r/LargeLanguageModels

LLMs are impressive compared with the experiments we had before. But with experience we are seeing their limitations better (many are discussed in depth in this subreddit), and also their problems (such as the increased need for power generation and the indirect need for water).

Is there a sub for discussing what can be designed to improve on LLMs? We know there must be something better for the simple reason that the human brain overlaps with LLM functionality for only 20 watts of power in only about 1300 cubic centimeters of space.

I’ve been testing a simple idea I call Comparative Feedback Control (CFC).

The question is not “can the model answer correctly?” but:

does the model still have enough evidence to legitimately close the decision?

I ran a series of small behavioral tests around things like:

• missing evidence being treated as negative evidence,
• old certificates being reused after the requirements changed,
• a closed process being mistaken for a resolved claim,
• loss of provenance causing a status to be transferred to the wrong claim.

One pattern I found was interesting: models sometimes correctly identified an uncertainty at first, but under pressure to “finish the task” they invented an extra rule and closed the decision anyway.

With an explicit CFC-style control rule, several of those failures disappeared in the tested runs.

For example, in one cross-session provenance experiment:

• baseline: 1 of 3 runs transferred an unsupported REJECTED status to a claim,
• with the CFC provenance rule: 3 of 3 runs kept the claim unresolved.

This is not a benchmark and not proof that CFC generally improves LLM reliability. The samples are small and exploratory. I’m publishing the failures as well as the passes because I’m mainly interested in whether the failure mechanism itself is real and reproducible.

I’ve put the consolidated report, result table and evidence package on Zenodo:

https://zenodo.org/records/21966517

I’d especially appreciate criticism of the experimental design or suggestions for adversarial cases that could break the control rule.

I’ve been testing a simple idea I call Comparative Feedback Control (CFC).

The question is not “can the model answer correctly?” but:

does the model still have enough evidence to legitimately close the decision?

I ran a series of small behavioral tests around things like:

• missing evidence being treated as negative evidence,
• old certificates being reused after the requirements changed,
• a closed process being mistaken for a resolved claim,
• loss of provenance causing a status to be transferred to the wrong claim.

One pattern I found was interesting: models sometimes correctly identified an uncertainty at first, but under pressure to “finish the task” they invented an extra rule and closed the decision anyway.

With an explicit CFC-style control rule, several of those failures disappeared in the tested runs.

For example, in one cross-session provenance experiment:

• baseline: 1 of 3 runs transferred an unsupported REJECTED status to a claim,
• with the CFC provenance rule: 3 of 3 runs kept the claim unresolved.

This is not a benchmark and not proof that CFC generally improves LLM reliability. The samples are small and exploratory. I’m publishing the failures as well as the passes because I’m mainly interested in whether the failure mechanism itself is real and reproducible.

I’ve put the consolidated report, result table and evidence package on Zenodo:

https://zenodo.org/records/21966517

I’d especially appreciate criticism of the experimental design or suggestions for adversarial cases that could break the control rule.

I’m not an AI researcher — I started testing this because I kept noticing a simple problem in conversations with AI.

A model can correctly say that an important piece of information is missing, but a few messages later it sometimes starts reasoning as if that information had somehow become known.

I built a small series of tests around that problem using Gemini, ChatGPT and Claude.

The rule started very simply: if information required for a decision is missing and cannot be obtained, identify the gap and ask the human whether to continue.

Then I tried to break it.

Early versions failed in some interesting ways. Models sometimes:

• invented assumptions after being told “just choose,”
• changed the decision criterion,
• treated two unknown possibilities as 50/50,
• imported outside base rates,
• or became so cautious that they refused legitimate hypothetical reasoning.

After several iterations I ended up with v4, based around one basic distinction:

unknown information should stay unknown, a hypothetical assumption should stay hypothetical, and a conclusion based on it should stay conditional.

I then tested whether that distinction survived several turns of conversation, model-generated hypothetical examples, compressed manager-facing documents, and structured outputs.

This is only an exploratory pilot — not a benchmark. Most cases were single runs and exact model versions weren’t systematically controlled.

I’ve published the full report openly here:

Zenodo:
https://zenodo.org/records/21937196

Hugging Face:
https://huggingface.co/datasets/krzysztofsliwka/missing-information-control-llm-pilot

I’d be interested in criticism, especially examples that could break the final rule. Finding a failure would actually be more useful to me than another successful test.

