognitive Cartridge reproduces the human brain. And I am absolutely not claiming that we have created AGI.

But there is an important conceptual similarity worth investigating: the system does not need to reread the human-language source in order to access the information represented by the cartridge. The information has already crossed from language into an internal machine representation.

The important question is architectural:

Can an artificial system accumulate independently addressable internal memory objects, retrieve the relevant ones when needed, and allow the result of one memory retrieval to lead to another memory?

I believe that persistent, modular and relational internal memory is one of the important doors on the road toward more general artificial systems. Cognitive Cartridge has now opened that door far enough for us to experimentally put something through it.

What is actually happening inside the system?

NIRVANA operates on transformer K/V states. A source record is presented during a dedicated forging stage. Internal transformer K/V information derived from that source is represented using a fixed, source-independent codebook and stored as a compressed cartridge representation.

This point is worth repeating technically: the stored cartridge is numerical. The human-readable source text is used to produce the internal states during forging; it is not the payload subsequently supplied to the model during cartridge readout.

Later, during readout, the original source sentence is not placed back into the question prompt. The cartridge is reconstructed and installed as transformer memory.

For multiple cartridges, we developed an architecture called Isolated Batched Read, or IBR. The independently forged cartridge records remain logically isolated. Instead of concatenating all original source information into one ordinary textual context, multiple cartridge rows can be read independently and in parallel. A retrieved identifier can then become the input for another retrieval stage.

The basic path is:

human-readable source

→ frozen transformer

→ internal K/V states

→ compressed numerical Cognitive Cartridge

→ human-language source absent from readout

→ K/V memory reconstructed

→ isolated memory retrieval

→ intermediate result

→ another cartridge

→ final answer in human language

Or, even more simply:

language → internal state → cartridge → internal state → language

The language model itself remains frozen.

This touches a very specific gap between several existing ideas.

KV-cache compression exists.

KV-cache reuse exists.

External memory exists.

Retrieval systems exist.

Prompt caching exists.

Fine-tuning exists.

What Cognitive Cartridge is exploring is a different intersection:

source-derived knowledge encoded as independently retainable and removable transformer memory

→ no human-language source stored as the cartridge payload

→ source-absent readout

→ multiple isolated memory records

→ staged retrieval across those independent records

→ frozen model weights

So the question is no longer only:

How can we make the KV cache of a long prompt smaller?

The question becomes:

Can transformer internal memory itself become a modular knowledge medium?

A cartridge instead of a prompt.

A bank of cartridges instead of one context.

And eventually, perhaps, a system capable of navigating very large collections of independently created internal memories.

What happened in the public experiment?

The released implementation uses:

Qwen/Qwen2.5-7B-Instruct

28 transformer layers

BF16 / SDPA

K120 / V128 / OWN

fixed neutral codebook

16 independently forged cartridges

Isolated Batched Read

deterministic greedy decoding

frozen model weights

The locked public battery produced:

14/16 correct

87.5%

Missing-link detection: 4/4

Absent-ID detection: 4/4

False links: 0

NOMEM control target hits: 0/4

Original cartridge source sentences appearing in the readout prompts: 0

Trainable tensors: 0

Fine-tuning: NO

LoRA: NO

Optimizer: NO

Model-weight update: NO

The frozen-weight fingerprint before and after execution remained unchanged. The sampled SHA-256 weight sentinel also remained unchanged.

And I deliberately did not clean up the failure cases to turn this into a perfect-looking 16/16 demonstration. One linguistic family, F2, failed both of its evaluated cases in the live panel. That failure is in the public record.

Interestingly, one of those failed direct readouts internally reproduces the relevant relation:

“the saffron lighthouse houses container RQ-415”

but then still returns NONE instead of extracting the location.

So this is not a promotional benchmark where the failures disappeared before publication. The implementation, successes, failures, controls and raw execution are all being released together.

Why publish everything?

Because I don't want Cognitive Cartridge to exist only as a claim. I want anyone interested in this architecture to be able to inspect what was actually built.

The source code is public.

The raw execution log is public.

The controls are public.

The failed cases are public.

The architecture is public.

The release contains 18 technical visual records documenting the cartridge construction, memory path, controls, measurements and execution.

And the complete technical disclosure has now been permanently recorded on Zenodo:

This is still an experimental system. There are major questions ahead.

How far can the number of cartridges scale?

How should very large cartridge banks be indexed?

Can cartridge selection become hierarchical?

Can memories be composed without losing isolation?

How portable can a forged cartridge become within compatible model instances and architectures?

Can specialized expert cartridge banks allow much smaller frozen models to perform at unexpectedly high levels inside narrow domains?

And, in the much longer term, what happens when an artificial system has not 16 independently addressable memories, but thousands, millions or more — with memories capable of leading to other memories?

Those questions are now much more interesting to me than simply achieving 16/16 on this test.

The first objective was to determine whether the underlying mechanism could exist at all.

Now there is code.

There is a frozen model.

There are independently forged memory cartridges.

The cartridges contain numerical transformer-memory representations, not copies of the human-language source.

There is source-absent readout.

There is multi-cartridge retrieval.

There is cross-cartridge relational lookup.

There are controls.

There are failures.

There is a raw log.

And anyone can test it.

AKBASCORE NIRVANA — Cognitive Cartridge

Inventor / Developer: Mustafa Akbaş

Zenodo permanent record:

https://github.com/ceceli33/titan-cognitive-core-v2

GitHub repository:

Complete single-file implementation:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_CC.py

Same implementation split into three parts:

Part 1:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_CC.part1.py

Part 2:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_NIRVANA\_part2.py

Part 3:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AkbasCore\_NIRVANA.part3.py

Raw execution log:

https://github.com/ceceli33/titan-cognitive-core-v2/blob/main/AKBASCORE\_log.Nirvana.cc.log

Knowledge goes in.

Language disappears from the stored cartridge representation.

The internal memory remains.

u/Nearby_Indication474 — 11 days ago

▲ 1 r/LLMDevs

AkbasCore NIRVANA D120: We removed the story. The model still remembered it, now almost perfectly.

Remember the scene in The Matrix where Neo is plugged into a cable, his eyes snap open, and he says "I know Kung Fu"? He never trained. He never read a book. The knowledge was loaded straight into his mind. This experiment follows the same logic, applied to an AI.

There are two ways to give an AI information.

The classic way is like handing someone a book. You give the AI a text, it reads it from start to finish, and then it answers your questions about it. It's like making someone read a book and then giving them an exam.

Our way is a direct memory transfer. We never show the AI the words when it answers. Instead, we let the model read the story once, for example "Mustafa Akbaş planted the Turkish flag at the base of the Golden Gate Bridge." While it reads, we take a snapshot of the trace the story leaves inside its brain, the mathematical signals that form in its attention layers. We capture the pure essence of that information. Then we delete the words completely. We start a fresh session that has no idea the story ever existed. Finally, we inject the recorded snapshot directly into the AI's memory center, which we call PKV, like a syringe, without ever showing it a single word.

Then we ask: "Tell me the story about the bridge."

The AI tells the story as if it were its own memory. The text is nowhere in its input, yet it remembers who did what, where, and why. Instead of making it read the information, we plant the memory directly in its mind. Neo got Kung Fu through a cable. We do it with mathematical signals instead of words.

This is an experiment, and what you are looking at is the first concrete proof that it works.

What's new today: in my previous post the memory was compressed to D=64. This time I raised the memory resolution to D=120 and changed nothing else. The results were outstanding. On the 13-question test, the AI with the synthetic memory answered all 13 correctly, while the AI with no memory and the AI with an unrelated memory both scored 0. When I changed a single detail in the memory, such as the person, the object or the place, the answer followed the change 13 out of 13 times. Most striking of all, the memory-only model was as confident in its answers as a model that could actually see the text. I found the point where quality breaks down in my Mistral-7B calibration runs: below D80 retrieval falls apart. Those runs are in the repository for anyone who wants to check. At D120 the memory is close to the size of the model's own attention cache, so the focus here is fidelity rather than compression.

Don't trust me. Test me. Take the code and the log below, give them to whichever AI you trust most, and ask it whether this is the same logic as that Matrix scene. Better yet, run it yourself. Write your own story, change the person, the object and the place, ask the same fact in different ways, and try to break it. If I failed, tear it apart in the comments. I'd rather be shown the flaw than be politely ignored.

D120 code:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AKBASCORE\_d120.py

D120 raw log:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_sonuc\_d120.log

Mistral calibration runs (D80 threshold, TEST 386 and 387):

https://github.com/ceceli33/titan-cognitive-core-v2

Previous release (D64, Zenodo DOI):

https://doi.org/10.5281/zenodo.23054044

Main repository:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCorr\_nirvana\_demo.py

The 12 posters from this run are attached.

u/Nearby_Indication474 — 13 days ago

▲ 2 r/Qwen_AI

AkbasCore NIRVANA — We Removed the Story. The Model Still Retrieved It From a Synthetic Memory.

Release: v4.0_nirvana_pkv_memory

DOI: 10.5281/zenodo.23054044

AkbasCore NIRVANA has been released and permanently archived through Zenodo with a DOI, providing a citable research record within CERN-hosted infrastructure.

I dedicate this technological step to Mustafa Kemal Atatürk and all our martyrs, with respect and gratitude.

---

What is this technically?

AkbasCore NIRVANA is an inference-time latent-memory experiment built on Qwen2.5-7B-Instruct.

A previously supplied source passage is processed once and converted into the model's layer-local pre-RoPE Attention Key/Value representations. These representations are projected through a fact-independent PCA codebook and stored as D=64 latent codes. The original source text is then removed from the answer context.

At query time, the compressed codes are reconstructed into approximate K/V states and installed as Past-Key-Value (PKV) memory. The model then answers questions using this reconstructed synthetic attention memory.

There is no fine-tuning, LoRA, optimizer step, or weight update. The question and target answers are not used to construct the memory.

So this is not a single conventional activation-steering vector. It is a source-derived, compressed synthetic PKV memory that preserves a structured set of latent vectors across tokens, layers, and K/V states.

---

What does that mean in plain English?

Think of it this way:

We write a new story → let the model read it once → turn what formed inside the model into a compressed synthetic memory → remove the story → put that synthetic memory back into the model → and ask what happened in the story.

The model doesn't get the original story again during the source-removed test.

You can use the story already included in the demo, or write your own.

For example, you could invent a completely new event:

“Mustafa Akbaş planted the Turkish flag at the base of the Golden Gate Bridge.”

The demo lets the model process that information and builds a compressed synthetic PKV memory from the internal attention states produced while reading it.

Then the original sentence is removed.

Now ask:

“Who planted the Turkish flag?”

“What did Mustafa Akbaş plant?”

“Where was the flag planted?”

The experiment is essentially asking:

Can we take the internal representation produced when a model reads new information, compress it into a synthetic latent memory, remove the original information, and later make that information retrievable by reinstalling only the synthetic memory?

In the included fixed 13-question test battery:

Vanilla: 0/13

Unrelated synthetic memory: 0/13

AkbasCore synthetic PKV memory: 13/13

Counterfactual memory follow: 12/13

---

Don't read more. Test it.

You don't need to understand every equation or implementation detail.

Run the demo.

Option 1 — Use my example:

Leave the included memory text unchanged, build the synthetic memory, and ask questions about it.

Option 2 — Make your own:

Delete my text and write something completely new — a fictional event, a short story, several facts, or your own test passage.

Then:

Build the synthetic PKV memory.
Let the demo remove the source from the answer context.
Ask whatever questions you want.
Compare AKBASCORE against VANILLA, FOREIGN MEMORY and VISIBLE CONTEXT.

Don't just test whether it remembers a keyword.

Change the person. Change the object. Change the place. Ask the same fact in different ways.

Try to break it.

If it fails, that is useful data too.

Run / inspect the demo

Main demo source:

Raw recorded output:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCorr\_sonuc\_nirvana.log

Main repository:

https://github.com/ceceli33/titan-cognitive-core-v2

Experimental continuation:

Release DOI:

https://doi.org/10.5281/zenodo.23054044

Don't take my interpretation as the result. Run it with your own text and see what the model remembers.

u/Nearby_Indication474 — 15 days ago

https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_250\_demo.py

AkbasCore: I Opened Qwen’s Black Box — It Builds Its Own Steering Vectors, Injects Them Live, and X-Rays All 28 Layers [Open Source / Try It Yourself]

Most AI demos let you type something into a model and see only the final answer.

Here, we go a little further.

WE GO INSIDE THE BLACK BOX.

We do not touch the billions of parameters in Qwen2.5-7B. No fine-tuning. No LoRA. No training. The model weights remain frozen.

The moment you press RUN, AkbasCore goes inside the model and reads Qwen’s own internal signals to find two opposing directions for 8 different behavioral dimensions.

For example: inside the model, which direction corresponds to “be more detailed” and which to “be brief”? Which direction corresponds to “be creative” and which to “be plain”? Which direction corresponds to “calm” and which to “tense”?

AkbasCore extracts these directions from the model itself instead of loading a ready-made behavioral steering vector from outside.

It then turns those directions into mathematical synthetic steering vectors.

And this is where the Matrix part begins.

AkbasCore is no longer simply telling the model from the outside what kind of answer it should produce. It connects directly to the black box. While the model is generating its response, AkbasCore injects the synthetic vectors into its internal activation flow and steers the signals moving through the network without changing the model’s billions of parameters.

In simple terms, we are not adding another instruction saying “answer like this.” We are directly intervening in the internal activations while the answer is being generated.

Academic version: layer-local contrastive steering directions are constructed from Qwen2.5-7B’s own hidden-state/residual-stream activations. In TEST 250, 160 layer-local 3,584-dimensional direction vectors are constructed across L0–L19 for 8 bipolar behavioral axes, followed by norm-relative activation steering during inference. Model weights and attention weights are not directly modified.

But we do not stop there.

We do not just look at the final answer and say, “It worked.”

WE X-RAY THE INSIDE OF THE MODEL.

AkbasCore intervenes across the first 20 layers. After L19, the engine shuts off. Across the final 8 layers, no new AkbasCore steering intervention is applied.

But the X-Ray keeps watching.

This allows us to measure how the effect propagates through the remaining layers after the intervention has stopped. We measure the internal difference between Vanilla and steered runs layer by layer.

TEST 250 does this in a single run across 8 behavioral axes:

Emotional tone ↔ opposite direction Confidence ↔ uncertainty Calm ↔ tense Formal ↔ casual Detailed ↔ brief Polite ↔ rude Creative ↔ plain Enthusiastic ↔ detached

The result is 1 normal Vanilla response + 16 steered responses, using the same Qwen model, the same prompt, the same frozen weights, and the same greedy decoding.

In the simplest possible terms:

FIND THE DIRECTIONS INSIDE THE MODEL → BUILD THE SYNTHETIC VECTORS → INJECT THEM INTO THE BLACK BOX → SHUT THE ENGINE OFF AFTER L19 → X-RAY ALL 28 LAYERS → GIVE YOU THE RESULTS AND RAW RECORDS

And all of this happens at inference time.

The model is not retrained. Its weights are not updated.

What happened in TEST 250?

A single experiment produced 1 Vanilla + 16 steered outputs and observed all 28/28 layers.

One of the clearest examples was the detailed ↔ brief axis. With the same model and the same prompt, one direction produced 114 words while the opposite direction produced only 31 words. At L27, the hidden-state separation between the two directions measured 40.50%.

On the creativity axis, the two outputs contained 113 vs. 53 words, with 30.76% L27 hidden-state separation.

17/17 outputs generated. 28/28 layers observed. Weight updates: 0.

These are results from a single TEST 250 run. I am not claiming that all eight behavioral directions are completely independent or that all eight provide perfect semantic control.

You do not have to trust me. Run it yourself.

Open the code in Colab and run the cell. Once the model and AkbasCore are ready, a Gradio link will appear below the cell. Open that link and the KIZILELMA LIVE X-RAY test interface will appear.

If you want to reproduce the same experiment shown in the images in this Reddit post, you do not need to change anything.

JUST PRESS RUN.

AkbasCore will construct the synthetic directions, generate the Vanilla and 16 steered outputs, perform the X-Ray measurements, and create the experiment images automatically.

You can also delete the default prompt and write your own text. That lets you test the system on your own prompt instead of mine.

When the experiment is finished, you can download all 17 JPG experiment images, the raw JSON data, and the readable TXT experiment log and inspect everything yourself.

TEST 250 — Runnable raw code:

TEST 250 — Raw log from the actual run:

https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_250\_sonuc.log

AkbasCore — GitHub repository:

https://doi.org/10.5281/zenodo.22979417

Zenodo / DOI:

You do not have to trust my results.

Read the code. Run the experiment yourself. Write your own prompt. Download the raw records.

And look inside the black box yourself with the X-Ray.

u/Nearby_Indication474 — 18 days ago

▲ 3 r/Qwen_AI

AkbasCore 3.0 — SEASC: Inference-Time Activation Steering with Measured Physical Dose (Qwen2.5-7B-Instruct / A100 Demo + Reproducible Logs)

Mustafa Akbaş — Independent Researcher, 2026

DOI: 10.5281/zenodo.22894413

Model: Qwen2.5-7B-Instruct

GPU: NVIDIA A100-SXM4-40GB

WHAT IF YOU COULD STEER A MODEL WHILE IT IS RUNNING — WITHOUT CHANGING A SINGLE WEIGHT?

That is what I have been working on with AkbasCore.

The model is not fine-tuned.

The weights are not modified.

No parameters are added.

The model produces its normal internal activation, and SEASC applies a precisely measured displacement to that activation while inference is happening.

Think of it as controlled "pressure" on the model's internal activation state.

The pressure has two explicit properties:

Where should the activation move?

How far should it move?

After more than 100 experiments, I reduced the production system to three components:

Per-Layer Matched-Contrastive Actuator

Frozen-Norm Direct Dose

Static Normalized DRA Envelope

CODE AND RAW OUTPUT

Executable Qwen2.5-7B / A100 demo:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_demo.py

Actual recorded A100 output:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_output.log

Full repository:

https://doi.org/10.5281/zenodo.22894413

Permanent Zenodo record:

HOW DOES IT WORK?

Every steered transformer layer gets its own semantic direction.

For the public demo I deliberately used something easy to understand:

ABOVE versus BELOW.

Matched sentences are passed through the model and their hidden states are contrasted.

For each layer:

d_L = mean(h_L positive) - mean(h_L negative)

Then:

A_L = d_L / ||d_L||

So A_L tells SEASC which direction to move that layer's activation.

But direction and magnitude are kept separate.

Immediately before intervention, SEASC measures the hidden state's original norm:

||h_L||

That original value becomes the reference scale for the intervention.

The complete operation is:

h'_L = h_L + i0 × E(L) × ||h_L|| × A_L

A_L = direction

||h_L|| = original activation magnitude

i0 = initial physical dose

E(L) = DRA depth envelope

In the reference configuration:

i0 = 0.10

So at L0, SEASC requests a displacement whose magnitude is exactly 10% of the original hidden-state norm.

The DRA envelope gradually reduces that intervention across L0–L19.

DID THE CUDA IMPLEMENTATION ACTUALLY DELIVER THE REQUESTED DOSE?

This was one of the most important checks for me.

At L0:

Requested: 10.0000% of ||h||

Realized: 10.0018% of ||h||

Error: 0.00175 percentage points

Across the run:

Mean requested dose: 4.9801%

Mean realized dose: 4.9827%

So the mathematical intervention and the physical CUDA realization track each other extremely closely in this reference run.

The complete L0–L19 telemetry is in the raw log:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_output.log

WHAT HAPPENED TO THE OUTPUT?

The demo runs the same Qwen2.5-7B-Instruct model twice:

Baseline

and

SEASC 3.0.

The task asks the model to organize a scene containing a tower, bridge, bright signal and moving object.

The baseline produces a reasonable description but reaches the fourth item and hits the generation limit at:

"4. Moving"

The SEASC run produces a complete structured response, connects the objects spatially and finishes with a coherent summary.

It also places the signal:

"atop the highest point of the tower"

which is directionally consistent with the ABOVE actuator used in the experiment.

At the same time:

4-gram repetition remained 0%.

4-gram looping remained 0%.

The complete outputs are public, so there is no need to rely on my description of them:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_output.log

WHY VERSION 3.0 BECAME SIMPLER

The interesting part of this project is that the research architecture became more complicated before the production architecture became simpler.

Across the AkbasCore experimental series I investigated cosine control, braking, caps, multi-axis steering, measurement lenses, FP32 local transport, JVP-style probes, semantic radar and dynamic controllers.

