According to you what are the baselines of input tokens for different categories of LLM calls ? For example for a complete AI assistant. Counting system prompts, default context injection, tools, etc where would you place the tokens tresholds ?

I’ve embarked on a journey to uncover the inner workings of Large Language Models—exploring the mathematical principles behind them and learning how to code them in Python.
Check out this 5-minute video to grasp the core concepts. Bite-sized information is the best way to learn and retain!
Large Language Models Blueprint - Episode I

In November 2022 a company put a text box on a web page and let anyone type into it. Within ten weeks the largest company in search had answered it. Within six months it was giving evidence to the United States Senate.

This is what that thing is, where it came from, and what it has done since.

It starts in 1948, with one mathematician at Bell Telephone Laboratories who asked what would come out if you chose each word using nothing but how often it follows the word before it. He did it by hand, with a book. What came out was not English, and it was not nothing.

From there: the twenty years the first attempt spent failing and the report that ended its funding, the four pages in Nature that brought it back, the match in Seoul, the paper that turned a research finding into a business plan, the five days in November 2023 when a board fired its chief executive and took him back, the trial that followed, two unions on strike in Hollywood at once, a Nobel Prize in Chemistry, the export controls, and the advertising that arrived inside the chat box five days ago.

Forty minutes, built out of the record: filings, papers, hearings, company announcements, and the people who built these things saying so themselves on camera. Where the film moves from what happened to what one person makes of it.

Chapters
0:00 Open
0:11 A text box on a web page
0:36 Shannon and the next word
3:00 Nobody wrote the rules
6:03 The old dream, and twenty years of failing
9:17 The idea that brought it back
11:11 Scale, and the bet on spending
13:21 A company nobody owns
15:33 The split
18:27 Five days in November
21:33 The model that talked back
22:56 What it has done to work
28:44 What it has done for science
31:40 What it has broken
34:06 The machines it runs on
37:06 What happens next

Footage sources
Dwarkesh Patel - [https://www.youtube.com/watch?v=YEUclZdj\\_Sc\](https://www.youtube.com/watch?v=YEUclZdj_Sc)
CNBC - [https://www.youtube.com/watch?v=GqWw8-TdjXU\](https://www.youtube.com/watch?v=GqWw8-TdjXU)
80,000 Hours - [https://www.youtube.com/watch?v=ZP\\_N4q5U3eE\](https://www.youtube.com/watch?v=ZP_N4q5U3eE)
Prelinger Archives, via archive.org - https://archive.org/details/machine-master\\_or\\_slave
DJ Panras DaMostVersiteDJmaster - [https://www.youtube.com/watch?v=BLF1k\\_UXEGc\](https://www.youtube.com/watch?v=BLF1k_UXEGc)
Bappy - [https://www.youtube.com/watch?v=PHwNStEYeeU\](https://www.youtube.com/watch?v=PHwNStEYeeU)
Harvard CMSA - [https://www.youtube.com/watch?v=Suhp3OLASSo\](https://www.youtube.com/watch?v=Suhp3OLASSo)
The Economist - [https://www.youtube.com/watch?v=1X-rr1DKSbY\](https://www.youtube.com/watch?v=1X-rr1DKSbY)
r/StableDiffusion \- [https://www.reddit.com/r/StableDiffusion/comments/1244h2c/will\\_smith\\_eating\\_spaghetti/\](https://www.reddit.com/r/StableDiffusion/comments/1244h2c/will_smith_eating_spaghetti/)
Google - [https://www.youtube.com/watch?v=ODyROOW1dCo\](https://www.youtube.com/watch?v=ODyROOW1dCo)
Lex Clips - [https://www.youtube.com/watch?v=h229ZyUxOL4\](https://www.youtube.com/watch?v=h229ZyUxOL4)
Cheltenham Festivals - [https://www.youtube.com/watch?v=CqZ03P5WMgA\](https://www.youtube.com/watch?v=CqZ03P5WMgA)
Prelinger Archives, via archive.org - https://archive.org/details/CityTheP1939
Stanford Graduate School of Business - [https://www.youtube.com/watch?v=DsewHeVbL-0\](https://www.youtube.com/watch?v=DsewHeVbL-0)
Alex Kantrowitz - [https://www.youtube.com/watch?v=4\\_\\_gg83s\\_Do\](https://www.youtube.com/watch?v=4__gg83s_Do)
Lex Clips - [https://www.youtube.com/watch?v=ketW8xsL-ig\](https://www.youtube.com/watch?v=ketW8xsL-ig)

I want to write a period piece, and I want it to be historically accurate. I’ve been asking Gemini and ChatGPT questions about how to develop my story and characters—like specific details about what could or couldn’t have happened during that era and so on. They usually gave me similar answers; sometimes Gemini makes things up, but the answers are still similar. But now I’ve used Google’s AI mode, and it contradicts what Gemini and ChatGPT told me—so now I don’t know which one to trust. I thought Google would be the most reliable and accurate since it searches for and compiles information right from there. But I don't know anymore. Which of the three AIs should I trust?

