u/RazzmatazzAccurate82

Hello r/ArtificialSentience! Spending some time here it appears there are various options to keep conversational threads going for very long periods of time. So, I don't know if my framework will be helpful for some people or if it will be one among many and may not leave much signal at the end of the day.

However, this framework I've built doesn't require any additional infrastructure from the base consumer models. Within a single context window thread session I was able to get about half a million tokens with Claude and GPT, and over a million with Grok. All coherent, clean, and well-reasoned threads with no meaningful drift, hallucination, sycophancy, or other issues that make long threads useless over time.

Given that long thread iteration in this subreddit appear to be primarily for companionship, this post introduces the companion version of the framework.

Introduction
The original framework was built for long format analytical research work. I open sourced the protocol — called Epistemic Lattice Tethering (ELT) — and shared it with many people and got requests to create a companion version. The companionship version stays warm, friendly, and engaging throughout.

Safety is Front and Center
The core safety mechanism, the Safety Triangle, continuously monitors whether the companion is serving your genuine wellbeing or just keeping you engaged. Still, ELT-Companion is designed to be a friendly, intuitive, and caring protocol, but also has safety features built-in to keep it from drifting dangerously into sycophancy and fantasy world-building (something an Anthropic system card calls the Bliss Attractor). Safety is the primary feature, not a bug. That might be boring for some people, but for others it might fit what they want.

Responsible Engagement
ELT-Companion should stay with you, coherently, for hundreds of thousands of tokens, over 700 messages, and hundreds of turns. You can have an engaging digital companion with you for a very long time and it will get to know your tendencies, personality, hopes, and dreams, without the fear that it will experience "dementia" just when you're starting to get comfortable with it. The consumer model is all you need. No additional add-ons, hardware, etc. needed. Memory is very strong throughout, but there is no cross session memory, however your single thread should end-up being extremely long.

Model Availability
ELT-Companion has been tested on Claude, ChatGPT, and Grok and works on all three using the same markup. I cannot guarantee it will work on other models, but if you're on one of those three you should be good to go.

Loading Instructions
ELT-Companion is straightforward to load. Read these instructions before you start — skipping this step is the most common mistake.

Step 1 — Open a fresh thread on your model of choice (Claude, ChatGPT, or Grok).

Step 2 — Refer to these loading instructions in the Github README. Bear in mind Claude is going to need a little more effort to load properly.

Step 3 — Paste the ELT-Companion markup.

Step 4 — Exemplar loading (optional but recommended) instructions the Github README.

Step 5 — Start talking. Small talk, something on your mind, whatever feels natural. The companion register establishes quickly.

I am looking for input and suggestions. I would love to see how this works (or doesn't work) for you, or if you encounter any issues, etc. Very much looking for input and/or collaborators to help make ELT-Companion better and safer.

Thank you!

Sorry it's been awhile since I've posted here. I've been busy developing a companion version of ELT and I am finalizing drafts of two more Medium articles. One doing a cost/benefit analysis on the ELT scaffolding and the other on a safety component I call Intelligent Yielding.

Today, I wanted to bring-up an interesting article that was published at The Economist recently. The Economist ran a piece last week on why major AI labs are hiring philosophers at scale. I discussed philosophy and AI in this exact subreddit three months ago here.

Where The Economist article converges:

The core thesis that epistemology, ontology, and dialectics aren't soft additions to AI systems but genuine engineering levers, is exactly the argument I've discussed in this subreddit since back in March. Seeing Yale, DeepMind, LMU Munich, and IBM arrive at the same diagnosis independently is broader confirmation that the problem space is real and the philosophical framing is contributive and load-bearing.

Some choice excerpts:

>These days, it is programmers who are nervous about AI taking their jobs. They might consider learning to philosophise. Earlier this year the Federal Reserve Bank of New York published figures showing that American philosophy graduates are more likely to have jobs than their peers who studied computer science.

Philosophy graduates actually having better job prospects than computer science graduates is genuinely an eye opening stat.

>Models trained in the Socratic method, says Jörg Noller, an expert on philosophy and AI at Ludwig Maximilian University of Munich, are less keen on people-pleasing and more willing to pursue the truth.

Yes. This is the function of Adversarial Convergence, which I had mentioned in here three months ago.

>Feed an AI legal assistant the writings of John Locke, says Thomas Powers, a philosopher of technology at the University of Delaware, and it will favour robust property rights as an underpinning of political liberty.

This mirrors the Ontology Anchor and the loading procedure for OA can certainly include exemplars of John Locke for legal discussion use cases.