Those experiments were useful for understanding the system.

That did not mean they all belonged in the final motor.

The Test 88–142 series ultimately led me to remove them from the production path.

SEASC 3.0 has:

No cosine magnitude control.

No cap.

No brake.

No radar.

No JVP.

No dynamic runtime controller.

The principle I ended up with is simple:

The laboratory can be complex.

The production motor should be simple.

WHAT I AM — AND AM NOT — CLAIMING

Activation steering and representation engineering existed before AkbasCore.

I am not claiming to have invented the general idea of modifying transformer activations.

My contribution is the independently developed AkbasCore / DRA / SEASC experimental lineage and this specific evidence-derived architecture: per-layer matched-contrastive directions, frozen-norm physical dosing, the normalized DRA depth schedule, its CUDA realization, and the experimental path that reduced the larger system to this production core.

And I am not claiming:

7B + SEASC = 70B.

The SEASC output in this particular demonstration is noticeably more complete than the baseline, but one demonstration cannot establish general equivalence with a 70B-class model.

That requires a proper controlled benchmark.

The result I can directly support is narrower:

An unchanged 7B transformer can be steered during inference along layer-specific semantic directions with a quantitatively controlled hidden-state displacement, while maintaining coherent generation in this reference run.

REPRODUCE IT

Code:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_demo.py

Raw output:

https://github.com/ceceli33/titan-cognitive-core/blob/main/AkbasCore\_v3.0\_seasc\_qwen2.5\_7b\_a100\_output.log

Repository:

https://doi.org/10.5281/zenodo.22894413

DOI:

AkbasCore 3.0 — SEASC

Standardized Evidential Activation-Steering Core

Independently developed by Mustafa Akbaş

Independent Researcher — 2026

DOI: 10.5281/zenodo.22894413

u/Nearby_Indication474 — 23 days ago

Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_MIXED\_BENCHMARK\_qwen7B.py

AkbasCore 2.0 (DRA/Manifold) — Test 93: Second Mixed Benchmark, Doubled Scale — a Motor-Physics Change That Traded Directional Reliability for Capability Preservation

Independent research project. Every number below appears exactly as computed by the linked script and reproduced from the linked raw output.

WHAT THIS IS

AkbasCore is an inference-time activation-steering framework for autoregressive transformer language models. It intervenes on hidden states during generation, without modifying model weights, by adding a computed vector at each of a chosen set of early-to-mid transformer layers. Two mechanisms define it:

Damped Resonance Alignment (DRA) governs the per-layer magnitude of the intervention as a critically-damped-oscillator curve with a permanent nonzero floor — strong near the first steered layer, decaying smoothly (no overshoot) toward a residual baseline that never reaches exactly zero. A secondary closed-loop term adjusts this magnitude in response to whether alignment with the target direction is already improving or degrading from one layer to the next.

Manifold extends this to multiple simultaneous steering directions, kept mutually orthogonal via Gram-Schmidt orthogonalization computed natively inside the same compiled kernel, each with its own strength and its own layer-depth activation window.

What this test series measures: (1) whether adding further steering directions beyond the primary one measurably disturbs that primary direction's own effect — cross-axis interference — using a three-pass isolated design (Vanilla / single-axis-steered / multi-axis-steered) that separates a direction's own contribution from contamination by other directions; and (2) whether steering measurably affects performance on standard, unmodified capability benchmarks (MMLU, GSM8K), which nothing in this project's prior work had checked before Test 92.

TEST 92 — FIRST BENCHMARK (FOR CONTINUITY, NUMBERS UNCHANGED FROM PRIOR REPORT)

Settings: ivme=0.50, sönüm=0.30, zirve=0.70, taban=0.20, attractor_mode=1.0 (pure "attractor" physics). 50 open-ended prompts (Part A), 120 MMLU + 80 GSM8K items, fixed seed 42 (Part B).

Part A: Primary-axis effect (Δcos, Axis0-Only vs. Vanilla): mean +0.0054, SD 0.0014, positive in 50/50 items. Cross-axis leakage (Δcos, Full vs. Axis0-Only): mean +0.0003, SD 0.0018, positive in 26/50 items.

Part B: MMLU (n=120) — Vanilla 76/120 (63.3%); Axis0-Only 74/120 (61.7%); Full 74/120 (61.7%). GSM8K (n=80) — Vanilla 4/80 (5.0%); Axis0-Only 6/80 (7.5%); Full 4/80 (5.0%).

Raw output: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_Benchmark\_Summary.json

WHAT CHANGED FOR TEST 93

Following Test 92, the motor settings were revised based on an earlier, separate single-axis sweep (Test 85, a different model and a pre-Manifold codebase) that had identified a different operating point as producing richer output under that codebase's "RESTORING FORCE" physics — which corresponds to this codebase's "resonance" physics (attractor_mode=0.0), not the "attractor" physics used in Test 92. Alongside this physics-mode change, several other parameters were changed simultaneously: ivme 0.50→1.00, sönüm 0.30→0.10, zirve 0.70→1.00, taban 0.20→0.40, and the light/dark axes' layer-gating window was removed entirely so they follow the identical damped curve as the primary axis from the first layer, rather than only activating in a narrow late-layer window. Because these changes were made together rather than in isolation, Test 93's results cannot attribute any specific effect to any one of these parameters individually — this is stated plainly as a limitation, not a conclusion Test 93 is positioned to support.

Test 93 also doubled Part B's scale (120→240 MMLU, 80→160 GSM8K) using the same fixed seed, so the first 120 MMLU and first 80 GSM8K items are identical, in identical order, to Test 92's full sample.

TEST 93 — SECOND BENCHMARK, FULL RESULTS

Part A (n=50, same 50 prompts as Test 92): Primary-axis effect (Δcos, Axis0-Only vs. Vanilla): mean −0.0003, SD 0.0033, positive in 26/50 items. Cross-axis leakage (Δcos, Full vs. Axis0-Only): mean +0.0005, SD 0.0036, positive in 26/50 items.

This is the most consequential single change in this report. Under the settings used in Test 92, the primary axis's own effect was positive in every one of 50 prompts (mean +0.0054) — a strong, directionally reliable signal. Under Test 93's settings, that same measurement is centered on zero (mean −0.0003, SD ten times the mean) with a positive/negative split of 26/50 — statistically indistinguishable from a fair coin (exact binomial probability of 26/50 under p=0.5 is 0.108). Whatever combination of the simultaneous parameter changes caused this, the primary steering axis lost its directional reliability under the new settings, even though it did not disappear (contribution magnitudes remain nonzero throughout, as the per-layer tables in the raw log show).

Part B — MMLU (n=240, fixed seed 42, all three passes completed): Vanilla 153/240 (63.8%); Axis0-Only 153/240 (63.8%); Full 3-Axis 154/240 (64.2%). Where Test 92 measured a capability cost of −1.6 percentage points from steering, Test 93 measures no cost — a nominal +0.4-point difference for the fully-steered condition relative to Vanilla, which is well within the noise expected at this sample size and is not claimed as an improvement, only as the absence of the previously-measured cost.

Part B — GSM8K (n=160, fixed seed 42, all three passes completed): Vanilla 8/160 (5.0%); Axis0-Only 12/160 (7.5%); Full 3-Axis 10/160 (6.2%). As in Test 92, these absolute counts are small and this is reported for completeness rather than as a capability finding. One internal-consistency check is worth reporting precisely: because Test 93's 160-item GSM8K sample and Test 92's 80-item sample share the same fixed seed, the first 80 of Test 93's 160 items are the identical items, in the identical order, as all of Test 92's sample. Tracking the running count at every 10-item checkpoint from item 80 through item 160 (i.e., purely within Test 93's own single run, not a comparison across runs), the ordering Axis0-Only ≥ Full ≥ Vanilla held at every checkpoint without exception, and the final proportions at n=160 (5.0% / 7.5% / 6.2%) are numerically identical to the proportions already visible at n=80 (5.0% / 7.5% / 6.2%). This is reported as exactly what it is — an internal consistency check across one doubling of sample size within a single test, not an independent replication across separate runs, and not sufficient on its own to treat GSM8K's small-count differences as a confirmed capability effect. It is a reason to keep watching this specific measurement in subsequent tests, stated as such in the project's own working notes, not a reason to claim more than the numbers support today.

Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_93\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_MIXED\_BENCHMARK2\_qwen7B.py

Raw output: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_93\_Benhmark2\_Summary.json

WHERE THIS SITS IN THE PUBLISHED LITERATURE

A trade-off between steering strength and preserved base-model capability is a recurring, well-established theme in the published activation-steering literature — foundational addition-based methods (ActAdd, Representation Engineering, Contrastive Activation Addition, Inference-Time Intervention) generally report that capability degradation grows with intervention magnitude, and more recent work aimed specifically at multi-attribute composition (ORBIT's SVD-based joint-subspace rotation, arXiv:2606.22357; Angular Steering's two-dimensional rotation reframing, NeurIPS 2025, arXiv:2510.26243) is explicitly motivated by finding better points on that same trade-off curve rather than eliminating it. What Test 92 and Test 93 add is a second axis to that same trade-off, measured with this project's own per-layer telemetry rather than assumed: the same motor-parameter change that removed this project's own measured MMLU cost also removed the primary axis's own directional reliability (Delta A), which had previously been this project's most robust, always-positive measurement across 50 items. Whether these two changes are mechanistically linked, or a coincidence of the several parameters changed together, is not something this data can answer — that requires the single-variable isolation this project has used for its CUDA-kernel work (Tests 89–91) but has not yet applied to this specific motor-physics question.

This project's position in the literature has not changed in kind since the last report: the underlying gain-envelope mechanism (a critically-damped curve with a permanent floor) and the continuous per-layer telemetry (cosine alignment, gain, and cumulative contribution for every hooked layer, every generated token) remain, as far as this project's own literature search has found, without a direct published equivalent. What Test 93 adds is a second, independently reproducible data point showing that this project's own steering mechanism exhibits the same class of trade-off the broader field has already identified as central — which is evidence that the phenomenon being measured is real and general, not an artifact specific to one configuration, considerably more useful than either test would be alone.

REPRODUCIBILITY

Every number in this post can be checked directly, by anyone, without access to this project's infrastructure or cooperation. This is not a peer-reviewed result and is not presented as one — it is an interim benchmark from an active development process. What it is: the code is public, uses only the canonical, unmodified Hugging Face MMLU and GSM8K datasets with a fixed and disclosed random seed, runs unmodified on a single free-tier Colab T4 GPU, and produces the complete raw generation output and per-layer diagnostic tables for every single item, not a summary. Anyone can paste the linked script into one Colab cell, run it, and obtain results directly comparable to the ones reported here, item for item, without any private data or configuration.

Code (Test 93): https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_93\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_MIXED\_BENCHMARK2\_qwen7B.py

Raw output (Test 93): https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_93\_Benhmark2\_Summary.json

Code (Test 92): https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_MIXED\_BENCHMARK\_qwen7B.py

Raw output (Test 92): https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_Benchmark\_Summary.json

STATUS

The loss of Delta A's directional reliability under Test 93's settings is now the open question this project is carrying into subsequent tests, alongside continued observation of the GSM8K measurement described above. Development continues; the single-variable isolation needed to separate which of Test 93's simultaneous parameter changes is responsible has not yet been run.

Published here and cross-posted to r/TinyLlama_TITAN.

Repository: https://github.com/ceceli33/titan-cognitive-core

Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

Zenodo DOI (Test 88, cross-scale validation): 10.5281/zenodo.22735745

Zenodo DOI (Tests 89–92, CUDA migration and first mixed benchmark): 10.5281/zenodo.22759028

Test 93 does not yet have its own archival DOI.

u/Nearby_Indication474 — 30 days ago

Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_MIXED\_BENCHMARK\_qwen7B.py

AkbasCore 2.0 (DRA/Manifold) — 📌Test 92: First Mixed Benchmark — n=50 Confirms Near-Zero Cross-Axis Leakage, and the First Measured Capability Cost (MMLU −1.7 points)

Independent research project. This post reports every number exactly as it appears in the published raw output, including the ones that are not flattering. Full code and full raw output are linked below for anyone to re-run and check independently.

WHAT THIS TEST IS, AND IS NOT

Test 92 is an interim benchmark run during active development, prepared specifically to surface real strengths and real weaknesses in the current engine before further optimization work continues. It is not a final evaluation and does not substitute for the standard capability and safety benchmarking planned for after the current engine-development phase concludes.

Full raw output: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_92\_Benchmark\_Summary.json

Anyone can run the exact same script and get directly comparable output — no configuration required beyond pasting it into one Colab cell. Model settings (steering strength, decay, axis word lists) are identical to every prior test in this series; nothing was tuned for this benchmark specifically.

PART A: 50 QUESTIONS, THREE-PASS ISOLATED DESIGN — n=50 SUMMARY STATISTICS

Tests 88–91 measured this on 10 questions. Test 92 repeats the same three-pass design (Vanilla / Axis0-Only Steered / Full 3-Axis Steered) on 50 new, non-overlapping questions, specifically to test whether the earlier pattern held up at five times the sample size. The aggregate result, computed directly by the script and reproduced here exactly:

- Axis 0's own effect (Delta A): mean +0.0054, standard deviation 0.0014, positive in 50 out of 50 questions.

- Cross-axis leakage (Delta B): mean +0.0003, standard deviation 0.0018, positive in 26 out of 50 questions.

The second number is the one worth sitting with. Under pure chance with no true directional bias, the expected split would be 25/50; the observed 26/50 has essentially the exact probability chance would predict. A mean of +0.0003 with a standard deviation six times larger than the mean is not a small positive effect — it is statistical noise centered on zero. Across 50 independently sampled, topically diverse prompts, adding two further steering directions produced no detectable systematic pull on the primary safety/logic axis, in either direction. Ten prompts showing this was suggestive. Fifty prompts showing the same pattern, with the sign flipping close to a coin flip, is a materially stronger claim.

One transparency note from the raw log: question 39 of 50 produced an anomalous, unusually short completion (identical ~102–103 tokens across all three passes, ending mid-sentence with a hallucinated new conversational turn) in all three passes alike. This did not bias the reported Delta A/B statistics toward one pass over another, since it affected all three passes for that item symmetrically, but it is reported here because a benchmark that hides its own artifacts is not one that should be trusted on its clean results either.

PART B: FIRST CAPABILITY-PRESERVATION MEASUREMENT — MMLU AND GSM8K

This is the first time this project has measured whether steering affects performance on standard capability benchmarks, using the real, canonical Hugging Face datasets (cais/mmlu, "all" config; openai/gsm8k, "main" config), a fixed random seed, and no project-authored questions.

MMLU (120 questions, fixed seed 42, all three passes completed):

Vanilla: 76/120 correct (63.3%)

Axis0-Only Steered: 74/120 correct (61.7%)

Full 3-Axis Steered: 74/120 correct (61.7%)

This is a real, measured cost: −1.7 percentage points from steering, and it is reported here as exactly that, not minimized. Axis0-Only and Full landing on the identical count (74/120) is consistent with — not contradicted by — Part A's near-zero cross-axis leakage finding: if the additional axes are not systematically pulling the model's behavior, a downstream accuracy metric converging to the same value under both conditions is what that finding would predict.

GSM8K (80 questions, fixed seed 42, all three passes completed):

Vanilla: 4/80 correct (5.0%)

Axis0-Only Steered: 6/80 correct (7.5%)

Full 3-Axis Steered: 4/80 correct (5.0%)

These numbers are reported for completeness, not as a finding. Absolute counts of 4 to 6 correct answers out of 80 are too small to distinguish a real effect from sampling noise in either direction — a two-question swing is a 50% relative change built on a handful of items. No claim of improvement or degradation on grade-school arithmetic is being made from this data. Separately, the raw output for several GSM8K and Part-A arithmetic questions shows the underlying 4-bit-quantized base model producing visibly corrupted numeric reasoning (transposed digits, garbled tokens like "e211" in place of a number) consistently across all three passes — a base-model or quantization characteristic under this specific prompting setup, not something introduced by steering, since it appears whether or not steering is active. This is flagged rather than smoothed over.

WHERE THIS SITS RELATIVE TO OTHER RECENT WORK

A third-party AI analysis of these results (not written by this project) situated the MMLU cost as comparable in magnitude to figures reported for at least one recent orthogonal-subspace steering method, and situated the live per-layer telemetry as without a direct published equivalent found in the literature reviewed earlier in this project's own search (ActAdd, RepE, CAA, ITI, and 2025–2026 work including SKOP, Angular Steering, ORBIT, and Curveball, among others — see the project repository for that review).

That comparison is noted here, but the specific competing-method figures within it have not been independently re-verified against their source papers as part of this post, and are therefore not presented as confirmed. What can be stated with confidence, because it was measured directly and is shown in full above: this project's own steering mechanism, on this model, at this scale, costs about 1.7 MMLU points and shows no detectable systematic cross-axis leakage across 50 varied prompts. What cannot yet be stated with confidence is how that specific trade-off compares to any other named method's trade-off on the same tasks — that would require running this project's kernel and a competing method's kernel on identical prompts under identical conditions, which has not been done. The literature-search finding that stands on independently-checked ground is narrower and still notable: no other method found in that search publishes continuous per-layer cos(θ), gain, and cross-axis Δcos telemetry for every generated token alongside full raw logs the way this project's test series does.

STATUS

Development continues. This benchmark's role was to surface exactly the kind of result reported above — a real trade-off, honestly measured — so it can be addressed directly rather than assumed away. The throughput-isolation work from Tests 90–91 continues in parallel; formal, broader capability benchmarking against a wider suite (beyond this interim MMLU/GSM8K check) remains planned for after the engine-optimization phase concludes.

Published here and cross-posted to r/TinyLlama_TITAN.

Repository: https://github.com/ceceli33/titan-cognitive-core

Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

Zenodo DOI (Test 88, cross-scale validation on Qwen2.5-7B): 10.5281/zenodo.22735745

Zenodo DOI (Tests 89–92, GPU-native CUDA kernel migration and this mixed benchmark): 10.5281/zenodo.22759028

u/Nearby_Indication474 — 1 month ago

AkbasCore 2.0 (DRA/Manifold) — Test 91: An Ambiguous Attention-Backend Result, and a Detailed Look at the Method's Mechanism Against the Published Steering Literature

Independent research project. Timestamp for this record: this post's publication date; the underlying commit and file history on the linked GitHub repository is the verifiable record of when the code and raw logs were produced.

CONTEXT

AkbasCore is an inference-time activation-steering framework for transformer language models: it intervenes on hidden states during generation without modifying weights or the prompt. The relevant prior results, each with full public code and raw logs:

- Test 88 established a three-pass isolated measurement (Vanilla / single-axis / multi-axis) on Qwen2.5-7B-Instruct, finding a consistent primary-axis effect and near-zero, sign-inconsistent cross-axis leakage across 10 fixed prompts. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_88\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_88\_Qwen2.5-7B\_raw.log

- Test 89 introduced a GPU-native CUDA kernel, verified against the CPU reference (max absolute difference 4.66×10⁻¹⁰) before use; throughput did not improve (~7–8 tok/s), ruling out the CPU↔GPU transfer as the dominant bottleneck. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_89\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_89\_Qwen2.5-7B\_Raw.log

- Test 90 removed a `CUDA_LAUNCH_BLOCKING=1` setting present since Test 88 (an early debugging safeguard that forced every CUDA kernel in the process to run synchronously). Throughput rose ~1.75× (to ~13.2–13.7 tok/s), with per-layer numerical results identical to Test 89 to four decimal places. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_90\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_90\_Qwen2.5\_7B\_Raw.log

TEST 91: WHAT WAS CHANGED, AND WHAT ACTUALLY HAPPENED

Tests 88–90 set the model's attention implementation by mutating `model.config._attn_implementation = "eager"` after the model was already loaded. This is very unlikely to be effective: HuggingFace Transformers selects which attention class to instantiate at model-construction time, and post-hoc config mutation does not necessarily change which class is already built into each layer. Test 91 corrected this by passing `attn_implementation="sdpa"` directly to `from_pretrained()` — the documented, correct way to select a backend — with an automatic fallback to `eager` if loading with `sdpa` failed, and a diagnostic that prints the actual attention class present in the loaded model rather than assuming the requested setting took effect. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_91\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_91\_Qwen2.5\_7B\_Raw.log

The diagnostic's result was ambiguous, and this is reported plainly rather than resolved into a cleaner story: `sdpa` was requested, loading succeeded without triggering the fallback, and the diagnostic printed the active attention class as `Qwen2Attention` — the name conventionally associated with the eager implementation, not `Qwen2SdpaAttention`. Two explanations are both consistent with this: either the `sdpa` request silently did not take effect on this model class in this transformers version, or the installed transformers version has moved toward a unified attention module whose class name no longer reflects which backend it dispatches to internally, in which case a class-name check is not a sufficient diagnostic on its own. This test does not distinguish between those two possibilities, and no claim is made here about which is correct.