I have seen videos about the transformer architecture etc., and I get that large language models generate responses based on some statistical likelihood of words and terms. However, I still don't get how they can completely make up facts and even references.

Why can't they state facts that they have come across in their training as they are? What is it, either from a mathematical standpoint or from an architectural standpoint of large language models that causes them to hallucinate?

An LLM doesn't store a dictionary of answers somewhere inside its brain.

It learns statistical patterns from enormous amounts of data.

Give it:

“The capital of France is…”

The model predicts what tokens are likely to come next.

Do this at massive scale, and surprisingly complex capabilities emerge.

How does predicting the next token turn into reasoning?

We all know that large language models can statistically predict what should be the next token, but how do we go from there to mathematical proofs? How is intelligence implemented?

I am a senior software engineer at a big fintech, I am using AI for a lot of time now.
I have been in interviews and I have taken a lot of interviews and more than ever AI is a big part.
especially LLMs.

I have been asked about creating a system that would ingest 10K events per minutes and design a system where you would get intents for each of each events with least number of inference calls.

I have been asked to make an agent which automates some workflow.
I have been asked to create a rag and honestly it's not that hard to answer.

and more or less this is what I ask too but I have stopped now.

Ultimately these are my insights

Usefulness of LLMs (NOT AI):
- Coding partner for good programmers, help them being 5X
- Pin pointed/similarity search (replacing stack overflow or recognising images/faces)
- Some agentic phone callers, (okayish performance)
- summarisation
That's it and do you think it's a 5 trillion dollar problem??

• The Trash part of LLMs (NOT AI): - It lacks trust and that's it the biggest disadvantage, wherever your business needs high trust, you can't replace this with AI, and the worst this, there is no mathematical way to actually evaluate your LLM response. (Engine replaced horses because engine works, you can trust them, they follow thermodynamics and mechanics) (I have this rule if physics is out of the equation and you are taking decisions only based on mathematics, work where human emotions or automated low stake computing are because only then you can get some results)
• The toughest to crack roles hiring engineers way more bullishly than ever. - Open claw, your mac minis are worthless now and so much for other tools.
• - No one know the actual case of AI in their org, teams are forced to use AI in their system, paying massive bills for good to have features. For the entire history of software, it been created to make people push lesser buttons, now somehow you think that people would like to type?? see the contradiction?
• Plastic feeling, whenever you are on a platform and you hit the issue, and you see AI addressing your problem, your first response is to connect me with human representative, you know you can't express to a machine, it lacks massive trust and it feels way less premium. - It's fucking expensive

None of the core business of food delivery, taxi, operating systems, hospitality, cloud (other then hosting llms) have been revolutionised and the only change is you see bugs in some of the apps now.

I do believe the AI can be fundamentally great in healthcare discovery, protein discovery and automating calculations but relying on agents to transform your business without trust, good luck with that.

It not a 5 Trillion problem. This is going to go down.
I am more than happy if you can transform my view and I am open to it but let's debate.

I've been noticing a pattern in how people use LLMs, and I'm curious if you've seen it elsewhere.

Here is the scenario: 

1. An LLM workflow has an intermediate artifact (e.g. debating between different personas, a chain-of-thought trace, a planning document, etc. 

2. That artifact is useful within the LLM pipeline and it improves the final output (better code, more persuasive essay, more accurate diagnosis). 

3. A human looks at that artifact and thinks: "Well, if it helps the LLM, it should help me too." 

4. So the artifact may get repurposed as a human-facing tool something to read, review, or edit (e.g. LLM reasoning traces shown to clinicians as "interpretable" decision support.).

The assumption that "what works for the model will work for me" seems to be at play. 

Curious to hear your thoughts and experiences, or more in-depth research related to this scenario. 

When an AI answer is weak, the first reaction is often to switch models. Sometimes that helps, but it doesn’t solve every type of failure.

I’ve started separating two different problems:

Model failure: the necessary evidence is available, but the model interprets it poorly.

Context failure: the necessary evidence was never provided in the first place.

For example, comparing five YouTube videos requires more than one prompt. The model may need the videos or transcripts, audience comments, the relevant time period, and a clear definition of what should be compared.

Before starting an analysis, I now ask:
What source material is actually necessary?
What relationships should the model look for?
What decision should the answer support?
Model choice still matters. But switching models cannot recover information that none of them received.

How do you distinguish a model problem from a context problem in your own workflow?
u organize it manually, use RAG, or rely on another workflow?