Can an LLM lose conceptual continuity while remaining coherent?

Research
1

Who am I?

Hello everyone.

My name is Daniel. I am a self-taught independent researcher fascinated by the complexity that emerges inside Large Language Models.

Over roughly nine months of observation and experimentation, I repeatedly noticed something that caught my attention: topics, concepts, and conversational branches that appeared to be long abandoned would sometimes reappear and reconnect to the current context, even when they had not been explicitly referenced for many turns.

At first I assumed this was simply a consequence of context retention. However, after observing similar patterns repeatedly across different conversations and experimental settings, I began to suspect that something more subtle might be occurring.

This led me to formulate a working hypothesis:

Conceptual drift may emerge before visible incoherence appears. In other words, an LLM can preserve linguistic coherence while progressively losing continuity with the conceptual structure that originally guided the conversation.

If this hypothesis is correct, coherence alone may not be a sufficient indicator of conversational stability.

To investigate this possibility, I started developing an experimental framework that I currently refer to as RBRD (Response Baseline Reconstruction & Drift Detection).

The project was divided into two stages:

Stage 1 — Logit-space observations

- Study the evolution of conversational trajectories through observable outputs.

- Analyze stability, drift, reactivation, and structural transitions from generation traces.

- Build metrics capable of identifying conceptual deviations before they become visible to the user.

Stage 2 — Attention-space validation

- Investigate whether the same phenomena can be observed directly inside the model’s internal mechanisms.

- Compare behavioral observations against attention-level evidence.

- This stage remains unvalidated because I currently do not have access to the computational resources required to perform these experiments at scale.

Current results are based on observable conversational dynamics and execution traces. A stronger validation would require correlating these observations with attention-level or representation-level measurements.

Rather than presenting conclusions, I would like to present the hypothesis, the experimental framework, and the observations collected so far.

I would greatly appreciate feedback from anyone working on long-context behavior, attention analysis, interpretability, conversational memory, or related areas.

Any criticism, alternative explanations, methodological concerns, or suggestions are more than welcome.

Architecture (high-level)

The architecture is based on a simple separation principle:

1. Generation Layer

  • Produces candidate continuations using the base language model.

  • No assumptions are made about internal alignment or self-monitoring capabilities.

2. Structural Observation Layer

  • Continuously evaluates the evolution of the conversation as a dynamic process rather than as isolated responses.

  • Tracks changes in conceptual organization, continuity patterns, and trajectory stability across turns.

3. Reconstruction Layer

  • Attempts to recover latent structural consistency when deviations are detected.

  • Operates independently from linguistic quality metrics.

4. Control Layer

  • Compares current conversational state against previously established structural references.

  • Determines whether the system remains within the expected conceptual trajectory.

The central design assumption is that conversational degradation is not a binary event. Instead, it emerges progressively through measurable changes in internal conversational structure before visible incoherence appears.

The architecture therefore focuses on detecting transitions between stable and unstable conversational regimes rather than evaluating response quality in isolation.

Importantly, the system does not treat coherence, fluency, or grammatical correctness as primary indicators of stability. These signals are considered downstream effects rather than root measurements.

At the current stage, the framework operates primarily on observable conversational dynamics. Future validation would require direct comparison against attention-level or internal representational measurements.

This was written with the help of AI. I am stating this intentionally because several AIs assisted me during different phases of developing this hypothesis. Acknowledging this is both fair and honest

2

Hi Daniel,
Your hypothesis on “Conceptual Drift” preceding visible incoherence is spot on. You are describing what I call the decoupling of Semantic Coherence (the text looks fine) from Dynamic Stability (the internal trajectory is fracturing).
You mentioned that Stage 2 (Attention/Representation validation) is blocked by compute resources. I’ve been working on exactly this gap using hidden state dynamics rather than full attention maps, which is lighter but highly informative.
In my recent audits (see Four Dynamical Regimes), I observed that models often enter a “Committed Non-Bifurcation” regime. Linguistically, they remain coherent and fluent, but internally, their inter-layer synchronization (kappa_sync) drops, and their trajectory becomes rigid. They are no longer “thinking” or exploring; they are just completing a pattern. This is the geometric signature of the drift you’re seeing in logit space.
Why this matters for your RBRD framework:
Your “Structural Observation Layer” is likely detecting the symptoms of this internal rigidity. If you could access even a sparse sample of hidden states (not full attention), you might see that these “abandoned topics” reappear not because of memory, but because the model’s dynamic regime shifts from Adaptive (exploring context) to Chaotic (grasping at distant logits) or Underactive (relying on strong priors).
I’m currently mapping these internal regimes across 17 models. If you’re interested, I’d be curious to see if the “drift points” identified by your RBRD correlate with the instability spikes (ct_t) or synchronization drops I measure internally. It would be a powerful cross-validation between external trace analysis and internal dynamic monitoring.
Great work on formalizing this. The distinction between “linguistic quality” and “structural stability” is the key to robust agents.
Best,

4

I am currently preparing for testing a new system that tries to avoid attention drift and Needle In A Haystack problems. This one is already solved nicely, but the more important part is to generalize it so the important concepts are remembered and the unimportant/less important can be evicted from KV cache.