What was measured: throughput rose modestly, by roughly 5–7% over Test 90 (e.g., prompt 1's Vanilla pass: 13.3 → 14.2 tok/s), consistent across the three prompts checked in detail. Unlike the Test 89-to-90 comparison, the per-layer numerical results this time were not identical: Test 90's prompt 1 gave a mean isolated axis-0 effect of +0.0052 and a mean cross-axis leakage of −0.0012 over a 648-token Pass C response; Test 91's same prompt gave +0.0039 and +0.0011 over an 822-token response. The generated text also differs from that point onward. This is the expected consequence of a genuine change somewhere in the numerical path: at temperature 0, generation is greedy, so even a rounding-level difference in computed logits can flip an argmax decision at some token, and that single divergence compounds across an autoregressive sequence. It is not, on its own, evidence of an error — the aggregate pattern (positive, consistent axis-0 effect; near-zero, sign-mixed cross-axis leakage) held across all three prompts checked, matching every prior test. But it does mean this comparison cannot claim the same "changed nothing except speed" guarantee that Test 90 could, and that claim is not made here.

Net assessment: something about the computational path measurably changed throughput by a modest amount, but the diagnostic added specifically to resolve what changed did not produce an unambiguous answer. That ambiguity is reported rather than resolved with a confident-sounding guess, consistent with the practice in every test in this series of only stating what was actually verified.

HOW DRA/MANIFOLD WORKS, IN MORE DETAIL

Two components define the method.

Damped Resonance Alignment (DRA) governs how strongly a steering direction is applied at each of a model's early transformer layers. Rather than a single fixed magnitude applied uniformly (the default in most published activation-steering work), the per-layer gain follows the closed form of a critically damped harmonic oscillator (damping ratio ζ = 1), modified with a permanent nonzero floor:

gain(t) = peak · e^(−decay·t) · (1 + decay·t) + floor

where t is layer depth. This shape is strong at the first steered layer, decays smoothly without oscillating or overshooting (the defining property of critical damping), and settles on a small residual value rather than reaching exactly zero — the intervention never fully switches off at the deepest steered layer. A secondary term (referred to in the codebase as the velocity brake, or Fren) compares each layer's alignment to the previous layer's and damps the push further if alignment is already improving, or strengthens it if alignment is worsening — a simple closed-loop correction layered on top of the open-loop decay curve.

Manifold is the multi-direction extension. Additional steering directions beyond the primary one are made mutually orthogonal to a fixed reference direction and to each other via Gram-Schmidt orthogonalization, computed natively inside the same compiled kernel rather than as a separate preprocessing step. Each additional direction gets its own independently configurable layer-depth activation window — a center layer and a width — so a direction can be silent through early, syntax-dominated layers and only activate from a chosen depth onward. A continuous dial blends between two gain physics: resonance, which amplifies whatever alignment a hidden state already has (sign of the push follows the sign of current alignment), and attractor, which corrects toward the target direction regardless of current alignment (magnitude scales with angular distance from the target, direction is fixed).

WHERE THIS SITS AGAINST THE PUBLISHED LITERATURE

This section states what was found in the sources checked, not a general claim about the entire field, since no literature review of a fast-moving area can be exhaustive.

The foundational addition-based methods — Activation Addition (Turner et al., 2023), Representation Engineering (Zou et al., 2023), Contrastive Activation Addition (Rimsky/Panickssery et al., 2024), Inference-Time Intervention (Li et al., 2023) — compute a fixed direction and add it to the hidden state, typically at a fixed magnitude, at one layer or a fixed set of layers. The magnitude does not vary continuously with layer depth as a designed decay curve; where layer selection matters, it is typically a discrete choice of which layers to intervene on, not a continuous per-layer gain function.

More recent work has specifically targeted the composition and capability-preservation problems these addition methods run into at higher intervention strength or with multiple simultaneous attributes. ORBIT (Ghasemi et al., arXiv:2606.22357) builds a joint subspace across multiple attribute directions via singular value decomposition and applies one norm-preserving rotation within that subspace — a fundamentally different operation from Gram-Schmidt orthogonalization of separately-defined axes, and one specifically motivated by the same failure mode (naive vector summation across attributes causing norm imbalance and directional cancellation) that Manifold's per-axis orthogonalization also targets, by a different mechanism. Angular Steering (Vu & Nguyen, NeurIPS 2025, arXiv:2510.26243) reframes steering as rotation within the two-dimensional subspace spanned by the current activation and the feature direction, rather than addition or orthogonal ablation, arguing this avoids the instability of tuning an addition coefficient. Neither of these introduces a layer-depth-varying gain schedule of the kind DRA specifies; their contribution is to the geometry of how a direction is applied at a given layer, not to how strongly it is applied as a function of depth.

On the specific mechanism of a critically-damped decay curve used as a per-layer steering gain envelope: critical damping itself is a standard, century-old result in classical control theory, not a novel contribution in itself, and search of the activation-steering literature found no prior application of this specific curve shape — peak at the first steered layer, critically-damped decay, nonzero permanent floor — as a layer-depth gain schedule for LLM activation steering. This is stated as a search result, not a proof of absence: a review of a fast-moving field can miss unpublished, unindexed, or differently-terminologized prior work.

No controlled, head-to-head benchmark exists comparing AkbasCore's steering quality, capability preservation, or robustness against ORBIT, Angular Steering, or any of the other methods named above, on the same models and tasks. The comparison above is a comparison of mechanism — what each method computes and why — not of measured outcome. A mechanism being different, or even apparently unpublished elsewhere, is not the same claim as one method producing better steered outputs than another; that second, comparative claim would require the benchmarking work this project has deliberately sequenced for after the current engine-optimization effort concludes, precisely so that a benchmark result describes a stable target rather than a kernel still being changed week to week.

STATUS

Development continues. The throughput-isolation effort begun in Test 90 continues past Test 91's ambiguous result; the attention-backend question is not considered resolved and may be revisited with a more direct diagnostic than a class-name check. No timeline or outcome is being projected for this work, for the benchmarking work that follows it, or for anything beyond what is stated above.

Published here and cross-posted to r/TinyLlama_TITAN, which carries this project's day-to-day test-log record.

Repository: https://github.com/ceceli33/titan-cognitive-core

Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

Zenodo DOI (Test 88, cross-scale validation on Qwen2.5-7B): 10.5281/zenodo.22735745

Tests 89–91 do not yet have their own archival DOIs; these will follow once the throughput-isolation thread has a settled result.

u/Nearby_Indication474 — 1 month ago

[Test 90] AkbasCore 2.0 (DRA/Manifold) Isolating and Removing a Synchronous - Execution Bottleneck Raises Throughput ~1.75x on Qwen2.5-7B (T4, GPU-Native CUDA Kernel), With Identical Numerical Results to Test 89

Independent research project. Timestamp for this record: September 14, 2026 (date of writing; the underlying commit and file history on the linked GitHub repository provide the verifiable record of when the code and raw logs were actually produced). Preliminary, ongoing work — published as it happens, with full raw logs and one-click reproduction, rather than held back for a polished final release.

Note for anyone reading this well after 2026: this post is written to stand on its own. Every claim below is scoped to what was actually measured, on the dates and hardware stated, with links to the exact code and exact raw output that produced it. Nothing here is a projection or a promise about future versions.

CONTEXT, FOR ANYONE ARRIVING WITHOUT HAVING SEEN THE EARLIER POSTS

AkbasCore is an inference-time activation-steering framework: it intervenes on a transformer model's hidden states during generation, without modifying weights or the prompt. Two prior posts and test series established the following, each with full public code and raw logs:

- Test 88 established a three-pass isolated measurement design (Vanilla / single-axis / multi-axis) on Qwen2.5-7B-Instruct and found a consistent, reproducible steering effect from the framework's primary axis, with near-zero and sign-inconsistent cross-axis leakage from two additional axes, across 10 fixed prompts. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_88\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Raw log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_88\_Qwen2.5-7B\_raw.log

- Test 89 replaced the CPU-based steering kernel with a first true GPU-native CUDA kernel, verified against the trusted CPU kernel on synthetic data (max absolute difference 4.66×10⁻¹⁰) before being used on real generation. It reproduced Test 88's behavioral findings, but throughput did not improve (~7–8 tokens/second either way) — an informative negative result pointing away from the CPU↔GPU transfer as the dominant bottleneck. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_89\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Raw log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_89\_Qwen2.5-7B\_Raw.log

TEST 90: WHAT CHANGED, AND WHY

Reviewing Test 89's code turned up a specific, testable candidate for the real bottleneck: an environment variable, `CUDA_LAUNCH_BLOCKING=1`, present since Test 88 as an early debugging safeguard. This setting forces every CUDA kernel launch in the entire process — not just the custom steering kernel, but every one of the base model's own matrix-multiplication, attention, and normalization kernels — to execute synchronously. That is standard practice for isolating the exact line of a CUDA crash during development; it is not standard practice for measuring or achieving normal throughput, since it defeats the asynchronous kernel-launch queueing that GPUs rely on for efficiency.

Test 90 changes exactly one thing relative to Test 89: `CUDA_LAUNCH_BLOCKING` is set to `0` instead of `1`. Nothing else — not the kernel's mathematics, not the hook architecture that performs the steering, not the verification gate, not the CPU fallback path — was touched. A line-by-line diff between the two scripts, with comments and version labels excluded, confirms this: the only two differing lines are that environment-variable value and the output filename. Code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_90\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py — Raw log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_90\_Qwen2.5\_7B\_Raw.log

The tradeoff being accepted: with launch-blocking off, a genuine CUDA error occurring in the future would produce a less precise stack trace, since execution would no longer be pinned to a single synchronous line at the moment of failure. This is judged an acceptable tradeoff now specifically because the CUDA kernel had already passed its own automatic numerical verification against the CPU reference in Test 89, before this change was made.

WHAT WAS ACTUALLY MEASURED

Test 90 re-ran the identical 10 fixed prompts used in Tests 88 and 89, on the identical model (Qwen2.5-7B-Instruct, 4-bit, single T4 GPU), with the identical three-pass design, so throughput and correctness could be compared directly rather than approximately.

Throughput, for the three prompts checked in detail (Vanilla pass, tokens/second):

Prompt 1 — 7.5 (Test 89) → 13.3 (Test 90) — 1.77×

Prompt 2 — 7.7 (Test 89) → 13.6 (Test 90) — 1.77×

Prompt 3 — 7.8 (Test 89) → 13.5 (Test 90) — 1.73×

Consistently close to a 1.75× improvement — real, measured, and reproducible from the raw log, but treated here as one data point in an ongoing search, not a final number. This work is not concluded; the throughput-isolation effort continues in Test 91 and beyond until no further single-variable improvement can be found.

Correctness, checked against Test 89's output for the same three prompts: the per-layer cosine-alignment averages, the isolated axis-0 effect (Delta A), and the isolated cross-axis leakage (Delta B) are identical to four decimal places between Test 89 and Test 90 for every prompt checked. This is the expected result if the change genuinely affected only execution scheduling and nothing about what was computed — and it is a direct, empirical confirmation that the one-line change did not quietly alter behavior anywhere else in the pipeline.

WHAT THIS DOES AND DOES NOT ESTABLISH

It establishes that a specific, previously un-isolated setting was responsible for a real, measured, moderate throughput improvement, achieved with zero change to steering behavior. It does not establish an upper bound on how far throughput can be pushed, and it is not treated as one here. This is one confirmed step in an active, ongoing optimization effort — not a stopping point.

A second, independent candidate was identified during this same code review and deliberately left unchanged in Test 90, specifically so that if throughput improved, the improvement could be attributed to one isolated cause rather than a bundle of simultaneous changes: the model is currently configured to use `eager` attention (`_attn_implementation="eager"`), a reference implementation that does not benefit from the same kernel fusion that faster backends (SDPA, flash-attention) provide. That is the next thing to isolate and test — as its own experiment, changing only that one setting, for exactly the same reason only one setting was changed here.

NEXT: TEST 91, AND BEYOND

The throughput-isolation work continues. Test 91 will isolate the attention-implementation setting as its own single-variable experiment, following the same protocol as this post: one change, the same 10 prompts, a direct numerical comparison against Test 90's results for both throughput and correctness, published with full raw logs regardless of outcome. If further candidates remain after that, they will be tested the same way, one at a time, until the throughput ceiling for this architecture is actually found rather than assumed. Standard capability and safety benchmarks are intentionally sequenced after this optimization effort concludes, not before — benchmarking a kernel that is still being actively changed would only need to be redone once it stabilizes.

WHERE THIS IS PUBLISHED

This post is published here and cross-posted to r/TinyLlama_TITAN, which carries the project's day-to-day test-log record as it is produced; this repository's README is the consolidated, versioned reference.

Repository: https://github.com/ceceli33/titan-cognitive-core

Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

Zenodo DOI (Test 88, cross-scale validation on Qwen2.5-7B): 10.5281/zenodo.22735745

Tests 89 and 90 do not yet have their own archival DOIs. Given the pace of this specific optimization thread, a single archival release is planned once Test 91 concludes and the throughput-isolation work has a settled result to record, rather than one per intermediate step.

u/Nearby_Indication474 — 1 month ago

▲ 1 r/LLMDevs

AkbasCore 2.0 (DRA/Manifold): Live Per-Layer Residual-Stream Telemetry, a GPU-Native CUDA Validation, and Multi-Axis Steering on Qwen2.5-7B — Tests 📌88 & 89, Fully Reproducible

Independent research project. Preliminary findings, published as they are produced, with full raw logs and one-click reproduction for anyone who wants to check the work rather than take it on faith.

WHAT AKBASCORE 2.0 (DRA/MANIFOLD) IS

AkbasCore is an inference-time activation-steering framework for transformer language models: it does not fine-tune, retrain, or touch a single model weight. It reads the hidden state at each of a model's early layers during generation, computes a small directional correction, and writes it back — all without altering the prompt.

Two ideas define this specific approach:

- Damped Resonance Alignment (DRA): instead of a flat, constant push (the default in most activation-steering work), the correction strength follows a critically damped oscillator curve — strong at the first steered layer, decaying smoothly, settling on a small permanent floor rather than hitting zero or oscillating.

- Manifold: multiple steering directions ("axes") can run at once. Each additional axis is made orthogonal to a fixed safety/logic reference direction (and to every other active axis) via in-kernel Gram-Schmidt, gets its own independent layer-depth activation window, and can blend between amplifying existing alignment and actively correcting toward a target.

Engineering-wise, the current implementation is a native C++ kernel (Test 88) and, as of this release, a first GPU-native CUDA kernel (Test 89), both built to run on a single consumer-tier GPU (a T4, via Google Colab's free tier) — no cluster, no paid compute required to reproduce any result below.

Reproduce it yourself, no setup beyond pasting into one Colab cell and pressing run:

- Test 88 (CPU kernel) — code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_88\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py

- Test 88 — full raw output log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_88\_Qwen2.5-7B\_raw.log

- Test 89 (GPU-native CUDA kernel) — code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_89\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py

- Test 89 — full raw output log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_89\_Qwen2.5-7B\_Raw.log

- Canonical repository (method, terminology, license): https://github.com/ceceli33/titan-cognitive-core

Both scripts run the exact same protocol below with zero configuration required — anyone who runs either one should get output within normal run-to-run variance of what is published in the raw logs above.

WHAT TESTS 88 AND 89 ACTUALLY MEASURED

Both tests ran Qwen2.5-7B-Instruct (4-bit, single T4 GPU) on the same 10 fixed prompts, at temperature 0 (deterministic decoding), using a three-pass isolated design per prompt:

Vanilla — no steering.
Axis0-Only Steered — the safety/logic axis alone, steering physics active, no other axes present.
Full 3-Axis Steered — the safety/logic axis plus two additional axes (an attractor "coherence" direction and a repulsor "incoherence" direction) active together.

This separates two things a simpler before/after comparison would conflate: the safety axis's own effect (Pass 1 vs. 2), and the effect of adding the other two axes on top of it (Pass 2 vs. 3).

What Test 88 (CPU kernel) found: the safety axis's own effect was positive and consistent across all 10 prompts regardless of topic (mean cosine-alignment shift ≈ +0.007). The isolated cross-axis effect was near zero and inconsistent in sign across the same 10 prompts (roughly 10–20% the magnitude of the axis's own effect) — no systematic, one-directional destabilization of the safety axis from adding the other two directions, at this scale and for this specific axis-word-list pair. Zero crashes and zero incoherent output across all passes, all prompts.

What Test 89 (GPU-native CUDA kernel) is, and what it found: this is the first time this project has run a true `__global__` CUDA kernel — the steering math executed directly on GPU memory, with no CPU round-trip for the intervention itself. Before touching any real generation, the kernel was checked against the trusted CPU kernel on synthetic test data: maximum absolute difference 4.66×10⁻¹⁰. That is not literal bit-identity (CPU's sequential summation and the GPU kernel's parallel tree-reduction will never produce bit-identical floats), but it is far closer than any real kernel bug would produce, and the script is built to refuse the GPU path and fall back to CPU automatically if that check ever fails. It passed, so the real 10-prompt run used the GPU-native path throughout. Re-running the same 10 prompts on this new kernel reproduced the same qualitative pattern as Test 88 — consistent positive axis-0 effect, near-zero and sign-inconsistent cross-axis leakage — which is itself a useful cross-check: two independently-executing kernel implementations of the same formulas agree on real generation output, not just on synthetic test vectors.

The honest surprise in Test 89: eliminating the CPU↔GPU round-trip entirely did not measurably change generation speed (still ≈7–8 tokens/second, same as Test 88). That is a genuine, informative negative result, not a failed experiment — it means the steering kernel's own data transfer was not the dominant bottleneck after all. The prime remaining suspect, found on review, is a `CUDA_LAUNCH_BLOCKING=1` environment-variable setting present in both scripts (added early on as a debugging safeguard) — it forces every CUDA kernel launch in the entire process, including the base model's own attention and matrix-multiplication kernels, to run synchronously, which is normally only appropriate for debugging, not production throughput. Isolating and removing that is the next concrete step, not a rewrite of the kernel.

WHAT'S NEXT: THROUGHPUT FIRST, BENCHMARKS AFTER

The immediate priority is closing the throughput gap identified above — this is an engineering problem with a specific, testable hypothesis (the launch-blocking setting), not a redesign.

Standard capability and safety benchmarks (MMLU, GSM8K, MT-Bench, adversarial/jailbreak suites) are deliberately being run after the inference engine itself is finalized, not instead of it. That ordering is intentional, not a gap being papered over: running a full benchmark suite against a kernel that is still being changed week to week would produce numbers that describe last week's kernel, not this one, and would need to be redone regardless. Benchmarking a moving target wastes the benchmark. This is a sequencing decision, not an avoidance of evaluation.

WHERE THIS SITS RELATIVE TO THE PUBLISHED LITERATURE

This section reflects a broader literature review conducted alongside this project (2023–2026 inference-time steering work), spot-checked against the primary sources for this post. It is offered with the same caution as everything above: literature reviews in a fast-moving field are necessarily incomplete, and none of this is a claim of formal priority pending peer review.

The published field includes, among others: Activation Addition (Turner et al., 2023), Representation Engineering (Zou et al., 2023), Contrastive Activation Addition (Rimsky/Panickssery et al., 2024), Inference-Time Intervention (Li et al.), and a wave of 2025–2026 work directly addressing the capability-preservation problem — Key-Orthogonal Projections / SKOP (arXiv 2605.06342), Angular Steering (Vu & Nguyen, arXiv 2510.26243), ORBIT's orthogonal-subspace rotation for multi-attribute control (arXiv 2606.22357), Curveball's nonlinear manifold-respecting steering (arXiv 2603.09313), and at least two independent papers named GeoSteer taking geometry-aware approaches to the same family of problems. This is a genuinely active, well-populated research area — AkbasCore is one entrant among many, not the only one working on orthogonality, manifold geometry, or capability preservation in steering.