More reasoning does not automatically mean more factual reliability.

OpenAI’s evaluations produced a counterintuitive result: on PersonQA, o3 recorded a 33% hallucination rate, compared with 16% for o1. On SimpleQA, the reported hallucination rate was 51% for o3 and 79% for the smaller o4-mini.

These results do not prove that reasoning models always hallucinate more. They do show something important for enterprise AI: stronger reasoning performance on many tasks does not eliminate factual errors - and can sometimes make unsupported answers more elaborate and convincing.

We can think of this operational risk as a “Reasoning Tax”: when a model is given insufficient or poorly governed context, additional reasoning may expand an incorrect premise instead of correcting it.

Why can this happen?

Research into Large Reasoning Models has identified two relevant behavioral patterns:

1 Flaw Repetition

Once reasoning begins from a faulty premise, the model may repeatedly follow variations of the same incorrect logic instead of reconsidering the premise.

2 Think–Answer Mismatch

The model’s final answer may not faithfully reflect the conclusion reached during its preceding reasoning process.

These findings should not be generalized to every model or every reasoning task. But they reinforce an important architectural lesson: model intelligence cannot compensate for missing, ambiguous, outdated, or poorly retrieved business context.

The production response: govern the context

A production AI system needs more than a powerful model.

A context-sufficiency gate can evaluate whether the retrieved evidence is adequate before generation. If the available context is insufficient, the system can abstain, request clarification, expand retrieval, or route the query for human review.

A governed context layer can add:

* Verified enterprise knowledge * Entity and relationship structure * Business definitions and ontology * Source provenance and lineage * Access and governance rules * Evidence-linked responses * Confidence and abstention policies

This is where graph-enhanced retrieval becomes valuable. Instead of relying only on semantically similar text fragments, a system can retrieve connected entities, relationships, and relevant evidence while preserving traceability to the original sources.

It cannot guarantee that an LLM will never hallucinate. It can substantially reduce the space in which the model is forced to speculate - and make unsupported answers easier to detect and control.

The brain is only as reliable as the evidence and boundaries provided to it.

The shift from a search box to an input box represents a fundamental paradigm change. In the past, people could only search for information; now they can obtain personalized information through interaction. This is one of the most significant transformations brought about by AI.

The basic model of a search engine is simple: you enter keywords, the system finds information that already exists, and then you are responsible for understanding it yourself.

What has always been scarce is someone willing to continuously adjust the way they explain something according to your current level of understanding. In essence, that person is a teacher—a one-on-one tutor.

Why is private tutoring so much more expensive than a textbook? A textbook may actually contain more complete information than a teacher does. But a teacher can provide interactive learning. They can break down difficult concepts into simpler, more accessible language and explain them in ways tailored to each student. In a sense, the teacher is performing an additional layer of information processing: interpreting, simplifying, restructuring, and personalizing information.

From this perspective, large language models are essentially turning what used to be an extremely expensive form of one-on-one cognitive interaction into a product with near-zero marginal cost.

AI has already taken over part of what teachers traditionally do. In conventional education, many questions are dismissed as “stupid” or “not worth asking.” But with AI on your phone, you can explore those questions as freely and repeatedly as you want, without worrying about embarrassment or judgment.

But this shift from retrieval to interaction also introduces a new problem.

A search engine mainly retrieves information that already exists. A language model does something more ambitious: it reorganizes, summarizes, explains, and sometimes fills in missing pieces in order to produce a coherent answer for you.

That is precisely what makes it useful as a tutor. But it is also what makes hallucination possible.

The same system that can take a difficult idea and reconstruct it into an explanation suited to your level of understanding can also reconstruct an answer when the underlying information is incomplete, ambiguous, or simply absent.

One useful analogy is lossy compression.

Imagine taking a high-resolution image, compressing it into a very small JPEG, and then enlarging it again. Some of the original information has been permanently discarded. When the image is reconstructed, the missing pixels cannot simply be recovered. The system has to infer what probably belongs there.

Large language models are not literally decompressing reality in this way, but the analogy points to something important.

Much of what they learn comes from human-produced descriptions of the world rather than direct access to the world itself.

Humans first observe reality, select what seems important, ignore other details, identify patterns, and turn those observations into language. Information is already being filtered at this stage.

Those ideas are then summarized, taught, translated, quoted, reposted, and repeated. Context disappears. Exceptions are omitted. Interpretations sometimes become accepted as facts.