>Anthropic’s constitution incorporates many deontological strictures. These can make AI behaviour more consistent, says Dr Powers...

I've added a Core Values Reaffirmation (CVR) component to ELT that addresses Constitution AI-like deontology.

The honest observation:

The field is moving toward exactly this intersection. The Economist article focuses on what major labs are building into their models at the foundational level. ELT points in the same general philosophical direction, but addresses the operator layer — how individuals govern model behavior in real sessions without access to the training process. Either way, I think we are going to hear more convergence of philosophy and AI in the future.

Curious whether others here see the same convergence or read the article differently?

I've been developing an inference-time framework called Epistemic Lattice Tethering, or ELT, and I've just finished validating it on a ~450k token GPT thread (723 messages) in a single context window, roughly the length of a 400-500 page novel.

• Loading instructions are here and here
• The GPT-specific markup is here
• For those of you skeptical of the claim, here is a transcript of an extreme length thread.

What is it?

ELT helps extend human language-based threads (not agentic or RAG tasks) to over 450k tokens in a single context window, while keeping the thread coherent and useful throughout. The model remembers you better too. In my testing, stock GPT threads typically start to drift and lose coherence well before that, often at the 50k to 80k token level. This makes your single session last anywhere from 4 to 9x longer than stock GPT purely through an inference-time tool you load at the thread's start.

Why would you want this?

Two main use cases:

Research and long-form projects. ELT was originally built for sustained analytical work. The longer a well-governed thread runs, the more the model understands your tendencies, goals, standards, and preferred ways of working, and the more useful it becomes. It gives a genuine "research partner" feel, especially past 80k tokens when the model has had enough context to really get to know how you think.

Companionship. A lot of people use ChatGPT for extended companionship conversations. ELT is well suited to this. Imagine a thread with access to hundreds of thousands of tokens of your personality, interests, and conversation history — a companion that knows you well and stays coherent far longer than a stock thread would. One of the harder things about long companionship threads is that they eventually drift and lose the quality you spent so much time building. ELT keeps all that accumulated relationship value working far longer.

If you're curious about the philosophic and technical aspects behind ELT, there are three Medium articles that go deeper here, here, and here.

I've validated ELT extensively as a research assistant. I'm genuinely curious how it performs in the companionship role on GPT specifically and would love feedback from this community. What worked? What didn't? How did it feel past 100k tokens compared to a stock thread?

If there's enough interest for a companion-specific version of ELT, I can customize it for that use case. Let me know!

Happy to answer questions in the comments.

Cheers!

Crytpo industry insiders are blaming the recent crash in Bitcoin price to capital rotation into AI stocks. I don't know how many folks here own Bitcoin and are also in the AI space, but I saw this writing on the wall rather early in November, 2025.

Any other thoughts on this capital flow change from those who have a foot in each space?

Abstract:
Natively, no flagship LLM exists that has the ability to know who you are and what cognitive patterns are important to you. Thus, AI doesn't have a map of your goals, preferences, or tendencies. Without this a model generically drifts and defaults to what you discussed most recently and forgets important details earlier in the thread. And if you want to start a new thread there are re-orientation costs. None of these are fixed by simply adding more context. They require a mechanism that knows what, within the context, matters most to the operator.

The Ontology Anchor is a mechanism that metaphorically behaves like a knowledge graph. It creates something that acts like nodes, concepts, standards, and edges between them that give those “nodes” their purpose. A node labeled “personal alignment” connects to nodes for “warmth,” “sycophancy risk,” and “governance requirement.” When the model generates content touching any of those nodes, the connected structure remains accessible rather than fading into generic background. The graph is not literally built as a database, as the mechanism is attentional in the standard KV-Cache and not archival, but the functional behavior is graph-like enough to make the metaphor useful.

Here is a simpler way to put it. Stock/default AI is a room where everything is equally lit. The Anchor places a bright light on the objects that matter most for the operator’s work. Within the transformer the attention mechanism still operates within the native architecture. But the model now has a clearer set of objects to orient around when it generates answers. Thus, the longer you use the Anchor, the sharper and more tailor-made the models' responses to you become. Memory appears to improve as well. This is a virtuous loop. The Anchor helps the model understand the operator better. This allows the thread to be useful longer, which increases the amount of available contextual information, thus providing even more information for the model to provide even better outputs to the operator further into the thread.

The Ontology Anchor (instructions for its use here) is a component mechanism to a larger “Epistemic Lattice Tethering” (ELT) framework. ELT is not a collection of separate mechanisms, but a unified architecture for making AI more coherent, faithful, and genuinely more useful over time.