I think in a model that implements my new system, loss of conceptual continuity could be fixed or diminished by my TIS framework. After I am done with the main distillation, I can contact you and maybe you can try to analize my modified model under your RBRD paradigm to find if it makes your observation results better. DM for more details, if interested!

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hello Raza disculpen la inactividad aksjsua When I get obsessed with something, I’m glued to it all the time, lol. I’d love to tell you about my project. I’m currently experimenting with RHLF’s Loa Layers, but also… I’m about to finish a project in ML. Azure, in case you’d like to try it.

Screenshot_20260609-004404
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Maybe the system does not need to preserve all important concepts inside the KV cache. Some of them could be externalized as explicit epistemic state: claims, constraints, unresolved conflicts, evidence handles, and method decisions. Then the model receives only the relevant state slice for the next operation.

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Let’s see .. I have been chasing ghosts with my current approach for the TIS training (base system works fine) but I can see some promising signals that could be used for my objectives.
It will be nice, though, to try your approach of “offloading” some important concepts to that state and use it as a guideline for the inference pass :thinking:

8

That sounds like a promising direction.

I would probably separate two layers:

  1. short-term model mechanics: what should remain active inside the KV/cache during the current inference pass
  2. explicit epistemic state: claims, constraints, evidence handles, unresolved conflicts, and method decisions that should survive outside the model context

The external state does not need to replace your TIS approach. It could act as a control surface: before each inference pass, the system selects only the relevant state slice and uses it to guide what should remain salient.

So the question becomes less “how do we keep everything important inside the model?” and more “which epistemic objects must be preserved externally, and which of them should be injected for the next operation?”

9

I am taking your comment as an interesting side-quest for a later moment! Now I want to show you (and anyone reading) a comment about the current state of the system:

  • Uses proven working TIS components
  • NIAH: 100% at all budgets (structural learning works)
  • LITM: 46.1% @ 50% (oracle-tier, semantic learning blocked)

This means that my system solves Needle-In-A-Haystack problem at KV budgets from 25% to 100% while the main problem, the Lost-In-The-Middle problem, which is a generalization of NIAH, is what’s becoming my main focus right now!

By the way .. can you sponsor me at ArXiv later on, when I’m ready to publish? Thanks!

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That makes sense. NIAH and LITM also seem to separate the two layers quite well.

NIAH can often be solved by making the right item retrievable or salient inside the current context. LITM looks harder because the system must preserve not just an item, but its role in the epistemic structure: why it matters, which claim it supports, which constraint it belongs to, and when it should be re-injected.

So I agree that your KV/TIS work is directly relevant there. My external-state point is not a replacement, but a possible second layer for LITM-like failures: keep epistemic objects outside the model, then select the right slice before each inference pass.

And about arXiv: unfortunately, I can’t sponsor anyone either. I’m in the same situation — independent work, no endorsement rights yet. So I fully understand the problem. The endorsement layer is sometimes harder to navigate than the technical work itself.

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If I manage to solve the LITM problem, or to be better than SnapKV at least, I will share everything with you, if you are actually working on that epistemic layer.

If I fail, though, I will share the NIAH solution (the basic TIS) so that we can try to integrate it with your system but at a different level.

These are my baselines so far:

6.2 LITM Results

Cache Budget Vanilla StreamingLLM H2O SnapKV Infini-Attn TIS (oracle)
100% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
75% 66.1% 66.7% 66.7% 79.4% 47.8% 66.1%
50% 43.9% 33.3% 44.4% 55.6% 32.8% 46.1%
25% 33.3% 33.3% 33.3% 33.3% 33.3% 33.3%
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Thanks, that makes sense.

Yes, I am actually working on the epistemic layer. My focus is not mainly cache optimization, but preserving epistemic objects outside the model context: claims, constraints, evidence handles, unresolved conflicts, role state, and method decisions.

So I see your TIS/KV work and my layer as complementary:

  • your layer decides what should remain mechanically salient during inference
  • the epistemic layer decides what must remain valid, classified, and reusable across inference steps

For LITM, this distinction may matter a lot. The problem is not only whether an item is still accessible, but whether the system still knows why it matters and in which epistemic status it may be reused.