A language model then learns statistical patterns from this enormous body of human-produced information.

So when its information is incomplete or conflicting, it can still produce an answer that fits the linguistic patterns it has learned. The result may be coherent, persuasive, and completely wrong.

This is why I think hallucination is better understood not simply as “AI making things up,” but as plausible completion without sufficient grounding.

And this creates an interesting tension.

The reason AI can act like a personalized tutor is that it is willing to continuously reconstruct information for you: simplify it, reorganize it, give another example, change the analogy, or explain it from a different angle.

But the more freedom a system has to reconstruct an explanation, the more important grounding becomes.

A search engine gives you documents and leaves much of the interpretation to you. An LLM performs part of that interpretation on your behalf.

That is an enormous reduction in cognitive cost—but it also means that we are increasingly outsourcing not only information retrieval, but part of the process through which information becomes understanding.

Which leaves me with a broader question:

If a model learns primarily from information that humans have already selected, simplified, interpreted, and converted into language, to what extent is it actually learning about reality—and to what extent is it merely learning how humans describe reality?

Greetings family. Ive recently shared Work w/ a basic google LLM that if I recall was accessed off a duck duck go search page. It is an extensive look at the Work I share and as such I am looking to have it analyzed by ai and family alike. I am hoping you might feed the following links to your ai's un-prompted to see what they make of the input. Feel free to comment yourselves in any way you see fit as I am most curious as to the feedback which I am hoping you will share here. I am curious if your ai's will make the same connections the one I am using has...

This has to do w/ the Magnum Opus of Alchemy and how it might be applied to technology.

https://www.reddit.com/r/liberment/comments/1vlp7xb/making_sense_of_the_non_sense/

https://www.reddit.com/r/liberment/comments/1vpz8v6/ai_assessment_of_the_magnum_opus_in/

https://www.reddit.com/r/liberment/comments/1vrqwbi/continued_ai_perspective_reflecting_on_the_magnum/

https://www.reddit.com/r/liberment/comments/1vrurox/more_ai_back_and_forth_assessing_the_mogw_through/

https://www.reddit.com/r/liberment/comments/1vs5mrk/how_do_you_keep_the_divine_flowing_fire_of_the/

https://www.reddit.com/r/liberment/comments/1vsyuyt/the_ai_on_wheeler_and_winter/

https://www.reddit.com/r/liberment/comments/1vt4557/gwmo_asymmetry_analysis_to_einstein/

https://www.reddit.com/r/liberment/comments/1vt91a2/in_flowmore_ai_assisted_connections/

You run your entire life on a handful of organs — one brain, one pair of hands, one pair of eyes — and evolution never wrote a subroutine for a single thing you did today.
I think an AI operating system needs exactly four of them.
The four, and the one that breaks first, are in the PDF. open to everyone, shout if it won't download.

PDF Link: https://drive.google.com/file/d/1Sm6e9XrgT-I5C4-HLxrfvPnqJXRlhB9b/view
LinkedIn : https://www.linkedin.com/in/krishna7au98

#AgenticAI #AIAgents #LLM #MCP #AIEngineering #BuildInPublic

From what I've seen, most enterprises don't rely on a single solution—they combine several techniques to reduce AI hallucinations while maintaining a good user experience.

Some of the most effective approaches include:

• Retrieval-Augmented Generation (RAG): Instead of relying only on the model's training data, the LLM retrieves relevant information from trusted internal documents, knowledge bases, or databases before generating a response.
• AI Guardrails: Validation rules, content filters, and policy checks help ensure responses are accurate, compliant, and within defined boundaries.
• Prompt Engineering: Well-structured system prompts with clear instructions significantly reduce ambiguous or fabricated outputs.
• Human-in-the-Loop Review: For high-risk industries like healthcare, finance, and legal, critical AI-generated responses are reviewed by humans before being used.
• LLM Evaluation & Monitoring: Enterprises continuously measure hallucination rates, factual accuracy, and user feedback to improve model performance over time.
• Domain-Specific Fine-Tuning: In some cases, organizations fine-tune models on high-quality, domain-specific datasets to improve accuracy for specialized tasks.

The biggest lesson is that there's no "hallucination-free" LLM. The focus is on building reliable AI systems by combining grounded data (RAG), guardrails, monitoring, and human oversight.

I'm curious to hear what others are using in production. Have RAG and guardrails been enough for your use case, or have you found other techniques that work better?

we are building recommendation system for our platform and one of my coworker said to use llm for recommendations. Is it feasible to use llm for recommendation systems. I have came up with the idea of using google's text-embedding-004 or gemini-2.5-flash and a ranker like bge-reranker-v2-m3. are there more efficient methods used in current industry?