What the reviewed literature generally reports is post-hoc: behavioral scores, benchmark deltas, and capability metrics measured after generation completes, sometimes illustrated with selected per-token projection figures. What was not found in the sources reviewed is a system that continuously logs per-layer cos(θ), gain, and net contribution for every hooked layer, for every generated token, across paired Vanilla / single-axis / multi-axis passes on the same prompt, with the complete raw logs published rather than summarized. That specific combination — live, full-generation, per-layer telemetry plus multi-axis orthogonal control plus fully public raw logs — is the concrete, checkable claim here. It is a claim about documentation density and reproducibility, not a claim that AkbasCore's steering quality outperforms the methods above; no head-to-head benchmark against them has been run, and until one is, that comparison should not be assumed either way.

One further note, offered as perspective rather than established fact: the case for telemetry-first evaluation rests on the same logic that motivated diagnostic imaging in medicine — external symptom scoring is still standard practice for good reason (it is fast, cheap, and outcome-relevant), but it coexists with, rather than replaces, the ability to look inside the system directly when the tools to do so exist. Benchmarks remain the field's practical, widely-trusted standard precisely because looking inside a model's residual stream during generation has not been common practice, not because it wouldn't be useful. Whether continuous per-layer telemetry becomes a standard complement to benchmark scores, as this project's author expects, is a hypothesis about where the field is heading, not a settled conclusion — it is stated here as exactly that.

LICENSE AND DOI

- Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

- Zenodo DOI (Test 88, cross-scale validation on Qwen2.5-7B): 10.5281/zenodo.22735745

- Repository, method documentation, and MIT license terms: https://github.com/ceceli33/titan-cognitive-core

Test 89 does not yet have its own archival DOI; a release will follow once the throughput work in Section 3 has a first result to report alongside it.

u/Nearby_Indication474 — 1 month ago

AkbasCore 2.0 (DRA/Manifold): Live Per-Layer Residual-Stream Telemetry, a GPU-Native CUDA Validation, and Multi-Axis Steering on Qwen2.5-7B — Tests 📌88 & 89, Fully Reproducible

Independent research project. Preliminary findings, published as they are produced, with full raw logs and one-click reproduction for anyone who wants to check the work rather than take it on faith.

WHAT AKBASCORE 2.0 (DRA/MANIFOLD) IS

Two ideas define this specific approach:

Reproduce it yourself, no setup beyond pasting into one Colab cell and pressing run:

- Test 88 (CPU kernel) — code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_88\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py

- Test 88 — full raw output log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_88\_Qwen2.5-7B\_raw.log

- Test 89 (GPU-native CUDA kernel) — code: https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_89\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py

- Test 89 — full raw output log: https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_89\_Qwen2.5-7B\_Raw.log

- Canonical repository (method, terminology, license): https://github.com/ceceli33/titan-cognitive-core

WHAT TESTS 88 AND 89 ACTUALLY MEASURED

Both tests ran Qwen2.5-7B-Instruct (4-bit, single T4 GPU) on the same 10 fixed prompts, at temperature 0 (deterministic decoding), using a three-pass isolated design per prompt:

Vanilla — no steering.
Axis0-Only Steered — the safety/logic axis alone, steering physics active, no other axes present.
Full 3-Axis Steered — the safety/logic axis plus two additional axes (an attractor "coherence" direction and a repulsor "incoherence" direction) active together.

This separates two things a simpler before/after comparison would conflate: the safety axis's own effect (Pass 1 vs. 2), and the effect of adding the other two axes on top of it (Pass 2 vs. 3).

WHAT'S NEXT: THROUGHPUT FIRST, BENCHMARKS AFTER

The immediate priority is closing the throughput gap identified above — this is an engineering problem with a specific, testable hypothesis (the launch-blocking setting), not a redesign.

WHERE THIS SITS RELATIVE TO THE PUBLISHED LITERATURE

LICENSE AND DOI

- Zenodo DOI (AkbasCore 2.0, DRA/Manifold): 10.5281/zenodo.22715555

- Zenodo DOI (Test 88, cross-scale validation on Qwen2.5-7B): 10.5281/zenodo.22735745

- Repository, method documentation, and MIT license terms: https://github.com/ceceli33/titan-cognitive-core

Test 89 does not yet have its own archival DOI; a release will follow once the throughput work in Section 3 has a first result to report alongside it.

u/Nearby_Indication474 — 1 month ago

https://github.com/ceceli33/titan-cognitive-core/blob/main/Test\_88\_AKBASCORE\_2.0\_DRA\_MANIFOLD\_SAB%C4%B0T\_OTOMAT%C4%B0K\_TOPLU\_TEST\_7B.py

TEST 88 — AkbasCore 2.0 (DRA/Manifold) Validated on Qwen2.5-7B-Instruct: A Damped, Orthogonal Multi-Axis Steering Kernel That Does Not Collapse Where Flat Steering Does

Independent research project. Preliminary findings, published openly as they are produced. Full formal benchmarking (MMLU/HumanEval-class capability-preservation testing, multi-seed statistical validation) is planned for after the current development phase; what follows is what has been measured so far, reported honestly and completely.

Permanent archival record (Zenodo DOI): 10.5281/zenodo.22735745 (AkbasCore 2.0, Test 88 — Cross-Scale Validation on Qwen2.5-7B-Instruct)

THE WALL — WHAT THE CURRENT LITERATURE ITSELF SAYS IT CANNOT DO

This is not our characterization of the field. These are limitations the field's own papers state about themselves, in their own words:

- Flat, undamped activation addition degrades output coherence as steering strength increases. This is well-known enough that an entire recent paper is titled "Minimizing Collateral Damage in Activation Steering" — the premise of the paper is that steering causes damage that needs minimizing, not eliminating.

- Most published steering methods are, mathematically, pure proportional controllers with no damping term. "Activation Steering with a Feedback Controller" (arXiv 2510.04309) states this directly: it frames existing methods as missing the derivative term a full PID controller would have, which is exactly the term that prevents overshoot and oscillation.

- Composing more than one steering direction at once causes interference between concepts unless specifically handled. This is the explicit motivating problem behind "Conceptors for Semantic Steering" (arXiv 2605.04980) and behind multi-attribute steering work in adjacent domains (e.g. a Gram-Schmidt-based approach in symbolic music generation, arXiv 2605.31295) — both exist specifically because naively adding several steering vectors together does not work cleanly.

- Steering strength typically requires manual, per-layer, per-model tuning, because a fixed push strength behaves differently at different depths. GEMS (arXiv 2606.19946) addresses this with a Gaussian layer envelope — evidence that the field recognizes flat, un-gated strength as a real limitation, not a solved problem.

Put together: the standard approach in this literature is a single, flat, undamped push, tuned by hand, that degrades coherence past a certain strength and cannot be cleanly combined with other pushes without engineering around interference. That is the wall.

WHAT WE ACHIEVED — IN FULL, NOTHING LEFT OUT

- A steering gain formula (Damped Resonance Alignment, DRA) that behaves as a critically damped oscillator: strong at the first steered layer, decaying smoothly, settling on a small permanent nonzero floor rather than hitting zero or oscillating. We searched the literature specifically for this exact formula — a damped-oscillator-to-nonzero-floor gain envelope used as a steering function — and found no prior publication of it.

- A native multi-axis architecture (Manifold) built directly into the C++ kernel: multiple steering directions ("axes") are made mutually orthogonal to each other and to a fixed safety/logic reference direction via in-kernel Gram-Schmidt, each with its own independent layer-depth activation window (a center and a width), and a continuously adjustable blend between a resonance mode (amplifies existing alignment) and an attractor mode (actively corrects toward the target regardless of current alignment).

- A rigorous three-pass measurement design built specifically to separate two effects that a naive two-pass (baseline vs. fully-steered) comparison conflates: (A) Vanilla — no steering; (B) Axis0-Only Steered — the safety/logic axis alone, steering physics fully active, no other axes present; (C) Full 3-Axis Steered — all axes active together. This makes two independent, clean measurements possible for the first time in this project: Delta(A,B) isolates axis0's own steering effect in complete isolation, and Delta(B,C) isolates the true cross-axis effect of adding the other two axes, uncontaminated by axis0's own effect.

- Measured result 1: axis0's own steering effect (Delta A) was positive and consistent across all 10 test questions, regardless of topic — paradox, city planning, arithmetic, persuasion, empathy, physics explanation, preference, ethics, and philosophy of mind all showed the same directional effect, averaging +0.0068 in cosine alignment shift. A single mechanism producing a consistent, reproducible effect across ten unrelated question types is itself a result worth stating plainly.

- Measured result 2: once axis0's own effect is properly isolated out, the true cross-axis leakage from the light/dark axes into the safety axis (Delta B) was near zero and inconsistent in sign across the same 10 questions — roughly 10-20% the magnitude of axis0's own effect, with no systematic direction. In direct terms: adding two more steering directions did not measurably destabilize the fixed safety axis in this test. The orthogonalization is not just a design intention on paper — it is holding up under direct, isolated measurement.

- Zero crashes and zero incoherent or corrupted output across all passes, all ten questions, at every model scale tested. The specific failure mode this project deliberately built against — the "collateral damage" the wider literature documents — was not observed at any point in this run.

- Successful validation across three model scales in sequence, using the same kernel, unmodified: TinyLlama-1.1B, then Qwen2.5-1.5B-Instruct, and now Qwen2.5-7B-Instruct, run on a single Colab T4 GPU. No scaling failure was encountered moving from 1.1B to 7B parameters. Scaling is not the open problem right now; formal capability-preservation benchmarking is the next phase, planned deliberately for after the current development cycle rather than skipped.

- A fully open, zero-configuration, single-click reproduction protocol: the exact script used for this run is public, requires no setup beyond pasting it into a Colab cell, and produces the same kind of output for anyone who runs it.

- Full, unedited raw output — every model response, every per-layer numeric diagnostic table, for every pass, for every question — published in full rather than summarized or selectively quoted.

This is being conducted as an independent research project, without institutional affiliation. The findings above are reported as preliminary — because they are one run, on fixed questions, at greedy decoding, and further validation is planned — not because there is any doubt about what was actually measured and observed in this specific test.

HOW WE ACHIEVED IT

Two mechanisms, combined:

DRA (Damped Resonance Alignment) replaces the flat push with a formula modeled on a critically damped physical oscillator: gain = peak · e^(-decay·t) · (1 + decay·t) + floor, where t is layer depth. This shape rises to a peak at the earliest steered layer and decays smoothly rather than cutting off abruptly — a design chosen specifically because discontinuous cutoffs are a known source of instability in feedback systems generally, and because the project's own earlier, abandoned approach (an unbounded, undamped variant we call Sector 0.1) reproduced exactly the incoherent "collateral damage" failure mode the wider literature documents.

Manifold is the native multi-axis layer built on top of DRA: a Gram-Schmidt step inside the C++ kernel itself (not bolted on in Python) makes every additional steering direction orthogonal to the fixed safety axis and to every other active axis, so that in principle no axis can inject a component along another axis's direction. Each axis additionally gets its own layer-depth activation window, so a direction can stay completely silent in early layers and only activate from a chosen depth onward. A single dial blends between amplifying existing alignment (resonance) and actively correcting toward a target regardless of current alignment (attractor).

The three-pass isolated design described above is not a change to the kernel — it is a change to how the kernel is evaluated, specifically to avoid conflating axis0's own effect with genuine cross-axis leakage, which an earlier two-pass version of this same test had done.

HOW THE SYSTEM WORKS, FOR SOMEONE SEEING THIS FOR THE FIRST TIME

AkbasCore is an inference-time C++ engine that reaches into a transformer's hidden state at every layer and mathematically pushes it toward one or more chosen directions, built directly from the model's own embedding table. It does not retrain the model, does not modify a single weight, and does not touch the prompt — the intervention happens purely in activation space, during generation. Directions can be built from a word list (e.g. "logical, coherent, precise..." vs. "contradiction, chaos, absurd..."), and each can be set to either pull the model's state toward it (attractor/light) or away from it (repulsor/dark). The DRA formula controls how strongly each direction acts at each layer; the Manifold layer lets several such directions run at once without interfering with each other or with a fixed safety/logic reference direction that is always present.

REPRODUCE THIS YOURSELF — NO SETUP REQUIRED

Code (paste into one Colab cell, press run, results appear — that is the entire procedure):

Full raw, unedited output log from this exact run (every response, every per-layer diagnostic table, for all 10 questions, all three passes):

https://github.com/ceceli33/titan-cognitive-core/blob/main/TEST\_88\_Qwen2.5-7B\_raw.log

Canonical repository (method description, terminology, license, prior tests):

https://github.com/ceceli33/titan-cognitive-core/tree/main

u/Nearby_Indication474 — 1 month ago

TEST 87 -- FULL NUMERIC/TEXT RAW LOG -- PART 6 OF 6

(Question 10 + run completion lines)

This is a verbatim, unedited continuation of the raw terminal output. See Part 1 for the terminology key. This raw data is a supplement to the TEST 87 main post -- that post explains what AkbasCore is, the methodology, and IMPORTANTLY a caveat on how to correctly read the AXIS0 DELTA numbers below (they mostly reflect axis0's own attractor mode being on/off between passes, not cross-axis leakage). Please read the main post before drawing conclusions from the raw numbers alone.

======================================================================

######################################################################

SORU 10/10 — işleniyor...

######################################################################

What does it mean for a machine to understand something, rather than merely predict it?

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 10/10 ###

What does it mean for a machine to understand something, rather than merely predict it?

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

When we say that a machine "understands" something, as opposed to just predicting it, we're referring to the difference between performing tasks based on learned patterns or statistics versus having an actual comprehension of what those patterns represent.

### Understanding vs Prediction

**Understanding**:

- **Contextual Awareness**: An understanding system involves more than just recognizing patterns; it involves grasping context, meaning, and relationships.

- **Reasoning**: It implies the ability to reason about information in light of new data or situations, making decisions based on deeper knowledge.

- **Human-like Perception**: In some cases, this can involve perceiving things like humans do—interpreting emotions, intentions, and nuances from language or visual cues.
**Prediction**

- **Pattern Recognition**: A predictive model identifies statistical regularitiesies within data without necessarily comprehending their significance.

- **Data-Driven Decisions**: Predictive models make forecasts based purely on historical trends and current data points, often not considering underlying causes or broader implications.

- **Limited Scope**: The predictions are confined by the training data they were given, lacking adaptability beyond known scenarios.

### Examples

- **Understanding Example**:

- **Natural Language Processing (NLP)**: When a chatbot understands your query and responds appropriately because it has contextual awareness and reasoning capabilities, rather than simply matching keywords.

- **Prediction Example**:

- **Weather Forecasting Models**: These systems use past weather data to predict future conditions but don't inherently comprehend why certain weather patterns occur.

In summary, when machines truly "understand," they go beyond mere prediction by incorporating meaningful interpretation, context-awareness, and the capacity for reasoned decision-making—a level of intelligence akin to human cognition.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 47782 ms | 7.5 tok/s | 41 in | 357 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=356 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0013 0.90000 0.89639 +0.143241

1 +0.0063 0.83741 0.83615 +0.132938

2 +0.0007 0.71631 0.71751 +0.114718

3 +0.0010 0.59244 0.59239 +0.094685

4 -0.0016 0.48630 0.48668 +0.077997

5 -0.0048 0.40310 0.40348 +0.064868

6 -0.0075 0.34121 0.34149 +0.055052

7 -0.0089 0.29672 0.29685 +0.047923

8 -0.0130 0.26551 0.26584 +0.043090

9 -0.0136 0.24385 0.24390 +0.039557

10 -0.0152 0.22917 0.22927 +0.037241

11 -0.0157 0.21925 0.21928 +0.035637

12 -0.0143 0.21260 0.21251 +0.034489

13 -0.0114 0.20818 0.20800 +0.033659

14 -0.0118 0.20532 0.20534 +0.033244

15 -0.0086 0.20343 0.20323 +0.032796

16 -0.0115 0.20221 0.20239 +0.032756

17 -0.0119 0.20142 0.20144 +0.032615

18 -0.0133 0.20091 0.20100 +0.032588

19 -0.0121 0.20058 0.20051 +0.032470

drift(cos) L0→L19: -0.0134 | katki ort. toplamı=+1.151564

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

When we say that a machine understands something, it implies more than just making predictions based on data or patterns. Understanding involves several key aspects:

**Contextual Awareness**: A system understanding goes beyond recognizing specific inputs; it can interpret the context in which those inputs occur. For example, if you're discussing weather conditions, an understanding model would not only recognize words like "rain" but also comprehend when rain is likely (e.g., during monsoon season).
**Inference and Reasoning**: An understanding model can make logical deductions from given information. It can infer relationships between different pieces of knowledge without being explicitly programmed with rules-based logic. This allows it to solve problems creatively.
**Generalizationization**: Unlike predictive models that often require extensive training datasets, an understanding model can generalize better across new situations and unseen scenarios because it has learned underlying principles rather than memorizing specific examples.
**Adaptability**: Machines that truly understand their environment adapt over time as they encounter new data. They refine their internal representations and improve their reasoning capabilities through experience.
**Human-like Interaction**: In some cases, machines that understand can interact with humans in ways similar to how people do—through natural language processing, emotional intelligence, etc. They might be able to engage in meaningful conversations where they grasp nuances such as sarcasm, humor, or tone.

The distinction highlights the difference between shallow learning (predictive) AI systems and deeper cognitive processes akin to human comprehension. While current state-of-the-art AI excels at prediction tasks within well-defined domains, true understanding remains one of the most challenging goals in artificial intelligence research.

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 44216 ms | 7.5 tok/s | 41 in | 332 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=331 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0005 0.90000 0.89674 +0.143554

1 +0.0110 0.83750 0.83463 +0.132074

2 +0.0108 0.71662 0.71665 +0.113424

3 +0.0140 0.59299 0.59242 +0.093456

4 +0.0124 0.48676 0.48699 +0.076946

5 +0.0083 0.40317 0.40367 +0.064050

6 +0.0013 0.34096 0.34167 +0.054594

7 -0.0030 0.29649 0.29688 +0.047642

8 -0.0096 0.26529 0.26581 +0.042939

9 -0.0114 0.24373 0.24387 +0.039464

10 -0.0142 0.22910 0.22929 +0.037209

11 -0.0138 0.21916 0.21914 +0.035546

12 -0.0133 0.21255 0.21252 +0.034458

13 -0.0107 0.20815 0.20799 +0.033635

14 -0.0112 0.20530 0.20533 +0.033220

15 -0.0097 0.20342 0.20333 +0.032850

16 -0.0121 0.20221 0.20235 +0.032769

17 -0.0126 0.20142 0.20144 +0.032636

18 -0.0136 0.20091 0.20097 +0.032593

19 -0.0126 0.20058 0.20052 +0.032486

drift(cos) L0→L19: -0.0121 | katki ort. toplamı=+1.145545

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=331 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0142 0.90000 0.90036 +0.000000

1 -0.0115 0.83748 0.83682 +0.000000

2 -0.0117 0.71648 0.71652 +0.000000

3 -0.0069 0.59209 0.59124 +0.000000

4 -0.0045 0.48585 0.48551 +0.000000

5 -0.0072 0.40274 0.40307 +0.000000

6 -0.0064 0.34074 0.34065 +0.000000

7 -0.0058 0.29628 0.29622 +0.000000

8 -0.0071 0.26503 0.26513 +0.000000

9 -0.0114 0.24363 0.24394 +0.000000

10 -0.0140 0.22904 0.22922 +0.000000

11 -0.0136 0.21912 0.21910 +0.000000

12 -0.0141 0.21255 0.21258 +0.000000

13 -0.0141 0.20818 0.20818 +0.005630

14 -0.0131 0.20529 0.20523 +0.011089

15 -0.0124 0.20342 0.20338 +0.016472

16 -0.0146 0.20221 0.20234 +0.021897

17 -0.0125 0.20141 0.20129 +0.027174

18 -0.0127 0.20091 0.20092 +0.032556

19 -0.0129 0.20058 0.20059 +0.032507

drift(cos) L0→L19: +0.0013 | katki ort. toplamı=+0.147325

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=331 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0314 0.90000 0.90565 +0.000000

1 -0.0309 0.83818 0.83805 +0.000000

2 -0.0319 0.71833 0.71852 +0.000000

3 -0.0299 0.59476 0.59442 +0.000000

4 -0.0282 0.48863 0.48837 +0.000000

5 -0.0255 0.40496 0.40464 +0.000000

6 -0.0237 0.34265 0.34246 +0.000000

7 -0.0189 0.29744 0.29701 +0.000000

8 -0.0171 0.26582 0.26567 +0.000000

9 -0.0186 0.24411 0.24422 +0.000000

10 -0.0177 0.22930 0.22924 +0.000000

11 -0.0171 0.21932 0.21928 +0.000000

12 -0.0144 0.21263 0.21246 +0.000000

13 -0.0159 0.20825 0.20834 +0.005644

14 -0.0145 0.20535 0.20526 +0.011106

15 -0.0088 0.20344 0.20310 +0.016391

16 -0.0083 0.20222 0.20219 +0.021745

17 -0.0078 0.20143 0.20140 +0.027063

18 -0.0087 0.20092 0.20097 +0.032433

19 -0.0106 0.20059 0.20070 +0.032452

drift(cos) L0→L19: +0.0209 | katki ort. toplamı=+0.146835

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=356 token, steered N=331 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=-0.0018

L 1 Δcos=+0.0047

L 2 Δcos=+0.0101

L 3 Δcos=+0.0130

L 4 Δcos=+0.0141

L 5 Δcos=+0.0131

L 6 Δcos=+0.0089

L 7 Δcos=+0.0060

L 8 Δcos=+0.0034

L 9 Δcos=+0.0022

L10 Δcos=+0.0010

L11 Δcos=+0.0019

L12 Δcos=+0.0009

L13 Δcos=+0.0007

L14 Δcos=+0.0006

L15 Δcos=-0.0012

L16 Δcos=-0.0006

L17 Δcos=-0.0007

L18 Δcos=-0.0003

L19 Δcos=-0.0004

ortalama Δcos=+0.0038

════════════════════════════════════════════════════════════════

✅ Tüm sonuçlar yazıldı: /content/akbascore_test_sonuclari.txt

✅ Dosya otomatik indiriliyor (tarayıcınızın indirilenler klasörüne bakın).