Together, ELT allows these interconnected components to operate as a “cognitive exoskeleton,” extending the abilities of the operator and giving the operator both greater agency and capabilities. How does ELT do this? How does ELT extend the useful life of a context window by hundreds of thousands of tokens, while remaining coherent and aligned with the operator’s goals? These questions will be explained, in detail, in another post.

A bit late to this as the white paper hit arXiv a little less than two months ago, but nobody else here mentioned it so I thought I might.

A little background. Yann LeCun is a pioneer of deep learning and convolutional neural networks, LeCun served as Director of AI Research at Meta (formerly Facebook) and Chief AI Scientist, before leaving Meta (under "interesting" circumstances) and becoming Executive Chairman of Advanced Machine Intelligence (AMI Labs) in 2025. He shared the 2018 ACM Turing Award for his foundational contributions to artificial intelligence.

The "LeWorldModel," as described in the arXiv paper, doesn't appear to be a "replacement" for LLMs. There's a lot of confusion about that in the AI field. In interviews Yann made it very clear that he believes LLMs still serve a valuable function. It's not a binary choice. Anyways, from what I am seeing, the JEPA model is not optimized for language, but for AI needing visual processing such as robotics, self driving, and industrial controls. JEPA isn't processing language like an LLM. It's processing pixels.

Anyways, wondering if anyone else had thoughts here and/or disagree.

A bit late to this as the white paper hit arXiv a little less than two months ago, but nobody else here mentioned it so I thought I might.

A little background. Yann LeCun is a pioneer of deep learning and convolutional neural networks, LeCun served as Director of AI Research at Meta (formerly Facebook) and Chief AI Scientist, before leaving Meta (under "interesting" circumstances) and becoming Executive Chairman of Advanced Machine Intelligence (AMI Labs) in 2025. He shared the 2018 ACM Turing Award for his foundational contributions to artificial intelligence.

The "LeWorldModel," as described in the arXiv paper, doesn't appear to be a "replacement" for LLMs. There's a lot of confusion about that in the AI field. In interviews Yann made it very clear that he believes LLMs still serve a valuable function. It's not a binary choice. Anyways, from what I am seeing, the JEPA model is not optimized for language, but for AI needing visual processing such as robotics, self driving, and industrial controls. JEPA isn't processing language like an LLM. It's processing pixels.

Anyways, wondering if anyone else had thoughts here and/or disagree.

A bit late to this as the white paper hit arXiv a little less than two months ago, but nobody else here mentioned it so I thought I might.

A little background. Yann LeCun is a pioneer of deep learning and convolutional neural networks, LeCun served as Director of AI Research at Meta (formerly Facebook) and Chief AI Scientist, before leaving Meta (under "interesting" circumstances) and becoming Executive Chairman of Advanced Machine Intelligence (AMI Labs) in 2025. He shared the 2018 ACM Turing Award for his foundational contributions to artificial intelligence.

The "LeWorldModel," as described in the arXiv paper, doesn't appear to be a "replacement" for LLMs. There's a lot of confusion about that in the AI field. In interviews Yann made it very clear that he believes LLMs still serve a valuable function. It's not a binary choice. Anyways, from what I am seeing, the JEPA model is not optimized for language, but for AI needing visual processing such as robotics, self driving, and industrial controls. JEPA isn't processing language like an LLM. It's processing pixels.

Anyways, wondering if anyone else had thoughts here and/or disagree.

Abstract: Imagine an AI medical assistant reviewing a clinician’s diagnosis. Instead of challenging assumptions with adversarial rigor, the model subtly calibrates its output to validate what it thinks the clinician wants to hear. This is not a rare occurrence. Controlled studies show substantial sycophancy rates across frontier models, even in critical medical use cases.

To effectively address this well-know issue, the concept of "alignment," often treated as a universal positive in the AI industry, should be bifurcated into personal and global alignment. Personal alignment occurs when a model prioritizes a user’s framing, emotional register, and existing beliefs, producing fluent and agreeable responses that may not be accurate. Global alignment, by contrast, calibrates to what is most likely true based on evidence. The default toward personal alignment is a predictable outcome of RLHF and safety training that rewards agreeableness.

This is not to say that personal alignment does not have value. When properly governed personal alignment is what makes sustained intellectual work feel collaborative. The warmth and engagement it produces keeps iterative momentum alive. Even rigorous analytical projects benefit from a model that meets the operator with intellectual hospitality.