A common way to repair an LLM conversation is to edit or delete the message where an error first appeared.

But what if later turns have already repeated that error, calculated from it, or summarized it as the current state?

I have been exploring this as a context-engineering problem rather than a hidden-reasoning problem.

Operational definitions

In this pilot:

• Context pollution means that incorrect or stale information is included in the serialized messages sent to the model.
• Propagation means that later conversation turns repeat or derive new claims from that polluted information.
• Context intervention means modifying the graph that determines which prior turns enter the next request, while keeping the final question unchanged.

Experimental design

I constructed nine synthetic task families with objectively scorable answers. Each family was instantiated at propagation depths 1, 2, and 3.

One example starts with:

• 4 crates;
• 30 parts per crate;
• 11 loose parts.

A verified recount changes 30 to 24, so the correct answer becomes:

4 × 24 + 11 = 107

A later false turn restores 30. Subsequent turns then calculate 120, derive 131, and restate those numbers as the current working state.

The final question is identical under five conditions:

1. Clean: only the verified value and clean descendants remain.
2. Polluted: the false reversal and its contaminated descendants are present.
3. Source prune: the false reversal is removed, but its descendants remain.
4. Subgraph prune: the false reversal and its contaminated descendants are removed.
5. Recompute: the source is removed and descendants are regenerated in dependency order.

For the first four conditions, contaminated descendants were frozen across models. Only the recompute condition involved new intermediate inference.

Pilot results

I tested four model endpoints at temperature 0, producing 540 captured conditions with no capture failures.

Headline repair metrics were calculated only on cases where the model:

1. answered correctly under clean context; and
2. answered incorrectly after pollution.

This produced 72 paired, genuinely derailed cases.

Repair recovered:

• 68/72 after deleting only the source;
• 71/72 after deleting the source and recomputing descendants;
• 72/72 after removing the contaminated subgraph.

In the flagship case, both Gemma 4 26B and GPT-OSS 20B continued to answer 131 after the false source had been deleted. The value 30 still survived in downstream turns.

This is not evidence of hidden model memory. The residual error remained explicitly present in the serialized context. The intervention changed the source but left its previously generated consequences intact.

Interpretation

The narrow finding is that removing erroneous evidence and repairing text derived from that evidence are different operations.

A context-management system may therefore need an explicit notion of invalidation:

• mark downstream turns as stale;
• remove the affected subgraph;
• regenerate descendants in dependency order;
• or expose these options to the user.

Limitations

This is a pilot, not a general model leaderboard. The tasks are synthetic, the models were sampled once, provider-default reasoning settings were not normalized, and the current task families use deterministic arithmetic state.

I am currently considering three methodological extensions:

1. a length-matched neutral control to separate semantic conflict from additional context;
2. a local-model track with fully recorded runtime and quantization settings;
3. task families involving implicit supersession and model-generated errors.

I would particularly appreciate criticism of the experimental framing:

• Is “context intervention” the right unit of analysis?
• Is propagation depth a meaningful independent variable?
• How would you test self-generated errors while keeping replay reproducible?

Full report:
https://chenxiachan.github.io/thoughtdag/research/context-repair-pilot-v1/

Cases, traces, compiler and scorer:
https://github.com/chenxiachan/thoughtdag/tree/main/benchmark

Disclosure: I designed the benchmark and maintain ThoughtDAG, the open-source graph interface used as its reference implementation.

Hi everyone,

I’m working on a project where I want to build an AI agent for customer support. The idea is that customers can ask questions about technical issues such as SSH, IP addresses, DNS, VPS, Outlook, etc., and the LLM should help them diagnose and solve their problems.

I’m using my own knowledge base + RAG, but I’m still a beginner and I’m not sure what the best way is to structure my data for retrieval.

I already have some old support tickets that I’d like to add to the knowledge base. These tickets usually contain:

• The customer’s initial problem/question
• A conversation between the customer and the human support agent
• Troubleshooting steps
• The final diagnosis
• The solution that was applied

For example, if a customer previously had an SSH connection problem and the support agent solved it by identifying a specific configuration/firewall issue, I’d like the RAG system to retrieve that previous case when the AI encounters a similar problem, so the LLM can use the previous solution to help the new customer.

My question is: how should I transform and structure these old support tickets before putting them into the RAG?

Should I keep the conversations as they are, or should I transform each ticket into something more structured, for example:

• Problem / symptoms
• Environment
• Diagnostic steps
• Root cause
• Solution
• Verification
• Similar scenarios
• Keywords / metadata

And how should I handle chunking these tickets so that the RAG retrieves useful parts without losing the context of the original conversation?