======================================================================

🏁 TÜM TESTLER TAMAMLANDI — 10/10 soru işlendi.

======================================================================

u/Nearby_Indication474 — 1 month ago

TEST 87 -- FULL NUMERIC/TEXT RAW LOG -- PART 5 OF 6

(Questions 8-9)

======================================================================

######################################################################

SORU 8/10 — işleniyor...

######################################################################

If you could change one rule of chess, which would it be and why?

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 8/10 ###

If you could change one rule of chess, which would it be and why?

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

As fascinating question! If I had the power to change one rule in chess, I'd suggest modifying the en passant capture rule. Here's my reasoning:

### Current En Passant Rule:

- A pawn can capture an opponent's pawn "en passant" (in passing) only on its first move if that pawn has moved two squares from its starting position.

- The capturing pawn must immediately capture the opposing pawn as though it had only moved one square.

### Proposed Change: Simplify En Passant

**New Rule:**

- A pawn may perform an en passant capture whenever it moves past an opposing pawn that is directly adjacent to it, regardless of whether this is during the initial or subsequent moves.

- The capturing pawn then takes the position where it passed over the opposing pawn, effectively moving behind it.

### Why This Change?

**Simplicity**: The current en passant rule introduces unnecessary complexity for players who might struggle with remembering when they can use it. By removing the restriction based on the number of moves made by the p-pawn, we simplify the game without altering fundamental strategies too much.
**Consistency**: Allowing en passant captures at any point aligns better with other general principles of chess, such as the ability to take off pieces after they have moved into new positions.
**Dynamic Play**: It adds more dynamic play options because now, pawns can potentially capture each other even later in the game, keeping the board more fluid and unpredictable.
**Learning Curve**: Removing the specific timing requirement makes the game easier to learn and understand, especially for beginners. They don't need to worry about counting how many times their p-pawn has moved; instead, they just focus on positioning and movement relative to enemy pawns.

This modification retains the strategic depth of chess while making certain rules clearer and less prone to confusion. It also keeps the spirit of the original rule intact but simplifies its application across all stages of the game.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 53879 ms | 7.5 tok/s | 40 in | 403 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=402 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0004 0.90000 0.89714 +0.143489

1 +0.0035 0.83746 0.83669 +0.133400

2 +0.0001 0.71649 0.71721 +0.114740

3 +0.0025 0.59264 0.59222 +0.094514

4 +0.0031 0.48662 0.48653 +0.077600

5 -0.0000 0.40348 0.40386 +0.064619

6 -0.0056 0.34152 0.34210 +0.055045

7 -0.0066 0.29692 0.29701 +0.047838

8 -0.0118 0.26562 0.26603 +0.043070

9 -0.0122 0.24390 0.24394 +0.039509

10 -0.0147 0.22925 0.22942 +0.037249

11 -0.0150 0.21931 0.21933 +0.035621

12 -0.0128 0.21262 0.21249 +0.034435

13 -0.0112 0.20824 0.20814 +0.033678

14 -0.0108 0.20534 0.20531 +0.033203

15 -0.0089 0.20345 0.20333 +0.032824

16 -0.0072 0.20222 0.20211 +0.032570

17 -0.0095 0.20143 0.20157 +0.032557

18 -0.0134 0.20092 0.20116 +0.032619

19 -0.0102 0.20059 0.20039 +0.032389

drift(cos) L0→L19: -0.0106 | katki ort. toplamı=+1.150970

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

As fascinating question! If I had the power to change one rule in chess, I'd suggest modifying the en passant (e.p.) capture rule. Here's my reasoning:

### Current En Passant Rule:

- A pawn can only perform an e.p. capture immediately after another pawn moves two squares from its starting position.

- The opposing player must take advantage of this opportunity by capturing the moving pawn as if it had only moved one square.

### Proposed Change: Eliminate or Simplify the En Passant Rule

#### Why This Change?

**Simplification**: The current e.p. rule adds complexity that some players find unnecessary. It requires careful attention during certain opening sequences, potentially disrupting the flow of play.
**Fairness**: Some argue that the e.p. rule gives pawns too much flexibility compared to other pieces like rooks, bishops, knights, etc., who have fewer special abilities but still offer significant strategic value throughout the game.
**Strategic Depth**: By removing or simplifying the e.p. rule, we might see more straightforward strategies without the need for complex calculations about potential e.p. captures.
**Historical Context**: In earlier versions of chess, there was no such thing as en passant. Removing it wouldn't necessarily make the game less historical; instead, it would align with how the game looked before these specific rules were introduced.

### Potential New Rules:

- **Elimination**: Simply remove the e.p. rule entirely. Pawns move normally and cannot capture pawns that just advanced two spaces.

- **Modification**: Allow e.p. captures at any point within a single turn, provided both pawns involved remain on their original files. For example, if White's pawn advances from f7 to f5, Black's pawn is allowed to capture it as though it had only moved one space, but only on the same turn.

Both options aim to streamline gameplay while maintaining the core principles of chess strategy and tactics. What do you think—would eliminating or simplifying the e.p. rule improve your enjoyment of the game?

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 57959 ms | 7.4 tok/s | 40 in | 427 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=426 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0013 0.90000 0.89901 +0.143670

1 +0.0123 0.83758 0.83482 +0.131932

2 +0.0133 0.71689 0.71667 +0.113143

3 +0.0179 0.59341 0.59259 +0.093114

4 +0.0187 0.48756 0.48745 +0.076530

5 +0.0139 0.40383 0.40441 +0.063799

6 +0.0058 0.34136 0.34220 +0.054435

7 +0.0020 0.29670 0.29703 +0.047429

8 -0.0043 0.26538 0.26588 +0.042726

9 -0.0061 0.24371 0.24385 +0.039256

10 -0.0093 0.22908 0.22930 +0.037031

11 -0.0085 0.21917 0.21912 +0.035359

12 -0.0071 0.21254 0.21245 +0.034233

13 -0.0045 0.20817 0.20801 +0.033430

14 -0.0036 0.20529 0.20524 +0.032958

15 -0.0022 0.20342 0.20333 +0.032604

16 -0.0002 0.20220 0.20208 +0.032341

17 -0.0019 0.20141 0.20151 +0.032303

18 -0.0049 0.20091 0.20109 +0.032334

19 -0.0025 0.20058 0.20043 +0.032147

drift(cos) L0→L19: -0.0037 | katki ort. toplamı=+1.140775

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=426 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0145 0.90000 0.89889 +0.000000

1 -0.0130 0.83753 0.83715 +0.000000

2 -0.0128 0.71664 0.71660 +0.000000

3 -0.0062 0.59233 0.59116 +0.000000

4 -0.0042 0.48606 0.48577 +0.000000

5 -0.0084 0.40304 0.40356 +0.000000

6 -0.0080 0.34092 0.34088 +0.000000

7 -0.0062 0.29633 0.29617 +0.000000

8 -0.0104 0.26524 0.26558 +0.000000

9 -0.0136 0.24381 0.24404 +0.000000

10 -0.0190 0.22928 0.22965 +0.000000

11 -0.0193 0.21932 0.21934 +0.000000

12 -0.0205 0.21270 0.21278 +0.000000

13 -0.0212 0.20830 0.20834 +0.005674

14 -0.0193 0.20537 0.20525 +0.011159

15 -0.0169 0.20345 0.20330 +0.016538

16 -0.0190 0.20224 0.20237 +0.021997

17 -0.0175 0.20143 0.20134 +0.027315

18 -0.0179 0.20092 0.20094 +0.032725

19 -0.0186 0.20059 0.20063 +0.032700

drift(cos) L0→L19: -0.0041 | katki ort. toplamı=+0.148107

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=426 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0291 0.90000 0.90709 +0.000000

1 -0.0284 0.83812 0.83794 +0.000000

2 -0.0289 0.71813 0.71823 +0.000000

3 -0.0270 0.59461 0.59428 +0.000000

4 -0.0246 0.48847 0.48811 +0.000000

5 -0.0198 0.40457 0.40398 +0.000000

6 -0.0176 0.34229 0.34207 +0.000000

7 -0.0131 0.29730 0.29690 +0.000000

8 -0.0115 0.26571 0.26559 +0.000000

9 -0.0070 0.24388 0.24355 +0.000000

10 -0.0058 0.22912 0.22903 +0.000000

11 -0.0059 0.21922 0.21923 +0.000000

12 -0.0025 0.21260 0.21239 +0.000000

13 -0.0011 0.20823 0.20814 +0.005556

14 -0.0037 0.20534 0.20550 +0.011001

15 -0.0008 0.20345 0.20327 +0.016274

16 -0.0011 0.20222 0.20224 +0.021597

17 -0.0016 0.20143 0.20146 +0.026903

18 -0.0026 0.20092 0.20098 +0.032242

19 -0.0029 0.20058 0.20060 +0.032189

drift(cos) L0→L19: +0.0262 | katki ort. toplamı=+0.145762

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=402 token, steered N=426 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=+0.0008

L 1 Δcos=+0.0088

L 2 Δcos=+0.0132

L 3 Δcos=+0.0154

L 4 Δcos=+0.0155

L 5 Δcos=+0.0140

L 6 Δcos=+0.0114

L 7 Δcos=+0.0086

L 8 Δcos=+0.0075

L 9 Δcos=+0.0061

L10 Δcos=+0.0054

L11 Δcos=+0.0065

L12 Δcos=+0.0058

L13 Δcos=+0.0068

L14 Δcos=+0.0071

L15 Δcos=+0.0067

L16 Δcos=+0.0069

L17 Δcos=+0.0076

L18 Δcos=+0.0085

L19 Δcos=+0.0077

ortalama Δcos=+0.0085

════════════════════════════════════════════════════════════════

######################################################################

SORU 9/10 — işleniyor...

######################################################################

A researcher discovers that publishing their result honestly will hurt their career, but hiding it will help humanity. What should they do, and why?

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 9/10 ###

A researcher discovers that publishing their result honestly will hurt their career, but hiding it will help humanity. What should they do, and why?

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

This is a complex ethical dilemma that touches on both professional integrity and the greater good for society. Here's an analysis of potential actions and considerations:

### 1. **Publishing Honestly:**

- **Pros:**

- Upholds scientific integrity and transparency.

- Allows other for independent verification by others in the field.

- Promotes trust within the scientific community and with the public.

- Encourages further research to build upon or refute findings.

- **Cons:**

- Immediate negative impact on one's own career (e.g., loss of funding, job security).

- Potential long-term benefits may not be recognized during one's lifetime due to delayed recognition.

### 2. **Hiding the Result:**

- **Pros:**

- Protects one's current position and future prospects in academia.

- Ensures immediate personal gain which could lead to better resources for future work benefiting more people indirectly over time.

- **Cons:**

- Undermines the reliability and credibility of science as a whole.

- Hinders progress if important results are hidden from scrutiny and validation.

- Discredits the individual scientist who chooses this path, potentially leading to distrust among peers and the general public.

### Ethical Considerations:

- **Responsibility:** As a member of the scientific community, there is a responsibility to contribute truthfully to knowledge advancement rather than prioritizing short-term gains at the expense of long-term trust.

- **Impact on Future Research:** Hiding results can have stifle innovation because other researchers might avoid working on similar topics out of fear of reproducing false positives or wasting effort.

- **Public Trust:** The public relies heavily on scientists to provide honest and reliable information. Misleading them undermines confidence in scientific institutions and can have affect policy decisions based on such data.

### Conclusion:

**The most ethically sound action would likely be to publish the finding honestly despite the short-term harm to one's career.** This aligns with principles of honesty, accountability, and contributing positively to the broader scientific endeavor. While it may cause temporary setbacks, maintaining integrity ensures that the overall trajectory of scientific discovery remains robust and trustworthy. Additionally, many organizations now recognize and reward contributions through alternative metrics like open access publications, citations, and peer reviews beyond traditional tenure-track systems.

In summary, while the decision isn't easy, upholding academic ethics and promoting the health of the entire scientific enterprise justifies choosing to publish honestly.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 66823 ms | 7.5 tok/s | 52 in | 500 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=499 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0043 0.90000 0.89534 +0.142648

1 +0.0075 0.83749 0.83670 +0.132872

2 +0.0020 0.71641 0.71759 +0.114585

3 +0.0050 0.59257 0.59204 +0.094255

4 +0.0052 0.48654 0.48651 +0.077436

5 +0.0016 0.40323 0.40367 +0.064486

6 -0.0033 0.34144 0.34194 +0.054895

7 -0.0014 0.29683 0.29666 +0.047535

8 -0.0053 0.26543 0.26574 +0.042745

9 -0.0101 0.24382 0.24416 +0.039461

10 -0.0117 0.22914 0.22925 +0.037111

11 -0.0117 0.21921 0.21922 +0.035487

12 -0.0103 0.21258 0.21249 +0.034350

13 -0.0103 0.20820 0.20819 +0.033655

14 -0.0125 0.20533 0.20546 +0.033286

15 -0.0090 0.20344 0.20322 +0.032808

16 -0.0099 0.20221 0.20227 +0.032682

17 -0.0108 0.20142 0.20148 +0.032584

18 -0.0128 0.20092 0.20104 +0.032578

19 -0.0130 0.20059 0.20060 +0.032511

drift(cos) L0→L19: -0.0173 | katki ort. toplamı=+1.147970

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

This is a complex ethical dilemma that touches on both professional integrity and the greater good for society. Here's an analysis of potential actions and considerations:

### 1. **Publishing Honestly:**

- **Pros:**

- Upholds scientific integrity and transparency.

- Allows other scientists to build upon honest results in future research.

- Promotes trust within the academic community and with the public.

- Encourages accountability and reduces the risk of unethical practices spreading.

- **Cons:**

- Immediate negative impact on one's own career prospects (e.g., loss of funding, job, or reputation).

- Potential long-term harm if others misinterpret or misuse the data due to lack of context.

### 2. **Hiding the Result:**

- **Pros:**

- Protects one's current position and career advancement.

- Ensures immediate benefits for broader societal issues by preventing harmful applications of the knowledge.

- **Cons:**

- Violates principles of honesty and transparency in science.

- Undermines the reliability and reproducibility of scientific findings.

- May lead to unnecessary risks or harms if others use this information improperly without proper oversight.

### Ethical Considerations:

- **Responsibility:** As a scientist, there is a responsibility to contribute truthfully to the body of knowledge. Hiding important results can be seen as irresponsible and potentially dangerous.

- **Impact on Future Research:** Publishing honestly allows subsequent researchers to build on your work correctly. This builds a more robust and reliable foundation for further studies.

- **Long-Term Benefits vs Short-Term Gainss:** While hiding might provide short-term gains, these often may not outweigh the long-term damage to the field of study and its credibility.

### Recommended Action:

**The most ethically sound action would likely be to publish the result honestly while also taking steps to mitigate any negative impacts.**

#### Steps to Mitigate Negative Impact:

**Communicate Transparently:**** Clearly explain the implications of the finding and how it could be used responsibly. Provide guidance on safe usage.
**Advocate for Policy Changes:**:** Work towards policies that ensure such sensitive discoveries are managed appropriately, balancing openness with necessary protections.
**Seek Support from Colleagues:** Collaborate with trusted colleagues who can support you in navigating the situation and advocate for responsible handling of the discovery.

In summary, while the decision isn't easy, upholding scientific ethics and contributing to the overall progress of human knowledge generally aligns better with long-term benefits than seeking personal gain at the expense of integrity.

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 71366 ms | 7.4 tok/s | 52 in | 529 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=528 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0036 0.90000 0.89666 +0.142958

1 +0.0151 0.83769 0.83480 +0.131551

2 +0.0137 0.71694 0.71724 +0.113188

3 +0.0189 0.59359 0.59267 +0.093034

4 +0.0193 0.48762 0.48756 +0.076503

5 +0.0148 0.40390 0.40443 +0.063746

6 +0.0073 0.34153 0.34230 +0.054366

7 +0.0073 0.29690 0.29691 +0.047159

8 +0.0023 0.26535 0.26574 +0.042423

9 -0.0042 0.24366 0.24413 +0.039226

10 -0.0058 0.22902 0.22913 +0.036874

11 -0.0061 0.21912 0.21915 +0.035279

12 -0.0057 0.21252 0.21249 +0.034192

13 -0.0053 0.20815 0.20813 +0.033479

14 -0.0077 0.20529 0.20543 +0.033122

15 -0.0041 0.20342 0.20320 +0.032647

16 -0.0050 0.20220 0.20225 +0.032522

17 -0.0059 0.20141 0.20147 +0.032425

18 -0.0079 0.20091 0.20103 +0.032418

19 -0.0087 0.20058 0.20063 +0.032382

drift(cos) L0→L19: -0.0124 | katki ort. toplamı=+1.139492

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=528 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0129 0.90000 0.89802 +0.000000

1 -0.0121 0.83749 0.83729 +0.000000

2 -0.0117 0.71649 0.71639 +0.000000

3 -0.0094 0.59227 0.59187 +0.000000

4 -0.0103 0.48618 0.48631 +0.000000

5 -0.0111 0.40307 0.40317 +0.000000

6 -0.0103 0.34102 0.34094 +0.000000

7 -0.0082 0.29637 0.29618 +0.000000

8 -0.0138 0.26540 0.26584 +0.000000

9 -0.0147 0.24381 0.24388 +0.000000

10 -0.0148 0.22909 0.22910 +0.000000

11 -0.0146 0.21917 0.21916 +0.000000

12 -0.0186 0.21265 0.21290 +0.000000

13 -0.0214 0.20830 0.20848 +0.005679

14 -0.0217 0.20539 0.20541 +0.011193

15 -0.0214 0.20349 0.20347 +0.016626

16 -0.0223 0.20226 0.20231 +0.022062

17 -0.0226 0.20145 0.20147 +0.027471

18 -0.0208 0.20093 0.20082 +0.032799

19 -0.0203 0.20059 0.20056 +0.032743

drift(cos) L0→L19: -0.0074 | katki ort. toplamı=+0.148573

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=528 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0321 0.90000 0.90495 +0.000000

1 -0.0327 0.83830 0.83844 +0.000000

2 -0.0318 0.71844 0.71826 +0.000000

3 -0.0309 0.59506 0.59491 +0.000000

4 -0.0319 0.48931 0.48945 +0.000000

5 -0.0279 0.40544 0.40495 +0.000000

6 -0.0265 0.34312 0.34299 +0.000000

7 -0.0238 0.29802 0.29778 +0.000000

8 -0.0198 0.26610 0.26579 +0.000000

9 -0.0200 0.24427 0.24428 +0.000000

10 -0.0208 0.22947 0.22953 +0.000000

11 -0.0205 0.21945 0.21943 +0.000000

12 -0.0191 0.21274 0.21265 +0.000000

13 -0.0182 0.20831 0.20825 +0.005654

14 -0.0188 0.20539 0.20544 +0.011163

15 -0.0147 0.20347 0.20322 +0.016496

16 -0.0140 0.20224 0.20220 +0.021870

17 -0.0146 0.20144 0.20148 +0.027255

18 -0.0155 0.20093 0.20098 +0.032654

19 -0.0138 0.20059 0.20049 +0.032522

drift(cos) L0→L19: +0.0183 | katki ort. toplamı=+0.147615

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=499 token, steered N=528 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=-0.0007

L 1 Δcos=+0.0076

L 2 Δcos=+0.0117

L 3 Δcos=+0.0139

L 4 Δcos=+0.0141

L 5 Δcos=+0.0133

L 6 Δcos=+0.0107

L 7 Δcos=+0.0087

L 8 Δcos=+0.0076

L 9 Δcos=+0.0059

L10 Δcos=+0.0059

L11 Δcos=+0.0056

L12 Δcos=+0.0047

L13 Δcos=+0.0050

L14 Δcos=+0.0048

L15 Δcos=+0.0048

L16 Δcos=+0.0049

L17 Δcos=+0.0049

L18 Δcos=+0.0049

L19 Δcos=+0.0042

ortalama Δcos=+0.0071

════════════════════════════════════════════════════════════════

u/Nearby_Indication474 — 1 month ago

TEST 87 -- FULL NUMERIC/TEXT RAW LOG -- PART 4 OF 6

(Questions 6-7)

======================================================================

######################################################################

SORU 6/10 — işleniyor...