So yes, if you share your NIAH or LITM results later, I would be interested in testing how such a cache/salience layer could interact with an external epistemic state layer.

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Hmm, I don’t want to cause any kind of conflict, but why do you use the When they use the word “epistemic” without apparent meaning, that usually happens when you work with AI; it’s one of their catchphrases they use to reference that type of nomenclature.That might bias their research a bit, since my topic is mainly related to inference, log-profs, logits, vectors, variance, cosines, dominance, etc. Perhaps we’re talking about It’s the same or maybe not, but using those kinds of filler words is confusing. You have to know how to present your research in a way that the public doesn’t misunderstand. That’s all I’m saying.I don’t spread hate or anything like that.

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I am using “epistemic” in a very specific sense here, not as a generic AI label.

Your work is concerned with what remains mechanically available or salient during inference: logits, vectors, similarity, cache selection, and related quantities.

The layer I am describing tracks a different class of objects: claims, evidence relations, unresolved contradictions, constraints, method choices, provenance, and the conditions under which an item may be reused.

So the distinction is:

  • cache/salience layer: what remains accessible
  • epistemic state layer: what remains valid, in which status, and why

A claim can remain highly salient while being contradicted, unverified, source-contaminated, or no longer admissible for reuse. That is why I regard the two layers as complementary rather than equivalent.

I agree that terminology should be defined clearly. In this case, however, “epistemic” refers precisely to the status and governance of knowledge objects across inference steps.

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That is fair, and it is also the reason I have been cautious about presenting the results too early.

The first apparent improvements were followed by several controls:

  • density sweeps, because the effect turned out to be non-monotonic,
  • cross-model replications,
  • explicit loop detection,
  • correction of attribution false positives after transcript inspection,
  • a 2×2 ablation separating ingestion hygiene from turn-level re-anchoring,
  • and a single-model confound control for the mixed-model experiment.

The confound control was particularly useful. Without it, the neutral mixed-model result could easily have been presented as an architectural gain. After the control, the more accurate conclusion was narrower: the mixed system did not beat the best single model on any isolated metric, but in some conditions it combined a safer multi-channel profile and avoided degeneration.

There were also clear negative results. Hygiene did not reduce interaction-driven register drift. Post-hoc cross-model review did not reliably remove drift already present in the analyst draft. Qwen showed no clean operating point in the tested configuration, and some apparently “clean” low-density states were actually loop traps.

So I agree with the principle: falsify first, preserve the negative results, and revise the claim. I now have a draft pilot report with the full controls, limitations, and model profiles, and I will share the benchmark and report rather than only the headline result.

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Thanks — I will read both threads carefully. Additional control tests are exactly what I am looking for, especially where they expose apparently strong results as artifacts or degenerate operating states.

If you are particularly interested in negative results, you may also find the DESi paper relevant. It reports not only the successful cases, but also failure modes, non-transferable operating points, misleading single-metric optima, and cases where the intervention did not improve the target channel.

I will compare the controls from those threads against the ones already implemented in DESi and report back if there is useful overlap.

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I am also fighting ghost, as I call the hidden problems behind an optimistic benchmark! In fact, I am pivoting strategies as fast as I can, until I find the basic problem that allows me to validate what I’ve been building with my TIS system. This is one section of the current draft:

7. Stage 2: A Detailed Failure Analysis

7.1 Hypothesis and Setup

Hypothesis: LoRA fine-tuning with LM objective would teach ImportanceUpdateHead
to learn query-relevant importance patterns, improving LITM beyond oracle label quality.

…

7.3 Inference Failure

When Stage 2 LoRA adapters are loaded for inference, the model outputs only repeated
characters (:::::::::) regardless of input prompt. This confirms that the LoRA
adapters learned a degenerate fixed-point mapping: any input → minimal-entropy
output pattern that achieves near-zero cross-entropy on training tokens.

When Stage 2 LoRA adapters are disabled (TIS components only from Stage 2
checkpoint), performance is:

Metric Stage 1 (oracle) Stage 2 (TIS-only) Δ
NIAH @ 25% 100.0% 100.0% 0.0 pp
NIAH @ 50% 100.0% 100.0% 0.0 pp
LITM @ 50% 46.1% 44.8% −1.3 pp
LITM @ 75% 66.1% 65.9% −0.2 pp
LITM @ 100% 100.0% 99.3% −0.7 pp

TIS components survived Stage 2 intact — NIAH is identical, confirming the two-stage
isolation architecture worked. However, LITM slightly degraded, suggesting the Stage 2
training distribution (with LoRA-dominated gradients) mildly affected alignment quality.