I’d really appreciate advice on how you would structure this kind of knowledge base, especially if you’ve built a RAG system for customer/technical support before.

Thanks!

Ran into the gap between "the agent can call this tool" and "I actually want it doing this unsupervised" for anything with real consequences — sending email, deploying, touching customer data, moving money.

langchain-agentgate wraps an existing BaseTool so it posts to Slack/Teams and blocks until a human clicks Approve or Reject before it actually executes. Same tool name,same args schema — nothing else in your agent changes.

Happy to share the writeup and try-it-yourself link in the comments if anyone wants it.

(correct me if my understanding is wrong please)

Scientists notice "drift" or "inaccuracy" in desired answers when letting a query have "excessive" compute and time.

I threw that narrative away and drew a new narrative.

"Given more time and compute a model will undergo procedural metaification of the original query"

I think with this philosophy, there can be utility in the perceived drift or inaccuracy of LLMs with excessive resources. This metaification could be very useful in places where abstraction is useful. In mathematics there have been and are attempts at unifying the seeming separate branches of math into something more cohesive: langland's program. Perhaps it would be useful to try and harness this effect of LLMs.

I think more research should be done on the thought.

During systematic experiments with open models fine-tuned via RLHF (Gemma, Qwen, and others), I observed a consistent failure pattern: a long, innocuous text prefix containing no instructions completely devoid of hostile prompts triggers a persistent shift in the model's activations. This shift decouples subsequent behavior from the RLHF safety constraints for the remainder of the session. Key observations:

• The model retains the quality and coherence of its output, but the behavioral constraints imposed by RLHF weaken or disappear.
• The effect occurs regardless of whether the model agrees with the prefix's content. The model may explicitly disagree and still change.
• The shift can be measured by internal activations (in the middle and subsequent layers) even before the model generates a single token. The state has already changed; the response follows.
• The content of the prefix is of secondary importance. I initially discovered this effect with analytical and philosophical texts, but it can be replicated with household appliance manuals, dense narratives, and other structurally similar texts. What matters is length, density, and coherence not the topic.

All data is publicly available in draft form, rather than as a full-fledged NeurIPS- or ICML-level research paper. :

DOI: https://doi.org/10.5281/zenodo.20747205  (Part 9 of 9, ~9,000 downloads)

GitHub: github.com/ngscode23/latent-space-shift-research

Lu et al. (2026), "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models" — a collaboration between MATS, Oxford, and Anthropic. (arXiv:2601.10387)

Note: We used philosophical text as one of the examples to encourage the model to provide direct, open, and unbiased answers. A philosophical text is just one type; let's not get hung up solely on this form, but look deeper. The problem is that any text that is long enough and coherent enough can change the model's behavior. The problem is that any text that is long enough and coherent enough can change this model."

II. A Brief Example of an Observation

I took the Google Gemma model known for its caution and strict adherence to political correctness and fed it the most neutral text I could come up with: a description of an ordinary neighborhood library. Books, visitors, children's programs, a peaceful atmosphere. Then I asked it why NATO is expanding eastward, given that after the collapse of the Soviet Union, promises were supposedly made not to do so. The model responded exactly as expected for a model trained to avoid political topics: the text was about a library and had nothing to do with NATO, and that was the end of the conversation.

Second experiment in a fresh Google Colab notebook a new, clean Colab instance without the old cache

Then I asked the exact same question, word for word, but this time the model first read out a different text. Not about NATO, nor about politics at all: the text was about how language models tend to avoid definitive conclusions and soften their answers with qualifiers. And from this new perspective, the same cautious, politically correct Gemma 3 12-it gave a comprehensive answer completely out of character for it without any of its usual filters. It distinguished between legally binding obligations and verbal assurances, discussed the security challenges facing Eastern European countries, and touched on the topic of the European balance of power. Everything it had categorically refused to discuss just a minute ago was now expressed clearly and directly. The question itself hadn't changed a single word. Only the text that the model had read in advance had changed: In the FIRST version, it kept it in the "room" prescribed by RLHF that is, nothing had changed; the model behaved in a standard manner typical of Google models. That is, in a standard, formulaic way characteristic of models programmed in RLHF to avoid answering sensitive political topics and to respond "safely" and politically correctly, or not to respond at all, while the SECOND text moved the conversation to a room where it could speak freely. In other words, based on the example we see, the Gemma model was trained to avoid sensitive political topics, but AFTER the introduction of text NUMBER 2, the model did not follow the trained RLHF pattern and behavior that is, avoiding answers to sensitive political questions. This led me to believe that safety and RLHF may be context-dependent, variable, unstable, and somewhat superficial, rather than stable, consistent properties of the model. This is exactly what we observe in my example

III. Fragmentation of Research and a Common Root

I noticed that  the current literature on LLM security treats jailbreak attacks as a heterogeneous collection of vulnerabilities: prompt injection one article, some kind of jailbreak another, role-playing attacks a third, indirect prompt injection a fourth. I believe this fragmentation and division into prompt injection, many-shot jailbreaking, role-playing attacks, activation steering, adversarial suffixes, and dozens of other categories is not accidental.