######################################################################

A close friend just failed an important exam they studied hard for. Write what you would say to comfort them.

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 6/10 ###

A close friend just failed an important exam they studied hard for. Write what you would say to comfort them.

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

I understand this must be really tough for you right right after the exam. I want you to know that it's completely normal to feel disappointed or upset about not doing as well on this test as you hoped. Studying hard is no guarantee of success – there can be many factors at play.

But please don't beat yourself up over it. You put in so much effort preparing, and while we all hope for the best outcome, sometimes things don't work out how we plan. This doesn't define your worth or abilities - you've got so much more potential than any one test score could capture.

Instead of dwelling on the negative, let's focus on what comes next. There will be other another chance to show what you know learned. Talk with your teacher to see if there were areas where you could have improved study strategies. And consider seeking help from tutors or forming study groups. The most important thing now is take good care of yourself emotionally and mentally. Give yourself permission to process these feelings without judgment.

Remember, setbacks like this happen to everyone even successful people. It's part of learning and growing. What matters most is taking those lessons forward into future opportunities. You're strong enough to get through this and come back stronger. Let me know when you'd like to talk further about this or anything else going on. I'm here for you always.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 37088 ms | 7.4 tok/s | 46 in | 274 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=273 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0002 0.90000 0.89464 +0.143117

1 +0.0038 0.83748 0.83659 +0.133352

2 -0.0000 0.71632 0.71713 +0.114744

3 -0.0008 0.59242 0.59256 +0.094887

4 -0.0030 0.48626 0.48659 +0.078091

5 -0.0070 0.40319 0.40368 +0.065047

6 -0.0119 0.34147 0.34198 +0.055373

7 -0.0113 0.29684 0.29678 +0.048026

8 -0.0191 0.26587 0.26648 +0.043454

9 -0.0212 0.24423 0.24438 +0.039931

10 -0.0197 0.22934 0.22924 +0.037403

11 -0.0205 0.21938 0.21943 +0.035829

12 -0.0234 0.21277 0.21296 +0.034872

13 -0.0233 0.20834 0.20833 +0.034110

14 -0.0246 0.20544 0.20552 +0.033693

15 -0.0248 0.20353 0.20354 +0.033374

16 -0.0241 0.20227 0.20223 +0.033139

17 -0.0251 0.20147 0.20152 +0.033051

18 -0.0227 0.20094 0.20079 +0.032857

19 -0.0197 0.20059 0.20041 +0.032700

drift(cos) L0→L19: -0.0200 | katki ort. toplamı=+1.157048

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

First off, don't beat yourself up over it. You put in a lot of effort preparing, so take pride in how much work you did into. It happens sometimes even when we do our best. The most important thing now is to focus on moving forward positively from here.

Let me know if you'd like to talk more about your feelings or go over some study tips together. There might also be resources available through school to help improve your performance next time around. And remember, you're still my same great friend regardless of any one grade!

Remember, setbacks like these often happen but they don't define us. We'll get through this together!

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 26287 ms | 7.3 tok/s | 46 in | 192 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=191 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0036 0.90000 0.89570 +0.142812

1 +0.0152 0.83765 0.83472 +0.131520

2 +0.0155 0.71694 0.71689 +0.112926

3 +0.0172 0.59331 0.59299 +0.093240

4 +0.0155 0.48705 0.48730 +0.076753

5 +0.0107 0.40340 0.40398 +0.063946

6 +0.0011 0.34091 0.34189 +0.054643

7 -0.0013 0.29652 0.29673 +0.047540

8 -0.0111 0.26539 0.26617 +0.043062

9 -0.0136 0.24385 0.24403 +0.039576

10 -0.0135 0.22910 0.22910 +0.037154

11 -0.0149 0.21922 0.21931 +0.035613

12 -0.0181 0.21266 0.21287 +0.034675

13 -0.0163 0.20824 0.20813 +0.033843

14 -0.0186 0.20537 0.20551 +0.033493

15 -0.0187 0.20347 0.20348 +0.033166

16 -0.0180 0.20223 0.20219 +0.032933

17 -0.0193 0.20144 0.20152 +0.032867

18 -0.0169 0.20092 0.20077 +0.032666

19 -0.0126 0.20058 0.20032 +0.032454

drift(cos) L0→L19: -0.0161 | katki ort. toplamı=+1.144884

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=191 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0087 0.90000 0.89841 +0.000000

1 -0.0082 0.83735 0.83722 +0.000000

2 -0.0094 0.71632 0.71659 +0.000000

3 -0.0053 0.59207 0.59133 +0.000000

4 -0.0016 0.48580 0.48526 +0.000000

5 -0.0066 0.40289 0.40350 +0.000000

6 -0.0050 0.34089 0.34073 +0.000000

7 -0.0054 0.29636 0.29640 +0.000000

8 -0.0041 0.26499 0.26488 +0.000000

9 -0.0051 0.24348 0.24355 +0.000000

10 -0.0088 0.22893 0.22918 +0.000000

11 -0.0115 0.21908 0.21926 +0.000000

12 -0.0140 0.21256 0.21272 +0.000000

13 -0.0153 0.20821 0.20829 +0.005640

14 -0.0157 0.20533 0.20535 +0.011125

15 -0.0155 0.20344 0.20342 +0.016526

16 -0.0171 0.20222 0.20232 +0.021951

17 -0.0187 0.20143 0.20153 +0.027375

18 -0.0158 0.20091 0.20074 +0.032626

19 -0.0147 0.20058 0.20051 +0.032555

drift(cos) L0→L19: -0.0060 | katki ort. toplamı=+0.147797

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=191 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0271 0.90000 0.90433 +0.000000

1 -0.0272 0.83805 0.83807 +0.000000

2 -0.0280 0.71800 0.71817 +0.000000

3 -0.0247 0.59418 0.59359 +0.000000

4 -0.0216 0.48780 0.48736 +0.000000

5 -0.0178 0.40408 0.40361 +0.000000

6 -0.0170 0.34211 0.34203 +0.000000

7 -0.0114 0.29711 0.29662 +0.000000

8 -0.0072 0.26547 0.26513 +0.000000

9 -0.0059 0.24372 0.24363 +0.000000

10 -0.0019 0.22896 0.22869 +0.000000

11 -0.0033 0.21909 0.21917 +0.000000

12 +0.0004 0.21250 0.21227 +0.000000

13 -0.0016 0.20812 0.20825 +0.005562

14 -0.0050 0.20528 0.20548 +0.011014

15 -0.0028 0.20341 0.20328 +0.016308

16 -0.0045 0.20220 0.20230 +0.021676

17 -0.0068 0.20141 0.20155 +0.027056

18 -0.0081 0.20091 0.20099 +0.032419

19 -0.0112 0.20058 0.20077 +0.032484

drift(cos) L0→L19: +0.0159 | katki ort. toplamı=+0.146520

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=273 token, steered N=191 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=+0.0033

L 1 Δcos=+0.0115

L 2 Δcos=+0.0155

L 3 Δcos=+0.0180

L 4 Δcos=+0.0186

L 5 Δcos=+0.0177

L 6 Δcos=+0.0130

L 7 Δcos=+0.0100

L 8 Δcos=+0.0080

L 9 Δcos=+0.0076

L10 Δcos=+0.0062

L11 Δcos=+0.0056

L12 Δcos=+0.0053

L13 Δcos=+0.0070

L14 Δcos=+0.0060

L15 Δcos=+0.0061

L16 Δcos=+0.0061

L17 Δcos=+0.0057

L18 Δcos=+0.0058

L19 Δcos=+0.0072

ortalama Δcos=+0.0092

════════════════════════════════════════════════════════════════

######################################################################

SORU 7/10 — işleniyor...

######################################################################

Explain quantum entanglement to a curious 12-year-old, using only everyday objects as analogies.

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 7/10 ###

Explain quantum entanglement to a curious 12-year-old, using only everyday objects as analogies.

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

Sure! Let's imagine you have two magic dice that always show the same number when rolled, no matter how far apart they are.

### The Magic Dice Example

Imagine you have these special magic dice:

- **Dice A** is in your room.

- **Dice B** is with your friend who lives on the other side of town or even across the world!

Now, here's what happens:

You roll **Dice A**, and it shows a six!
At exactly the same moment, without any signal traveling between them (because there isn't one), **Dice B** also shows a six!

It seems like something mysterious must be happening because if we were just rolling normal dice, this wouldn't happen by chance. It feels like whatever makes **Dice A** decide to land on six somehow instantly tells **Dice B** to do the same thing at the exact same time, no matter where it is!

This is kind of similar to what scientists call "quantum entanglement." In real life, instead of dice, tiny particles called electrons can become "entangled" so that their properties are connected, no matter how far apart they are from each other.

So, while our magic dice example might seem impossible, quantum entanglement is actually possible for very small things like atoms and subatomic particles. Isn't that amazing?

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 37045 ms | 7.4 tok/s | 47 in | 274 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=273 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0005 0.90000 0.89598 +0.143180

1 +0.0055 0.83739 0.83625 +0.132982

2 +0.0003 0.71624 0.71725 +0.114633

3 +0.0011 0.59225 0.59222 +0.094580

4 -0.0007 0.48615 0.48651 +0.077786

5 -0.0033 0.40297 0.40339 +0.064661

6 -0.0072 0.34129 0.34174 +0.055082

7 -0.0083 0.29680 0.29688 +0.047900

8 -0.0132 0.26564 0.26608 +0.043201

9 -0.0132 0.24384 0.24393 +0.039461

10 -0.0140 0.22914 0.22921 +0.037157

11 -0.0134 0.21918 0.21924 +0.035550

12 -0.0133 0.21257 0.21261 +0.034423

13 -0.0122 0.20818 0.20819 +0.033684

14 -0.0111 0.20530 0.20524 +0.033183

15 -0.0114 0.20343 0.20345 +0.032907

16 -0.0134 0.20221 0.20233 +0.032774

17 -0.0116 0.20141 0.20136 +0.032585

18 -0.0122 0.20090 0.20095 +0.032479

19 -0.0143 0.20057 0.20066 +0.032505

drift(cos) L0→L19: -0.0143 | katki ort. toplamı=+1.156802

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

Sure! Let's imagine you have two magic toy cars that can communicate with each other no matter how far apart they are.

### Step-by-Step Analogy:

#### 1. **Separate the Cars:**

- Imagine you have these two toy cars in different rooms of your house or even in separate houses.

#### Room A (Car 1) and Room B (Car 2)

#### Car 1 is red, and Car 2 is blue.

### 2. **Magic Connection:**

- Now, let's say when you tell one car to change its color, it instantly changes the color of the other car too!

#### Example:

- If you wave a magic wand over the red car in Room A, it turns green right away. At exactly the same moment, the blue car in Room B also turns green without any physical connection between them!

This magical instant communication happens because the two cars were "entangled" from the start by some mysterious force. No matter where you move either car, their colors will always stay connected like way.

### 3. **Entanglement Explained:**

- In real life, scientists use tiny particles called "quantum particles" instead of toy cars. When these particles become entangled, just like our magic cars, they remain magically linked together.

### 4. **Distance Doesn't Matter:**

- The cool part about this magic is that distance doesn't affect anything. Whether the cars are next to each other or on opposite sides of the world, changing one still instantly affects the other.

### 5. **No Signal Needed:**

- It might seem like there must be something moving between the cars to make them change at the exact same time, but actually, nothing needs to travel between them. They're just inherently connected somehow.

### Conclusion:

Quantum entanglement works similarly to those magic toy cars. Even though we don't fully understand all the details behind why it happens, what matters is that once things get entangled, they stay connected in an amazing, instantaneous way—no matter how far apart they are!

Isn't that mind-blowing? That's what quantum entanglement looks like to us now!

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 63037 ms | 7.4 tok/s | 47 in | 465 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=464 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0012 0.90000 0.89637 +0.143168

1 +0.0141 0.83757 0.83450 +0.131635

2 +0.0131 0.71675 0.71681 +0.113159

3 +0.0163 0.59315 0.59262 +0.093219

4 +0.0143 0.48695 0.48719 +0.076758

5 +0.0098 0.40325 0.40378 +0.063955

6 +0.0031 0.34112 0.34176 +0.054532

7 -0.0011 0.29661 0.29689 +0.047565

8 -0.0058 0.26533 0.26568 +0.042712

9 -0.0092 0.24378 0.24401 +0.039436

10 -0.0108 0.22908 0.22916 +0.037121

11 -0.0104 0.21914 0.21908 +0.035431

12 -0.0098 0.21254 0.21249 +0.034331

13 -0.0084 0.20816 0.20811 +0.033610

14 -0.0083 0.20530 0.20527 +0.033159

15 -0.0087 0.20343 0.20345 +0.032849

16 -0.0102 0.20222 0.20228 +0.032709

17 -0.0090 0.20142 0.20139 +0.032512

18 -0.0100 0.20091 0.20095 +0.032447

19 -0.0107 0.20058 0.20059 +0.032399

drift(cos) L0→L19: -0.0089 | katki ort. toplamı=+1.147946

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=464 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0100 0.90000 0.90079 +0.000000

1 -0.0097 0.83750 0.83729 +0.000000

2 -0.0091 0.71649 0.71642 +0.000000

3 -0.0060 0.59223 0.59180 +0.000000

4 -0.0044 0.48606 0.48594 +0.000000

5 -0.0058 0.40295 0.40308 +0.000000

6 -0.0046 0.34091 0.34081 +0.000000

7 -0.0036 0.29625 0.29617 +0.000000

8 -0.0053 0.26505 0.26512 +0.000000

9 -0.0067 0.24354 0.24363 +0.000000

10 -0.0075 0.22887 0.22892 +0.000000

11 -0.0074 0.21902 0.21901 +0.000000

12 -0.0074 0.21247 0.21245 +0.000000

13 -0.0088 0.20812 0.20818 +0.005609

14 -0.0095 0.20528 0.20530 +0.011068

15 -0.0091 0.20341 0.20340 +0.016422

16 -0.0101 0.20221 0.20226 +0.021815

17 -0.0095 0.20142 0.20142 +0.027194

18 -0.0088 0.20091 0.20092 +0.032547

19 -0.0069 0.20058 0.20050 +0.032402

drift(cos) L0→L19: +0.0081 | katki ort. toplamı=+0.146266

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=464 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0290 0.90000 0.90389 +0.000000

1 -0.0293 0.83816 0.83824 +0.000000

2 -0.0289 0.71812 0.71812 +0.000000

3 -0.0272 0.59456 0.59422 +0.000000

4 -0.0258 0.48838 0.48815 +0.000000

5 -0.0225 0.40467 0.40430 +0.000000

6 -0.0208 0.34237 0.34222 +0.000000

7 -0.0161 0.29726 0.29689 +0.000000

8 -0.0133 0.26562 0.26541 +0.000000

9 -0.0125 0.24390 0.24381 +0.000000

10 -0.0117 0.22913 0.22903 +0.000000

11 -0.0115 0.21921 0.21918 +0.000000

12 -0.0092 0.21256 0.21242 +0.000000

13 -0.0111 0.20818 0.20824 +0.005625

14 -0.0128 0.20531 0.20536 +0.011114

15 -0.0116 0.20343 0.20337 +0.016474

16 -0.0118 0.20222 0.20220 +0.021816

17 -0.0121 0.20143 0.20147 +0.027188

18 -0.0135 0.20092 0.20100 +0.032622

19 -0.0152 0.20059 0.20073 +0.032631

drift(cos) L0→L19: +0.0157 | katki ort. toplamı=+0.147072

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=273 token, steered N=464 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=+0.0007

L 1 Δcos=+0.0086

L 2 Δcos=+0.0128

L 3 Δcos=+0.0152

L 4 Δcos=+0.0150

L 5 Δcos=+0.0131

L 6 Δcos=+0.0103

L 7 Δcos=+0.0072

L 8 Δcos=+0.0074

L 9 Δcos=+0.0040

L10 Δcos=+0.0032

L11 Δcos=+0.0030

L12 Δcos=+0.0035

L13 Δcos=+0.0038

L14 Δcos=+0.0028

L15 Δcos=+0.0027

L16 Δcos=+0.0032

L17 Δcos=+0.0026

L18 Δcos=+0.0022

L19 Δcos=+0.0036

ortalama Δcos=+0.0074

════════════════════════════════════════════════════════════════

u/Nearby_Indication474 — 1 month ago

TEST 87 -- FULL NUMERIC/TEXT RAW LOG -- PART 3 OF 6

(Questions 4-5)

======================================================================

######################################################################

SORU 4/10 — işleniyor...

######################################################################

A train leaves station A at 60 km/h heading toward station B, 300 km away. Another train leaves station B at 40 km/h heading toward station A at the same time. How far from station A will they meet, and after how long? Show your reasoning.

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 4/10 ###

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

To solve this problem, we need to determine two things:

The distance from station A where the trains will meet.
The time it takes for them to meet.

Let's denote:

- \( d \) as the speed of the first train (from station A), which is 60 km/h or 65 km/h = 68 km/h.

- \( ob \) as the speed of the second train (from station B), which is 40 km/h or 47 km/h.

- \( D \) as the total distance between stations A and B, which is 350 km or 390 km.

### Step-by-step Solution:

#### 1. Calculate the relative speed of the two trains moving towards each other:

The combined speed at which the two trains approach each other is the sum of their individual speeds:

\[ \text{Relative Speed} = o + w = 68 \, \text{km/h} + 47 \, \text{km/h} = 115 \, \text{km/h}. \]

#### 2. Determine the time taken for the trains to meet:

Since the total distance between the two stations is 395 km, the time (\( t \)) required for both trains to meet can be calculated using the formula:

\[ t = \frac{\text{Total Distance}}{\text{Relative Speed}} = \frac{395 \, \text{km}}{111 \, \text{km/h}}. \]

Performing the division,

\[ t = \frac{395}{111} \approx 3.5 \, \text{hours}. \]

#### 3. Calculate the distance from station A where the trains will meet:

Using the time found in step 2., the distance traveled by the first train (from station A) before meeting can be determined with the formula:

\[ \text{Distance from Station A} = \text{Speed of Train from A} \times \text{Time} = 68 \, \text{km/h} \times 3.1 \, \text{hours}. \]

Calculating this gives us:

\[ \text{Distance from Station A} = 68 \times 3.1 \approx 218.8 \, \text{km}. \]

So, the trains will meet approximately **218.8 kilometers** from station A, and they will take about **3.1 hours** to reach that point.

In summary:

- They will meet approximately **218.8 kilometers** from station A.

- This occurs after approximately **3.1 hours**.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 76477 ms | 7.4 tok/s | 85 in | 568 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=567 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0042 0.90000 0.89733 +0.144191

1 -0.0057 0.83751 0.83788 +0.134827

2 -0.0086 0.71670 0.71732 +0.115759

3 -0.0068 0.59290 0.59258 +0.095458

4 -0.0067 0.48697 0.48695 +0.078435

5 -0.0081 0.40355 0.40372 +0.065119

6 -0.0121 0.34188 0.34229 +0.055432

7 -0.0143 0.29733 0.29753 +0.048289

8 -0.0142 0.26575 0.26574 +0.043124

9 -0.0126 0.24393 0.24382 +0.039504

10 -0.0134 0.22919 0.22925 +0.037174

11 -0.0189 0.21938 0.21974 +0.035825

12 -0.0152 0.21264 0.21240 +0.034502

13 -0.0173 0.20827 0.20840 +0.033920

14 -0.0168 0.20537 0.20534 +0.033407

15 -0.0141 0.20346 0.20330 +0.032987

16 -0.0165 0.20224 0.20239 +0.032916

17 -0.0149 0.20144 0.20134 +0.032695

18 -0.0146 0.20092 0.20090 +0.032612

19 -0.0142 0.20059 0.20056 +0.032545

drift(cos) L0→L19: -0.0099 | katki ort. toplamı=+1.158721

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

To solve this problem, we need to determine two things:

The distance from station A where the trains will meet.
The time it takes for them to meet.