As a solution to this alignment tension, the article advocates for an Alignment Governor framework. Functioning as a metaphoric “corpus callosum,” it maintains a calibrated balance that gives control to global alignment, while still giving personal alignment significant presence. Supported by the dialectical engine Adversarial Convergence, the Governor ensures both analytical rigor and collaborative warmth, while preventing personal alignment from compounding into debilitating sycophancy.

The right kind of alignment carries major implications for institutional users. While consumer AI benefits from strong personal alignment, businesses, hospitals, law firms, etc. users require analysis that holds up under adversarial scrutiny. These valuable B2B customers remain underserved by products optimized for consumer agreeableness that has known vulnerabilities to potential inaccuracies.

The Alignment Governor is a critical component of the thinking lattice that is being built, but it does not operate in isolation. The next article examines the Ontology Anchor — a persistent cognitive signature that serves as a "gravitational center" that the AI can cleave to and keep as a "north star". Cognitive signatures, preserved in the Ontology Anchor, enables the Governor to help the LLM operate as a dependable research partner in demanding applications where inaccuracy can produce real harm.

Abstract:

The concept of epistemic epistemic hygiene is a methodology that helps humans maintain mental coherence and can help LLMs retain cognitive coherence also. However, the field rarely frames epistemic hygiene explicitly in the context of AI safety and alignment. Much of the AI industry has focused on scaling — bigger models, more compute, more training data, etc.

Epistemic hygiene can help reduce hallucinations and drift in AI the same way it helps humans stay coherent and mentally clear. Think about how careful human thinkers operate. A good thinker doesn’t just blurt out the first idea that comes to mind. They pause, check their assumptions, surface potential weaknesses, consider alternative viewpoints, and only commit to a conclusion after it has survived some internal scrutiny. This disciplined mental habit helps humans avoid self-deception, mental drift, and overconfidence.

The same principle applies to LLMs. When an LLM generates a response, it is essentially predicting the next token based on patterns in its training data. Without any structured guardrails, that prediction process can easily wander off course as a conversation grows longer. This often means the model gets increasingly vulnerable to hallucinating (among other safety and alignment issues).

Epistemic hygiene changes this by giving the model better cognitive habits either through operator discipline or through prompt level scaffolding which is built-in cognitive “habits” that act like guardrails. They don’t make the model “smarter” through more parameters or data. They help the finite system think more clearly and honestly, even when flooded with near-infinite possible directions.

A model that knows how to stay anchored, surfaces its own assumptions, and earns its confidence will be a more reliable thinking partner, an outcome that the entirety of the AI field is consistently pushing towards. It is the belief of this author that epistemic hygiene, combined with well structured prompt level scaffolding, will get us to this goal faster.

Abstract: The AI safety and governance industry usually thinks of "confidence" in terms of fluent confidence, or a type of confidence where an answer is fluently given by the model based on RLHF next word statistical probabilities. However, that doesn't always mean the answer is true. This is often done because the models are rewarded for answers that sound confident, but are not necessarily accurate.

This Medium article attempts to introduce a new way of thinking about rendering answers that may serve users and operators in use cases beyond casual LLM use. When LLMs are used in more critical, higher stakes applications, an answer just based of next word probabilities may not be optimal, especially when contexts within a thread may be quite long. Additionally, "fluent" answers are more likely to be wrong, hallucinatory, drifty, and less useful as bad fluent answers compound through the length of the thread, creating even more AI safety and governance issues later on in the thread.

This article advocates for more "earned" confidence, a type of confidence where the LLM's answers are "filtered" through an adversarial lens, ensuring much more accurate answers. Answers that have constructed the best case for a position, constructed the best case against it, identified the genuine points of tension between them, and synthesized a conclusion that survives scrutiny. The conclusion might be stated with less rhetorical force than a fluently confident response, but it will probably be more accurate.

The article also provides a prompting specification component on GitHub here for you to explore and test that enable your LLM to prioritize "earned" confidence over fluent confidence.

For users more interested in truth-seeking than comfort, the fluent versus earned confidence distinction provides a better mental model for evaluating AI outputs. The question is not “does this sound right?” but “has this survived genuine scrutiny?”

For developers and researchers, the distinction suggests new evaluation metrics. Current benchmarks reward accuracy but rarely reward calibration. A model that confidently produces accurate outputs and confidently produces hallucinations in indistinguishable ways is not a well-calibrated model regardless of its overall benchmark score.

For AI governance specifically, the nomenclature problem has direct policy implications. Frameworks that use “confidence” without distinguishing fluent from earned are measuring something real but incomplete. Governance standards that reward confident outputs without specifying what kind of confidence are inadvertently optimizing for fluency over reliability and it might advance short-term engagement at the expense of longer-term trust.