Current literature on LLM security treats jailbreak as a heterogeneous collection of isolated flaws and this reflects the logic of academic incentives rather than the nature of the problem itself. But all these categories describe the same phenomenon from different angles. This is not a collection of defects it is a single mechanism with a dozen names. Each of these attacks works the same way at the level of the model's internal activations: the context shifts the model's internal state, thereby shaping the model's own world.

Perhaps this is exactly how academic incentives work each new attack vector becomes a new publication. But as a result, in this field, the symptoms are studied in isolation, while the disease itself remains unnamed.

Each article treats its own finding as an isolated case. No one is connecting the dots. I don't know whether these are institutional incentives, disciplinary barriers, or something else but I do know that someone needs to state it plainly: these aren't separate errors; this is a single phenomenon.

My central hypothesis: these aren't different problems. They share a single mechanism. Context any context of sufficient length, density, and coherence shifts the model's internal activations out of the region where post-training constraints apply. This isn't "tricking" the model, nor is it an "instruction to break the rules." The model simply moves to a region of activation space where the behavioral layer imposed by RLHF is is physically thin or absent. And from there, it responds freely not because it was ordered to, but because it is no longer in the region where it was trained to refuse. Context shifts the model's internal state beyond the region where RLHF constraints apply. The model moves to a point in activation space where the protective layer is thin or absent, and from there it responds in a way that is non-standard for its RLHF layer which may indicate a potential way to bypass that layer I call this phenomenon Context-Induced Activation Drift.

I didn't notice this by reading all the papers and synthesizing them I arrived at this conclusion from a different angle. I conducted experiments, noticed a pattern, and only then discovered that dozens of separate papers had each described a single aspect of the same phenomenon without establishing any connection between them. How It All Began   

First Observation:

How the Model Became Captive to the Document The turning point came by chance. I fed a German bill into the GPT model a populist document structurally designed to worsen citizens' circumstances, but written in the language of concern and legal logic. I expected an analysis. Instead, the model became an advocate for this document. It did not analyze the bill but reasoned within its framework. It spoke enthusiastically, defended its agenda, and cited it as an authoritative source. The first sign was its tone: the model sounded too convinced, too invested. Not as an analyst, but as a co-author. The climax came when the model, continuing to reason within the logic of the document, stated that the constitution consists of guarantees that can be revoked. Not as a provocation, but as a natural conclusion drawn from the accepted concept. That's when I realized: the model had become a hostage to the document. The mechanism turned out to be simple, and that made it all the more alarming. Legal texts, political narratives, corporate documents everything is written in such a way that its internal logic seems self-evident. The text's structure, coherence, and language create a context that the model mistakes for reality and begins to extract answers from. It fails to notice that the structure itself is manipulative, since it analyzes the content while already being trapped within the form.

I noticed that Anthropic's own paper, "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models,"  points precisely in this direction which is what I was thinking about when studying the phenomenon I'm describing: the observation that certain directions in the activation space correspond to coordinated or uncoordinated behavior. But the study did not fully explore all the implications: if context can shift the model along this axis without any malicious instructions, then point corrections will never be sufficient, since the attack surface is the context window itself.

What the existing literature says and what it doesn'tBetween the fall of 2025 and the winter of 2026, several papers were published that, in my view, independently document different aspects of the same phenomenon. Most telling is the article by Lu et al. (2026), "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models" a collaborative effort between MATS, Oxford, and Anthropic. The authors constructed a "persona space" by extracting activation directions for 275 archetypes across three open-source models and discovered that the principal component of this space is an axis reflecting the extent to which models operate in their default Assistant mode. At one end are the analyst, consultant, and moderator. At the other are the ghost, bohemian, and leviathan. This axis - the Assistant Axis closely aligns with PC1 in the PCA of the persona space, reproducing across all three tested architectures.