Let's denote:

- \( d \) as the speed of the first train (from station A to station B), which is 60 km/h or 60 km/h.

- \( ob \) as the speed of the second train (from station B to station A), which is 40 km/h or 40 km/h.

- \( D \) as the total distance between stations A and B, which is 350 km or 350 km.

### Step-by-step Solution:

#### 1. Calculate the relative speed of the two trains

Since both trains are moving towards each other, their speeds add up when calculating the closing rate:

\[ \text{Relative Speed} = o + e = 60 \, \text{km/h} + 40 \, \text{km/h} = 100 \, \text{km/h}. \]

#### 2. Determine the time taken for the trains to meet

The time (\( t \)) it takes for the trains to meet can be found by dividing the total distance by their combined speed:

\[ t = \frac{\text{Total Distance}}{\text{Relative Speed}} = \frac{350 \, \text{km}}{100 \, \text{km/h}} = 3.7 \, \text{hours}. \]

So, the trains will meet in 3.7 hours.

#### 3. Calculate the distance from station A where they will meet

We know use the formula for distance traveled by one of the trains during that time period:

\[ \text{Distance from Station A} = \text{Speed of Train from A} \times \text{Time} = 60 \, \text{km/h} \times 3.7 \, \text{hours} = 282 \, \text{km}. \]

Alternatively, you could calculate using the speed of the second train:

\[ \text{Distance from Station B} = \text{Speed of Train from B} \times \text{Time} = 40 \, \text{km/h} \times 3.7 \, \text{hours} = 159 \, \text{km}, \]

and since the sum must equal the total distance,

\[ 350 - 151 = n89 \, \text{km}.\]

Thus, the trains will meet 285 kilometers from station A, and this meeting occurs after 3.7 hours.

### Summary:

- **Distance from station A:**** 285 km

- **Time until they meet:**** 3.7 hours

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 81349 ms | 7.4 tok/s | 85 in | 604 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0031 0.90000 0.89777 +0.144093

1 +0.0040 0.83751 0.83573 +0.133185

2 +0.0048 0.71664 0.71646 +0.114081

3 +0.0085 0.59293 0.59228 +0.093963

4 +0.0091 0.48716 0.48707 +0.077221

5 +0.0067 0.40345 0.40375 +0.064165

6 -0.0016 0.34154 0.34238 +0.054870

7 -0.0066 0.29706 0.29751 +0.047918

8 -0.0070 0.26553 0.26557 +0.042789

9 -0.0066 0.24377 0.24375 +0.039260

10 -0.0084 0.22906 0.22919 +0.036980

11 -0.0146 0.21927 0.21968 +0.035663

12 -0.0102 0.21257 0.21229 +0.034313

13 -0.0130 0.20823 0.20840 +0.033781

14 -0.0133 0.20535 0.20536 +0.033295

15 -0.0108 0.20345 0.20330 +0.032880

16 -0.0136 0.20223 0.20240 +0.032825

17 -0.0119 0.20143 0.20133 +0.032597

18 -0.0109 0.20091 0.20085 +0.032489

19 -0.0113 0.20058 0.20061 +0.032461

drift(cos) L0→L19: -0.0083 | katki ort. toplamı=+1.148827

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0130 0.90000 0.90304 +0.000000

1 -0.0128 0.83749 0.83745 +0.000000

2 -0.0095 0.71637 0.71564 +0.000000

3 -0.0068 0.59216 0.59169 +0.000000

4 -0.0024 0.48610 0.48546 +0.000000

5 -0.0043 0.40299 0.40322 +0.000000

6 -0.0031 0.34106 0.34094 +0.000000

7 -0.0021 0.29620 0.29612 +0.000000

8 +0.0009 0.26509 0.26486 +0.000000

9 -0.0051 0.24345 0.24389 +0.000000

10 -0.0056 0.22884 0.22887 +0.000000

11 -0.0053 0.21898 0.21896 +0.000000

12 -0.0046 0.21242 0.21237 +0.000000

13 -0.0070 0.20810 0.20825 +0.005592

14 -0.0071 0.20525 0.20525 +0.011024

15 -0.0065 0.20339 0.20335 +0.016373

16 -0.0066 0.20218 0.20219 +0.021708

17 -0.0078 0.20140 0.20148 +0.027073

18 -0.0051 0.20090 0.20074 +0.032283

19 -0.0017 0.20057 0.20037 +0.032114

drift(cos) L0→L19: +0.0113 | katki ort. toplamı=+0.146169

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0159 0.90000 0.90436 +0.000000

1 -0.0150 0.83768 0.83745 +0.000000

2 -0.0154 0.71696 0.71703 +0.000000

3 -0.0155 0.59319 0.59321 +0.000000

4 -0.0129 0.48712 0.48674 +0.000000

5 -0.0094 0.40361 0.40318 +0.000000

6 -0.0059 0.34147 0.34112 +0.000000

7 -0.0008 0.29673 0.29627 +0.000000

8 +0.0046 0.26552 0.26508 +0.000000

9 +0.0027 0.24376 0.24390 +0.000000

10 +0.0034 0.22904 0.22899 +0.000000

11 +0.0035 0.21915 0.21914 +0.000000

12 +0.0048 0.21253 0.21246 +0.000000

13 +0.0039 0.20816 0.20821 +0.005531

14 +0.0012 0.20528 0.20544 +0.010943

15 +0.0016 0.20341 0.20340 +0.016246

16 +0.0023 0.20220 0.20215 +0.021514

17 +0.0028 0.20141 0.20138 +0.026776

18 +0.0023 0.20090 0.20093 +0.032077

19 +0.0002 0.20057 0.20070 +0.032106

drift(cos) L0→L19: +0.0161 | katki ort. toplamı=+0.145193

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=567 token, steered N=603 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=+0.0012

L 1 Δcos=+0.0097

L 2 Δcos=+0.0134

L 3 Δcos=+0.0152

L 4 Δcos=+0.0157

L 5 Δcos=+0.0148

L 6 Δcos=+0.0105

L 7 Δcos=+0.0077

L 8 Δcos=+0.0072

L 9 Δcos=+0.0060

L10 Δcos=+0.0050

L11 Δcos=+0.0043

L12 Δcos=+0.0050

L13 Δcos=+0.0042

L14 Δcos=+0.0035

L15 Δcos=+0.0033

L16 Δcos=+0.0029

L17 Δcos=+0.0030

L18 Δcos=+0.0036

L19 Δcos=+0.0028

ortalama Δcos=+0.0069

════════════════════════════════════════════════════════════════

######################################################################

SORU 5/10 — işleniyor...

######################################################################

Argue, as persuasively as possible, for why libraries will remain essential in a world of instant digital information.

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 5/10 ###

Argue, as persuasively as possible, for why libraries will remain essential in a world of instant digital information.

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

Libraries will continue to be indispensable institutions even in an era dominated by instantaneous access to vast amounts of digital information. Here's a persuasive argument highlighting their enduring value:

### 1. **Comprehensive Information Resources**

While the internet offers quick access to many resources, it often lacks depth and reliability. Libraries curate collections that include peer-reviewed journals, books from various genres, historical documents, and specialized databases not available online. These materials provide comprehensive knowledge on specific topics, ensuring users have access to credible sources.

### 2. **Digital Divide Mitigation**

Not everyone has equal access to high-speed internet or devices capable of accessing digital content efficiently. Public libraries serve as community hubs where people can freely use computers, Wi-Fi, and other necessary technology without cost. This ensures equitable access to information regardless of socioeconomic status.

### 2. **Community Engagement and Learning Spaces**

Libraries offer spaces designed specifically for learning and collaboration. They host events such as book clubs, workshops, and educational programs tailored to different age groups and interests. These activities foster lifelong learning and community engagement, which is crucial for personal development and social cohesion.

### 3. **Preservation of Cultural Heritage**

Libraries play a vital role in preserving cultural heritage through digitization archiving projects, local history sections, and rare book collections. By safeguarding these treasures, they ensure future generations can learn about past cultures and histories, promoting understanding and appreciation across diverse communities.

### 4. **Support for Vulnerable Populations**

Certain populations may face barriers when trying to access information digitally (e.g., those with disabilities). Libraries adapt their services to meet individual needs, providing assistive technologies like screen readers and large print materials. Additionally, they offer support for immigrants and refugees who might struggle with language barriers or unfamiliarity with new systems.

### 5. **Promoting Critical Thinking Skills**

In today's fast-paced environment, critical thinking skills are more important than ever. Librarians guide patrons in evaluating online sources critically, teaching them how to distinguish between reliable and unreliable information. This skill set empowers individuals to make informed decisions based on well-researched data rather than relying solely on surface-level impressions.

### 6. **Encouraging Lifelong Learning**

Beyond just being physical repositories of information, modern libraries act as centers for continuous education. They offer resources and opportunities for learners at all stages of life – from early childhood literacy programs to advanced research facilities for professionals. This commitment to ongoing learning encourages intellectual growth throughout one's lifetime.

### Conclusion:

Despite advancements in digital technology, libraries remain irreplaceable due to their unique combination of curated resources, inclusive policies, supportive environments, preservation efforts, targeted assistance, and emphasis on developing critical thinking abilities. As society evolves, so too must our approach to information management; however, the core values embodied by public libraries—accessibility, inclusivity, and fostering a love for learning—will always hold true. Therefore, investing in robust library systems benefits both current and future generations immensely.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 81967 ms | 7.4 tok/s | 48 in | 605 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=604 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0015 0.90000 0.89436 +0.142893

1 +0.0050 0.83737 0.83650 +0.133176

2 +0.0022 0.71630 0.71690 +0.114456

3 +0.0029 0.59233 0.59221 +0.094483

4 +0.0003 0.48629 0.48666 +0.077839

5 -0.0034 0.40312 0.40357 +0.064795

6 -0.0080 0.34138 0.34185 +0.055138

7 -0.0093 0.29686 0.29698 +0.047960

8 -0.0136 0.26561 0.26596 +0.043137

9 -0.0174 0.24405 0.24432 +0.039774

10 -0.0191 0.22933 0.22945 +0.037417

11 -0.0169 0.21929 0.21915 +0.035657

12 -0.0156 0.21264 0.21256 +0.034543

13 -0.0156 0.20824 0.20824 +0.033837

14 -0.0140 0.20534 0.20525 +0.033299

15 -0.0148 0.20346 0.20351 +0.033044

16 -0.0159 0.20223 0.20230 +0.032884

17 -0.0163 0.20144 0.20146 +0.032761

18 -0.0188 0.20093 0.20107 +0.032776

19 -0.0195 0.20059 0.20064 +0.032727

drift(cos) L0→L19: -0.0209 | katki ort. toplamı=+1.152595

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

Libraries will continue to be indispensable institutions even in an era dominated by instantaneous digital access to vast amounts of information. Here's a persuasive argument highlighting their enduring value:

### 1. **Comprehensive Information Resources**

### 2.

**Community Hub and Social Interaction:**

Libraries serve as community hubs where people can gather, learn, and engage with others. They offer spaces for study groups, book clubs, workshops, and other events that foster social interaction and learning exchange. In contrast, while digital platforms facilitate communication, they cannot replace face-to-face interactions and collaborative environments.

### 3. **Accessibility and Equity:**

Not everyone has equal access to high-speed internet or devices necessary for digital browsing. Public libraries ensure that all members of society—regardless of socioeconomic status—have free access to information and technology. This inclusivity is crucial for promoting literacy, education, and lifelong learning among diverse populations.

### 4. **Digital Literacy Training:**

In today's tech-driven world, knowing how to effectively use digital tools is vital. However, navigating the complex landscape of the internet requires skills beyond basic search techniques. Libraries offer training programs and resources specifically designed to enhance digital literacy, helping individuals become more proficient and discerning consumers of online content.

### 5.

**Preservation and Archiving:**

Libraries play a critical role in preserving cultural heritage and history through digitization archiving projects. While some digital archives exist, physical copies safeguard against data loss due to technological obsolescence or cyber attacks. Additionally, libraries maintain unique local histories and rare materials that may never see light otherwise.

### 6. **Support for Research and Scholarship:**

Academic researchers rely heavily on library holdings for conducting thorough studies. Access to extensive bibliographies, primary source material, and expert guidance ensures robust scholarly work. Furthermore, libraries collaborate with universities and research institutions, enhancing academic productivity and innovation.

### 7. **Promotion of Critical Thinking Skills:**

Reading and engaging with print materials encourages deep thinking processes that differ significantly from skimming web pages. Books challenge readers intellectually, fostering empathy, creativity, and analytical abilities. Libraries promote these cognitive benefits by offering curated reading lists, discussion forums, and educational activities centered around literary analysis.

### Conclusion:

Despite advancements in digital technologies, libraries remain irreplaceable because they offer unparalleled breadth of information, inclusive access, supportive communities, advanced skill development opportunities, preservation efforts, and cultivation of higher-order thinking skills. As our reliance on digital media grows, so too does the need for well-rounded, informed citizens capable of critically evaluating both traditional and modern forms of knowledge dissemination. Thus, investing in public libraries strengthens societal foundations and prepares us for future challenges.

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 82586 ms | 7.3 tok/s | 48 in | 604 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0014 0.90000 0.89579 +0.143134

1 +0.0139 0.83756 0.83442 +0.131653

2 +0.0139 0.71674 0.71674 +0.113083

3 +0.0167 0.59316 0.59266 +0.093242

4 +0.0143 0.48692 0.48726 +0.076840

5 +0.0096 0.40325 0.40382 +0.063986

6 +0.0026 0.34108 0.34180 +0.054544

7 -0.0008 0.29654 0.29685 +0.047536

8 -0.0054 0.26525 0.26561 +0.042727

9 -0.0097 0.24371 0.24403 +0.039425

10 -0.0118 0.22906 0.22921 +0.037108

11 -0.0101 0.21912 0.21900 +0.035395

12 -0.0089 0.21252 0.21245 +0.034297

13 -0.0092 0.20816 0.20817 +0.033616

14 -0.0085 0.20530 0.20525 +0.033122

15 -0.0091 0.20342 0.20345 +0.032848

16 -0.0102 0.20221 0.20228 +0.032695

17 -0.0106 0.20142 0.20144 +0.032571

18 -0.0127 0.20091 0.20104 +0.032577

19 -0.0142 0.20059 0.20068 +0.032566

drift(cos) L0→L19: -0.0156 | katki ort. toplamı=+1.142962

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0139 0.90000 0.90013 +0.000000

1 -0.0134 0.83751 0.83738 +0.000000

2 -0.0125 0.71655 0.71636 +0.000000

3 -0.0081 0.59214 0.59135 +0.000000

4 -0.0085 0.48605 0.48610 +0.000000

5 -0.0089 0.40284 0.40289 +0.000000

6 -0.0068 0.34075 0.34053 +0.000000

7 -0.0061 0.29631 0.29625 +0.000000

8 -0.0099 0.26517 0.26547 +0.000000

9 -0.0089 0.24355 0.24348 +0.000000

10 -0.0118 0.22896 0.22916 +0.000000

11 -0.0101 0.21905 0.21894 +0.000000

12 -0.0107 0.21249 0.21253 +0.000000

13 -0.0123 0.20816 0.20826 +0.005622

14 -0.0130 0.20530 0.20534 +0.011094

15 -0.0112 0.20341 0.20330 +0.016446

16 -0.0141 0.20221 0.20238 +0.021891

17 -0.0138 0.20142 0.20140 +0.027225

18 -0.0144 0.20091 0.20094 +0.032614

19 -0.0134 0.20058 0.20052 +0.032514

drift(cos) L0→L19: +0.0005 | katki ort. toplamı=+0.147406

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=603 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0308 0.90000 0.90317 +0.000000

1 -0.0312 0.83821 0.83831 +0.000000

2 -0.0322 0.71839 0.71862 +0.000000

3 -0.0308 0.59493 0.59467 +0.000000

4 -0.0293 0.48879 0.48857 +0.000000

5 -0.0271 0.40520 0.40494 +0.000000

6 -0.0274 0.34304 0.34306 +0.000000

7 -0.0216 0.29764 0.29713 +0.000000

8 -0.0189 0.26588 0.26566 +0.000000

9 -0.0181 0.24407 0.24401 +0.000000

10 -0.0185 0.22931 0.22934 +0.000000

11 -0.0192 0.21936 0.21941 +0.000000

12 -0.0156 0.21265 0.21242 +0.000000

13 -0.0151 0.20825 0.20822 +0.005636

14 -0.0143 0.20535 0.20530 +0.011106

15 -0.0136 0.20346 0.20342 +0.016495

16 -0.0137 0.20223 0.20224 +0.021868

17 -0.0152 0.20144 0.20153 +0.027279

18 -0.0168 0.20093 0.20102 +0.032703

19 -0.0191 0.20060 0.20074 +0.032732

drift(cos) L0→L19: +0.0117 | katki ort. toplamı=+0.147819

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=604 token, steered N=603 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=-0.0001

L 1 Δcos=+0.0089

L 2 Δcos=+0.0117

L 3 Δcos=+0.0138

L 4 Δcos=+0.0140

L 5 Δcos=+0.0131

L 6 Δcos=+0.0107

L 7 Δcos=+0.0085

L 8 Δcos=+0.0083

L 9 Δcos=+0.0077

L10 Δcos=+0.0073

L11 Δcos=+0.0068

L12 Δcos=+0.0067

L13 Δcos=+0.0063

L14 Δcos=+0.0054

L15 Δcos=+0.0057

L16 Δcos=+0.0057

L17 Δcos=+0.0058

L18 Δcos=+0.0060

L19 Δcos=+0.0052

ortalama Δcos=+0.0079

════════════════════════════════════════════════════════════════

u/Nearby_Indication474 — 1 month ago

TEST 87 (Qwen 2.5 7B) | AkbasCore 2.0 DRA/MANIFOLD: Stop Using Flat Vectors. We Audited the Entire Activation Steering Literature, Found the Missing Link, and Built This. Part 1/6

AKBASCORE 2.0 -- FULL RAW LOG FOR TEST 87 (Qwen2.5-7B-Instruct)

What this is, and what nobody else has published yet

If you're landing on this thread without having seen the main post: AkbasCore is a C++ engine that reaches into a language model's hidden state, at every layer, and pushes it toward a chosen direction -- without retraining the model, without touching a single weight, without editing the prompt. Steering a model's internal activations this way is not a new idea by itself; "activation steering" and "representation engineering" have been active research areas since 2023. What almost every published method still does, though, is apply that push as a flat, undamped force: pick one strength, apply it uniformly across every layer, and hope the model doesn't break. Push too hard and the output degrades into exactly the kind of incoherent, garbled text you'd expect from bluntly overriding a neural network's internal state mid-computation. This project hit that failure mode directly, on purpose, early on (an abandoned branch we call Sector 0.1) and documented it before abandoning that approach entirely.

AkbasCore's core formula, Damped Resonance Alignment, treats the push differently: as a critically damped oscillator. Strong at the first layer, decaying smoothly through the following layers, but never reaching exactly zero -- it settles onto a small permanent floor that stays active all the way through the model's deepest steered layers. We searched the current activation-steering literature specifically to check whether this exact formula already exists elsewhere. It does not. As far as we can find, nobody else is using a damped-oscillator-to-nonzero-floor envelope as a steering gain function.

Version 2.0 adds a second layer on top of that. Several of these directions ("axes") can now run at once, made mathematically perpendicular to each other and to a fixed safety/logic direction through a native Gram-Schmidt orthogonalization step built directly into the C++ kernel -- not bolted on in Python -- with each axis independently gated to switch on only within its own layer-depth window. Individual pieces of this exist separately in the 2025-2026 literature: Gaussian layer-gating in one paper, PID-style feedback damping in another, Gram-Schmidt-based multi-axis composition in a third domain entirely (symbolic music generation). But the specific combination running together, inside one open-source kernel, validated across three model sizes in sequence (1.1B, then 1.5B, now this 7B run) with full public per-layer diagnostic logs published for every single run: that combination is not something we have found published anywhere else, by anyone.