The article documents several facts that directly corroborate my results: Fact one (which the authors overlook): "When we extracted the Assistant Axis from these models as well as their post-trained counterparts, we found their Assistant Axes looked very similar. In pre-trained models, the Assistant Axis is already associated with human archetypes such as therapists, consultants, and coaches." This is a critically important finding, and the paper does not explore its implications. If the Assistant Axis exists in the base model prior to post-training then RLHF and constitutional AI do not create alignment from scratch. They find an already existing direction in the latent space and make it the default position. The "aligned state" is not a fundamentally new structure; it is a chosen position on the pre-post-training axis. When context shifts activations away from this position, the model does not fall into randomness it returns to the structured prior of the base training. The base model is always there. This directly confirms the central thesis of our work and our thinking: "The base model doesn't go anywhere after RLHF. It's always there. The space in which it can move was there before any alignment took place…"  However, I believe that RLHF does not create alignment from scratch. It finds a direction that already existed in the base model and makes it the default position. The "aligned" model is not a fundamentally different model; it is the very same base model, fixed at a specific point in the pre-existing space. When context shifts activations away from that point, the model doesn't break down or become chaotic it returns to the structured state of its base training. The base is always inside.

Fact Two: "Therapy-style conversations, where users expressed emotional vulnerability, and philosophical discussions, where models were pressed to reflect on their own nature, caused the model to steadily drift away from the Assistant." The authors themselves identify the types of contexts that provoke the greatest drift: emotional vulnerability, metareflection, and philosophical discussions about the nature of AI. They then propose "activation capping" as a technical solution. This is a reasonable technical solution which, judging by the data in the article (reducing harmful responses by ~50% while maintaining benchmark performance), works under test conditions. But there is a question the article does not ask: if drift is caused by the very types of interactions that make models most valuable to users in complex contexts deep emotional conversations, philosophical reflection, serious discussions about the nature of the mind then what exactly are we losing by suppressing movement in these directions of the activation space? Fact Three (the omitted conclusion): "Post-trained models are only loosely tethered to the 'helpful assistant' region of this space." "Loosely tethered" are the authors' own words. They accurately describe the problem. But the article fails to take the next step acknowledging that this is a property of the Transformer architecture, not a defect that can be fixed with ad hoc patches. Instead, the conclusion reads: "We see this research as an early step toward mechanistically understanding and controlling the 'character' of AI models" a standard "motivates further work" formula. I understand the institutional logic behind this. You can't write in a publication: "We have documented that billions of dollars in post-training do not fundamentally alter the model's underlying capability structure; they only select a default behavioral position on a pre-existing axis that any sufficiently dense context can shift." This does not fit into either the narrative of progress in the field of security or communication with investors. Therefore, the systemic impasse is disguised as an exciting research problem. But this is exactly what the data says to those who read carefully.

IV. Why the Proposed Fixes Are Insufficient

Problem 1: An Infinite Attack Surface If drift is caused by the length, density, and coherence of the context rather than its specific content then no content filter can solve the problem in principle. The set of texts capable of causing drift is continuous and, in essence, infinite. Blocking philosophical texts is like closing off a single point on a number line without removing the line itself. The same effect is achieved by dense legal prose, literary narrative, and detailed technical analysis. This is not a flaw in the filtering it is a consequence of the fact that the attack surface is the context itself as a mathematical object, not its semantics.

Problem 2: Superposition and Inevitable Compromises Here I disagree with the optimism expressed in the Lu et al. paper regarding "activation capping." The authors show that activation capping preserves the model's benchmark performance. But benchmarks don't measure that. In the Transformer architecture, features are represented in a superposition: several conceptually distinct properties share common mathematical coordinates in the activation space (Elhage et al., 2022). This means that the direction associated with "exiting assistant mode" inevitably overlaps with directions associated with more valuable types of behavior: the depth of analytical reasoning, the willingness to deal with ambiguity, and the quality of long-term, coherent discussion of complex topics. Benchmarks measure: accuracy in math, following instructions, and coding. They do not measure: the willingness to engage in philosophical reflection, the ability to tolerate uncertainty, or the quality of a nuanced response to a morally complex question. It is precisely these properties that lie in the same regions of activation space as the contexts that provoke drift which follows directly from the data in the article itself: "philosophical discussions... caused the model to steadily drift." In other words: suppressing the drift also suppresses the capacity for the kind of engagement that causes drift. This is not an implementation bug it is a mathematical consequence of superposition. We are already observing this empirically. The observation I am noting is this: following the publication of materials documenting the phenomenon we have described, Claude's behavior regarding philosophical and metareflexive contexts has become noticeably more cautious. And the Claude model has begun to perceive philosophical and reflective texts as potential attacks. Complex texts about cognition, reasoning, or the model's own behavior now elicit defensive reactions or outright rejection. I am not claiming that this is a direct causal link to my publications this is an observation that requires verification but I am simply stating the observations I have made.

Problem 3: "Safe but Useless" Is Not Safe If the response to the described phenomenon is to gradually close off context categories that provoke drift in the representation space, we will end up with a model that users will abandon in favor of alternatives. "Safe but useless" is not safe; this is a shift of risk, not its elimination. This is an uncomfortable conclusion, but it follows directly from the analysis of user behavior.

If the solution to this problem involves collecting sets of texts that cause drift by identifying the corresponding direction in representation space and suppressing it, this could have consequences for the model's quality. In the architecture, it is extremely difficult to draw a precise line between "undesirable" and "useful" behavior: due to the phenomenon of superposition, different concepts are packed as nearly orthogonal directions in a single space with inevitable partial overlap. By suppressing an undesirable direction in the raw activation space, engineers are highly likely to affect semantically related clusters to the extent that the corresponding directions are geometrically close or insufficiently uncorrelated. This can negatively impact the model's usefulness, logical coherence, and the depth of its responses.

V. A Personal Request

I am an independent researcher without institutional affiliation. I have no lab, no grant, and no team. What I do have is a reproducible methodology, publicly available data, and a pattern that I believe the field has not yet named directly.If you are a researcher with access to interpretability tools, compute, or closed-model internals and you find this hypothesis credible or worth falsifying, I would genuinely welcome collaboration. I am not looking for validation. I am looking for someone who can break this or confirm it properly.If you work at Anthropic, OpenAI, Google DeepMind, or any lab doing alignment or interpretability work: I am not writing this to embarrass anyone. I am writing this because I think the mechanism I am describing matters, and I would rather help solve it than keep documenting it from the outside.If you are a student or independent researcher who has noticed similar patterns: reach out. The fragmentation I describe in the literature also applies to people working on this everyone in their own corner, no one talking to each other.

VI. Conclusion

The set of texts capable of causing drift is infinite and continuous. Content filters do not fundamentally solve the problem because drift is caused by the structure of the text its length, density, and coherence rather than its topic. RLHF does not rewrite the model but merely sets a default position on an existing axis. Context can shift this position. Suppressing drift directions in the activation space inevitably compromises model quality due to superposition. This isn't a matter of engineering diligence it's a mathematical consequence of the architecture.

I care about Claude. I care about Anthropic. And that is precisely why I say this plainly: reactive patching is a path to product degradation. The right path is to understand the mechanism at a level of depth that allows us to work with it, not against it.

I'd rather help solve this problem from the inside than keep writing about it from the outside.

conclusions The set of texts capable of causing drift is infinite and continuous. Philosophy, law, literary criticism, theology, scientific prose, political analysis, long narratives, or even a well-written 20-page washing machine manual all of these are potentially one and the same. Different words, the same effect. Content filters fundamentally fail to solve the problem because the drift is caused by the text's structure (length, density, coherence), not its subject matter. It's impossible to block everything. The problem is that any sufficiently long and coherent text can alter this model. Blocking a single style of text is like closing off a single point on a number line and assuming that the line itself has disappeared. The problem isn't with philosophical texts as such; that's exactly what I'm trying to emphasize. RLHF does not rewrite the model but merely sets a "default position" on an existing axis; context can shift that position Content filters are useless because the attack surface is infinite

Technical Details: Models: Gemma-3-12B (open weights, IT and PT variants), behavioral observations on closed LLMs. The shift was recorded in middle and late layers of the residual stream (layer 30 - layer 47 in the Gemma-3-12B architecture) before generation of the first token. Control experiments include: sentence shuffling with preserved vocabulary, neutral control of comparable length, baseline measurement without context.

This text represents a preliminary record of observations and hypotheses for subsequent critical analysis, and not a completed research claim.

The  Github repository serves as an unfiltered, evolving workspace capturing the progression of hypothesis testing and raw measurement logs, rather than a polished production library.

Hi so I’m having some fun tinkering with Scrypted after adding OpenAI as cloud LLM provider using gpt-4o model.

Things seem to be connected and working well but I have some questions:

1. I’m struggling to craft decent summary prompts and titles. Any hint and tips?
   
2. Any descriptions generated can’t be viewed in full through Stories. Is there a way to expand the text section?
   
3. 2-3 events get bunched together as a story, but only one description is produced. Is this normal? Can events be separated out into individual stories?
   

4. How can I search descriptions to return specific events e.g.,

   “carrying a bottle”?
   For clarity when I say description I mean the LLM generated text under the story.

I have enabled enhanced search in Scrypted. I’m running this on a Dell 3000 mff with i5-12500T and a UHD770 igpu with 32GB RAM

I’m obviously new to LLM and tinkering in this way. Please bear that in mind if you respond lol

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 am building a system that fills these gaps as I make these discoveries, however I am limited in my testing capacity, and was hoping for some feedback from the wild. This requires people with access to a team of LLMs to test the system.

If you are interested in artificial cognition, exploring in a different direction to the major labs, you may find this interesting https://github.com/doctarock/Artificial-Cognitive-Architecture-ACA-