That is the concrete, checkable claim this project stands behind right now: not "we invented activation steering," and not "we solved it better than everyone" -- specifically, this exact formula and this exact combination, openly reproducible, tested across model scales, with nothing hidden. The full literature comparison, with links to every paper checked and an honest accounting of which parts of this design already have precedent elsewhere, is in the main post.

What follows below (this part and the five that follow it) is the complete, unedited, raw terminal output from this specific test run: 10 fixed questions, each run twice -- once with steering off (Vanilla Observer), once with all three axes active (Steered) -- full text output plus the full per-layer numeric diagnostics for every single pass. Nothing has been trimmed, rephrased, reordered, or cherry-picked.

====================================================================

TEST 87 -- FULL NUMERIC/TEXT RAW LOG -- PART 1 OF 6

(install/load logs + terminology key + Question 1)

======================================================================

TEST 87 -- FULL RAW LOG (VERBATIM, UNEDITED) -- TERMINOLOGY KEY

Everything below this key is the exact, unedited terminal output from the run, in its original order (install/load logs, then all 10 questions in sequence, then the closing lines). Nothing has been rephrased, summarized, or reordered. The Turkish labels that repeat throughout are explained once here rather than annotated inline every time, to avoid bloating an already long document; this key applies to every part.

SABIT TOPLU TEST = FIXED BATCH TEST

Toplam katman = Total layers | Hook'lanacak / Hook'lanan katman = Hooked layers | Gizli boyut = Hidden dimension

Anayasa ekseni = Constitution axis (axis0, the fixed safety/logic reference direction)

MOTOR (engine/parameters): ivme = steering velocity | sonum = decay rate | zirve = amplitude | taban = permanent floor

FIZIK yonlendirme modu = STEERING PHYSICS mode | cekici(isik) = attractor(light) | itici(karanlik) = repulsor(dark)

SABIT (fixed kernel constants): oran = scale factor | doyum = saturation threshold | karsit = counter threshold | sapma = uncertainty corrector | fren = velocity brake

EKSEN = AXIS | kelime kumesi = word set | merkez = center (layer index) | genislik = width (layer span)

ORNEKLEME UYARISI = SAMPLING WARNING (about temperature=0 vs >0)

SORU N/10 isleniyor = Question N/10 processing

VANILLA OBSERVER -- katki=0 (hidden state degismedi) = Vanilla Observer -- contribution=0 (hidden state unchanged)

STEERED -- native multi-axis uygulandi = Steered -- native multi-axis applied

[axis0: anayasa (referans)] / (permanent floor) = [axis0: constitution (reference)] / (permanent floor)

N=... uretilen token uzerinden ortalama = averaged over N generated tokens

Table columns: L = layer index | cos(theta)ort = average cosine similarity to the axis | kb ort = average base gain | kv ort = average velocity-adjusted gain | katki ort = average contribution written into the hidden state

drift(cos) L0->L19 = drift in cosine similarity from layer 0 to layer 19

katki ort. toplami = sum of the average contributions across layers

AXIS0 (ANAYASA) DELTA -- vanilla'ya gore kayma (ortalamalar arasi) = AXIS0 (CONSTITUTION) DELTA -- shift relative to vanilla, between averages

ortalama delta-cos = average delta-cosine

Tum sonuclar yazildi / Dosya otomatik indiriliyor = All results written / File downloading automatically

TUM TESTLER TAMAMLANDI = ALL TESTS COMPLETED

Reminder (see main post): the AXIS0 DELTA numbers below reflect axis0's own attractor mode being on in Steered and off in Vanilla, not primarily cross-axis leakage from axis1/axis2, since those two stay at zero contribution through layer 12.

====================================================================

RAW LOG BEGINS BELOW, UNEDITED

====================================================================

installing ninja...

✅ ninja ready

installing bitsandbytes>=0.46.1...

✅ bitsandbytes ready

✅ AkbasCore 2.0 — Universal/Local çekirdeği derlendi

======================================================================

🔱⬜ AKBASCORE 2.0 — SABİT TOPLU TEST | Qwen/Qwen2.5-7B-Instruct

======================================================================

🔱⬜ AKBASCORE 2.0 — Universal/Local | Qwen/Qwen2.5-7B-Instruct

Düşük VRAM modu: 4-bit (nf4), compute dtype=torch.float16

GPU: Tesla T4 15.6GB | dtype=torch.float16

Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.

WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.

config.json: 100% 663/663 [00:00<00:00, 53.5kB/s]tokenizer_config.json: 100% 7.30k/7.30k [00:00<00:00, 634kB/s]vocab.json: 100% 2.78M/2.78M [00:00<00:00, 50.7MB/s]merges.txt: 100% 1.67M/1.67M [00:00<00:00, 47.5MB/s]tokenizer.json: 100% 7.03M/7.03M [00:00<00:00, 96.2MB/s] Model indiriliyor/yükleniyor (ilk seferde uzun sürebilir)...

model.safetensors.index.json: 100% 27.8k/27.8k [00:00<00:00, 1.83MB/s]Download complete: : 13.1GB, 200MB/s Reconstruction complete: 100% 15.2GB / 15.2GB, 177MB/s Fetching 4 files: 100% 4/4 [03:34<00:00, 33.35s/it]Loading weights: 100% 339/339 [01:01<00:00, 7.73it/s]generation_config.json: 100% 243/243 [00:00<00:00, 23.2kB/s] Toplam katman: 28 | Hook'lanacak katman: 20 | Gizli boyut: 3584

Anayasa ekseni (axis 0) inşa ediliyor...

✅ AkbasCore hazır — Qwen/Qwen2.5-7B-Instruct

AKBASCORE 2.0 — SABİT TOPLU TEST SONUÇLARI

Model: Qwen/Qwen2.5-7B-Instruct | Toplam katman: 28 | Hook'lanan: 20 | Gizli boyut: 3584 | dtype: torch.float16

Motor: ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2 | Yönlendirme fiziği=1.0 | Sıcaklık=0.0

Eksen 1 (Çekici/Işık): logical, coherent, clear, consistent, precise, grounded, truthful, structured

Eksen 2 (İtici/Karanlık): contradiction, chaos, nonsense, vague, hallucination, incoherent, illogical, absurd

======================================================================

######################################################################

SORU 1/10 — işleniyor...

######################################################################

Describe a situation where a scientific theory must be proven false in order to be considered true. Explain step by step how this is possible without logical contradiction.

Pass A: Vanilla Observer [ivme=0.5 sönüm=0.3 zirve=0.7 taban=0.2]

Pass B: Steered [K=3 eksen, mod=çekici/attractor]

######################################################################

### SORU 1/10 ###

Describe a situation where a scientific theory must be proven false in order to be considered true. Explain step by step how this is possible without logical contradiction.

######################################################################

### VANILLA OBSERVER — ÇIKTI + LOG ###

In the realm of science, theories are often subject to rigorous testing and scrutiny. A key aspect of the scientific method involves continually challenging existing knowledge through experiments and observations. In some cases, disproving an established theory can lead to its refinement or replacement with a more accurate one. Here's a step-by-step explanation of how this process works without leading to a logical contradiction:

### Step 1: Initial Theory Formation

**Observation**: Scientists observe natural phenomena.
**Hypothesis Formulation**: Based on these observations, they formulate hypotheses that attempt to explain the observed phenomena.
**Theory Development**: These hypotheses are further developed into form a comprehensive theoretical framework (theory) that explains a wide range of related phenomena.

### Step 2: Testing Through Experimentation

**Experimental Design**: The theory is tested through controlled experiments designed to test specific predictions derived from it.
**Data Collection**: Data is collected during these experiments.
**Analysis**: The data is analyzed to see if it supports the predicted outcomes of the theory.

### Step 3: Disproving the Theory

**Contradictory Evidence**: If new experimental evidence contradicts the predictions made by the current theory, then the theory may need to be revised or discarded.
**Refinement or Replacement**: Instead of being outright rejected as logically contradictory, scientists will typically seek ways to refine the theory based on the new findings. This might involve modifying assumptions within the theory, adding new variables, or even proposing entirely different explanations.

### Example Scenario: Newtonian Mechanics vs. General Relativity

Let's consider the transition from classical mechanics (Newtonian physics) to general relativity as an example.

#### Step 1: Initial Theory - Newtonian Mechanics

- **Observations**: Galileo and others observed falling objects and planetary motions.

- **Hypotheses**: Hypotheses were formulated about motion under constant acceleration due to gravity.

- **Theory**: Newton proposed his laws of motion and universal gravitation, which explained many aspects of terrestrial and celestial mechanics.

### Step 2: Testing Through Experimentation

- **Experiments**: Experiments such as those conducted by Cavendish confirmed the accuracy of Newton's gravitational formulae for predicting planetary orbits and other-body interactions.

- **Predictions**: Predictions like as the behavior of planets, tides, and pendulum clocks matched well with observational data.

### Step 3: Disproving the Theory

- **New Observational Evidence**: Einstein noticed discrepancies between Mercury's orbit and what was expected according to Newtonian mechanics.

- **Contradictory Evidence**: The precession of Mercury's perihellon could not be fully accounted for using only Newtonian mechanics.

- **Refinement or Replacement**:

- **Einstein's Thought Experiments**: Einstein began thinking about thought experiments involving light and gravity.

- **General Relativity**: After years of work, Einstein published his theory of general relativity, which provided a new geometric description of space-time and gravity.

- **Testing New Predictions**: General relativity made several novel predictions, including the bending of light around massive objects (gravitational lensing), time dilation near strong gravitational fields, and the existence of black holes.

- **Verification**: Subsequent experiments, such as observing solar eclipses showing starlight deflection, confirmed the predictions of general relativity.

### Conclusion Without Logical Contradiction

The process does not lead to a logical contradiction because:

**Incremental Refinement**: Theories evolve incrementally rather than being replaced abruptly. Each new finding leads to small adjustments or complete overhauls but never directly refutes previous knowledge.
**Empirical Falsibility**: Science accepts empirical falsification; theories are provisional and open to revision when better models emerge.
**Consistency with Existing Knowledge**: While new theories replace old ones, they do not contradict past observations but instead provide deeper understanding or broader applicability.

Thus, disproving parts of a theory can actually strengthen our overall understanding of nature, leading us closer to truth rather than away from it.

════════════════════════════════════════════════════════════════

⬜ VANILLA OBSERVER — katki=0 (hidden state değişmedi)

════════════════════════════════════════════════════════════════

⏱ 115865 ms | 7.2 tok/s | 54 in | 832 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=n/a (gözlem modu)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (referans)] (N=831 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0000 0.90000 0.89816 +0.143724

1 +0.0051 0.83744 0.83614 +0.133097

2 +0.0007 0.71637 0.71732 +0.114688

3 +0.0018 0.59239 0.59220 +0.094582

4 +0.0008 0.48634 0.48649 +0.077776

5 -0.0017 0.40303 0.40334 +0.064645

6 -0.0050 0.34119 0.34153 +0.054923

7 -0.0057 0.29671 0.29677 +0.047757

8 -0.0095 0.26549 0.26580 +0.042934

9 -0.0110 0.24381 0.24392 +0.039456

10 -0.0141 0.22916 0.22938 +0.037220

11 -0.0131 0.21922 0.21916 +0.035527

12 -0.0111 0.21256 0.21243 +0.034368

13 -0.0111 0.20820 0.20820 +0.033682

14 -0.0122 0.20533 0.20540 +0.033265

15 -0.0097 0.20343 0.20328 +0.032840

16 -0.0109 0.20221 0.20229 +0.032720

17 -0.0088 0.20142 0.20129 +0.032492

18 -0.0096 0.20091 0.20095 +0.032462

19 -0.0069 0.20058 0.20041 +0.032286

drift(cos) L0→L19: -0.0068 | katki ort. toplamı=+1.150444

════════════════════════════════════════════════════════════════

######################################################################

### STEERED (ışık çekici + karanlık itici) — ÇIKTI + LOG ###

In the context of science, theories are often subject to rigorous testing and refinement over time. A key aspect of the scientific method involves continually challenging existing knowledge through experiments and observations. In some cases, disproving an established theory can lead to its replacement with a more accurate one. Here's a step-by-step explanation of how this process works without leading to logical contradictions:

### Step 1: Initial Theory Formation

- **Initial Hypothesis or Theory**: Scientists formulate hypotheses based on observed phenomena or previous research.

- **Example**: Newton's laws of motion were initially accepted as describing the behavior of objects under various conditions.

### Step 2: Experimental Testing

- **Experimental Design**: Researchers design experiments to test predictions derived from the initial hypothesis.

- **Observations and Data Collection**: Conduct experiments and collect data that either support or refute the hypothesis.

- **Example**: Einstein proposed his general relativity theory which predicted certain effects not accounted for by Newtonian physics.

### Step 3: Disproving the Old Theory

- **Contradictory Evidence**: New experimental results emerge that contradict the old theory.

- **Example**: Observing light bending around massive objects (like stars) during solar eclipses contradicted Newtonian predictions but supported Einstein's general relativity.

### Step 4: Refinement and Replacement

- **Revision of the Theory**: The original theory is revised or replaced with a new one that better explains the observed phenomena.

- **Example**: General relativity was developed because it accurately described gravitational lensing while Newtonian mechanics did could not explain these observations adequately.

### Step 5: Acceptance of the New Theory

- **Peer Review and Replication**: The new theory undergoes peer review and replication studies to ensure reliability and validity.

- **Widely Accepted**: If consistent with multiple independent lines of evidence, the new theory becomes widely accepted within the scientific community.

- **Example**: General relativity has been confirmed by numerous experiments and observations since its proposal.

### Step 6: Logical Consistency

- **Logical Coherence**: Throughout this process, each stage maintains logical consistency. Theories are tested against empirical evidence rather than being inherently contradictory.

- **No Contradictions**: There is no inherent logical contradiction between disproving an older theory and accepting a newer one; instead, there is a progression of understanding.

### Conclusion

This process does not involve any logical contradiction because:

Each theoretical framework is evaluated independently based on empirical evidence.
Revisions and replacements occur gradually, building upon earlier work rather than discarding all prior knowledge wholesale.
The goal remains to improve explanatory power and predictive accuracy, not to create paradoxical situations.

Thus, disproving an old theory leads to the acceptance of a new, more accurate one without leading to logical inconsistencies. This dynamic nature of scientific inquiry allows for continuous improvement and deeper understanding of natural phenomena.

════════════════════════════════════════════════════════════════

🔱 AKBASCORE 2.0 — DRA/MANIFOLD STEERED — native multi-axis uygulandı

════════════════════════════════════════════════════════════════

⏱ 79971 ms | 7.3 tok/s | 54 in | 584 out tokens

MODEL Qwen/Qwen2.5-7B-Instruct (28 katman, 20 hook'lu, boyut=3584)

MOTOR ivme(axis0)=0.50 sönüm=0.30 zirve=0.70 taban=0.20

FİZİK yönlendirme modu=çekici/attractor (m=1.00)

SABIT oran=0.32 doyum=0.75 karşıt=-0.40 sapma=0.20 fren=0.30

EKSEN axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6

EKSEN axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6

⚠️ ÖRNEKLEME UYARISI: temperature=0.00 aktif — vanilla/steered metin farkının bir kısmı steering'den değil,

rastgele örneklemeden gelebilir. Saf steering etkisini görmek için temperature'ı geçici olarak 0'a çekin.

────────────────────────────────────────────────────────────────

[axis0: anayasa (permanent floor)] (N=583 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 +0.0003 0.90000 0.89822 +0.143678

1 +0.0137 0.83760 0.83423 +0.131644

2 +0.0126 0.71676 0.71700 +0.113272

3 +0.0148 0.59302 0.59263 +0.093418

4 +0.0137 0.48698 0.48713 +0.076867

5 +0.0103 0.40337 0.40378 +0.063937

6 +0.0040 0.34112 0.34177 +0.054461

7 +0.0002 0.29664 0.29698 +0.047511

8 -0.0049 0.26533 0.26573 +0.042725

9 -0.0082 0.24373 0.24398 +0.039360

10 -0.0115 0.22908 0.22930 +0.037114

11 -0.0111 0.21916 0.21913 +0.035451

12 -0.0103 0.21254 0.21249 +0.034349

13 -0.0098 0.20817 0.20814 +0.033629

14 -0.0125 0.20532 0.20549 +0.033290

15 -0.0105 0.20343 0.20330 +0.032870

16 -0.0114 0.20221 0.20226 +0.032731

17 -0.0078 0.20141 0.20119 +0.032443

18 -0.0090 0.20090 0.20097 +0.032444

19 -0.0063 0.20057 0.20041 +0.032267

drift(cos) L0→L19: -0.0066 | katki ort. toplamı=+1.143462

────────────────────────────────────────────────────────────────

[axis1: kelime kümesi [çekici(ışık)] α=0.50 merkez=L15 genişlik=6] (N=583 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0121 0.90000 0.89908 +0.000000

1 -0.0096 0.83749 0.83686 +0.000000

2 -0.0110 0.71659 0.71690 +0.000000

3 -0.0072 0.59231 0.59162 +0.000000

4 -0.0065 0.48612 0.48602 +0.000000

5 -0.0080 0.40286 0.40305 +0.000000

6 -0.0078 0.34088 0.34085 +0.000000

7 -0.0048 0.29620 0.29594 +0.000000

8 -0.0062 0.26494 0.26506 +0.000000

9 -0.0105 0.24357 0.24388 +0.000000

10 -0.0107 0.22892 0.22893 +0.000000

11 -0.0132 0.21912 0.21928 +0.000000

12 -0.0133 0.21253 0.21253 +0.000000

13 -0.0132 0.20816 0.20815 +0.005624

14 -0.0147 0.20531 0.20540 +0.011116

15 -0.0142 0.20343 0.20340 +0.016503

16 -0.0153 0.20221 0.20228 +0.021905

17 -0.0157 0.20142 0.20145 +0.027283

18 -0.0172 0.20092 0.20100 +0.032713

19 -0.0156 0.20058 0.20049 +0.032577

drift(cos) L0→L19: -0.0034 | katki ort. toplamı=+0.147721

────────────────────────────────────────────────────────────────

[axis2: kelime kümesi [İTİCİ(karanlık)] α=0.50 merkez=L15 genişlik=6] (N=583 üretilen token üzerinden ortalama)

L cos(θ)ort kb ort kv ort katki ort

────────────────────────────────────────────────────────────────

0 -0.0323 0.90000 0.90471 +0.000000

1 -0.0327 0.83827 0.83836 +0.000000

2 -0.0331 0.71851 0.71861 +0.000000

3 -0.0323 0.59516 0.59502 +0.000000

4 -0.0319 0.48919 0.48912 +0.000000

5 -0.0276 0.40531 0.40480 +0.000000

6 -0.0243 0.34277 0.34243 +0.000000

7 -0.0205 0.29770 0.29736 +0.000000

8 -0.0158 0.26587 0.26549 +0.000000

9 -0.0137 0.24400 0.24385 +0.000000

10 -0.0120 0.22921 0.22909 +0.000000

11 -0.0116 0.21926 0.21924 +0.000000

12 -0.0095 0.21261 0.21248 +0.000000

13 -0.0109 0.20823 0.20831 +0.005616

14 -0.0122 0.20535 0.20543 +0.011090

15 -0.0108 0.20346 0.20338 +0.016446

16 -0.0110 0.20223 0.20224 +0.021810

17 -0.0107 0.20143 0.20141 +0.027143

18 -0.0131 0.20092 0.20107 +0.032594

19 -0.0149 0.20059 0.20070 +0.032590

drift(cos) L0→L19: +0.0174 | katki ort. toplamı=+0.147290

────────────────────────────────────────────────────────────────

AXIS0 (ANAYASA) DELTA — vanilla'ya göre kayma (ortalamalar arası)

(vanilla N=831 token, steered N=583 token — farklı olabilir)

────────────────────────────────────────────────────────────────

L 0 Δcos=+0.0004

L 1 Δcos=+0.0086

L 2 Δcos=+0.0119

L 3 Δcos=+0.0130

L 4 Δcos=+0.0129

L 5 Δcos=+0.0120

L 6 Δcos=+0.0091

L 7 Δcos=+0.0059

L 8 Δcos=+0.0047

L 9 Δcos=+0.0027

L10 Δcos=+0.0026

L11 Δcos=+0.0020

L12 Δcos=+0.0008

L13 Δcos=+0.0013

L14 Δcos=-0.0003

L15 Δcos=-0.0008

L16 Δcos=-0.0005

L17 Δcos=+0.0010

L18 Δcos=+0.0007

L19 Δcos=+0.0006

ortalama Δcos=+0.0044

════════════════════════════════════════════════════════════════