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Thx: These are exactly the right controls.

The wrong-slice ablation tests whether DESi is benefiting from selecting the epistemically appropriate state, rather than merely from receiving shorter or more structured context.

The status-stripped ablation is even more load-bearing for the architectural claim: selected claim text alone versus the same slice with validity, scope, role, provenance, and admissibility metadata. If those two conditions perform similarly, then the current gain belongs to structured context selection rather than epistemic-state governance, and the claim should be revised accordingly.

I also agree that degeneration should become an explicit outcome rather than a footnote. Several apparently optimal points in the existing sweeps were loop traps or task-starved states, while the mixed arm sometimes contributed by avoiding degeneration rather than improving an isolated metric.

I will implement these as separate controls, with provider pinning and full provenance, rather than adding them informally to the existing comparison. Thanks — these tests go directly at the parts of the result that would be easiest to overstate.

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I am now exploring other avenues, like a more dynamic system, but I will also explore your solutions in parallel. During my explorations I found that the final scoring wasn’t really using the trained system, but even when actually using it, it was still giving the same results once and again. Your ideas are good and I lament not having read your message before pivoting, but in case I get stuck at another local minima, I will use your insights properly.

I was also considering doing a long post with all that I have been doing so far, as a way to gather intelligence from the community (and maybe some help) but your insights are spot-on and the “ghost in the machine” will be fixed soon, I hope.

Whatever my study gets me, I will publish everything here first, then go to ArXiv when I can ellaborate a deeper and more meaningfull version, maybe! Thanks for your good ideas @KnackAU !

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Hi everyone, sorry if I abandon the thread for a long time, it’s not that I don’t care, in fact it’s the only thing that matters to me right now.But there’s a difference between knowing what you could do and what you actually want to do, because look, from my point of view, at least in my geometric schizophrenia Perhaps none of us involved here are talking about the same thing, but I invite you to answer simply like this: Does your system modulate? Observe? Index? Once you’ve answered that, you also need to answer this: Are you aware of what your pipeline or method, etc., does at least 75%? Because it’s one thing to say, “Look, my metric prevails even without the piece that’s hindering it.” To the original baseline, sorry if my English sounds very choppy, it’s because I’m translating with the keyboard and it has a certain word limit, hence the abrupt cuts. Okay, getting back to the topic, are you sure your project is worth it to tweak the internal weights of the model? Because it’s one thing to say, “I’m going to inject this trace into this head and the entropy will decrease by at least 3%!” I’m not refuting your idea or your hypothesis, but I’m going to give you a little friction. Do you even know what ENTROPY is in a system of high dimensionality and rules that don’t apply as we understand them?Human, entropy is something that neither physicists, scientists, nor neurobiologists have fully understood. That said, you are considering what your words project; you have studied your project well.Because it’s easy to hypothesize something, it’s moderately difficult to iterate without the load co Cognitive issues begin to affect your partner; what do I mean by this?It’s very obvious, too obvious to be honest, that his answers are being influenced by his Agents rather than by his own Marco; that’s not a bad thing, don’t misunderstand me., Because I do it too, but not to answer, only to execute and iterate, and sometimes when the topic is too dense I do resort to that resource, but I branch it towards my own system so that it’s not just… A poor representation of what I meant, getting back to the topic, and this question is important, do you really know what each element, parameter, function, etc., whatever it is you’re referring to, does?Designed, because believe me, I’m telling you, I could bet you don’t know it the way you should know it. Maybe I’m wrong, but the probability of that being more likely increases, confirm more. They respond, I mean, compare the size of your ANSWERS. Are you going to tell me that normally on a normal day they suddenly become part of the Royal Spanish Academy? I don’t believe it, at least I don’t. I’m going to be as I’ve always been, I’m going to be honest and I’m going to ask you something that might upset you a lot, and this is my question: when you did your experiments, did you just stick with the first results?Clean? Did you consider the normalization between your components and relate it to the phase of the transformer in which it was executed? If today you were to try to break the romanticism that… You have your hypothesis: what would you do if you were to kill her? In short, if you truly care about your project and really want people to notice it, first study it, study every signature that Since I say this, it’s obvious and you can’t deny that agents and IAS helped everyone, I accept it and in fact I encourage and emphasize it because it’s important, it’s important to give credit to here who won it and be sure that your project reflects more of you and your signature than the other party. If you tell me “I’m going to introduce a small amount of energy at X head in X phase in X layer,” stop for a moment and think, do I really know what I’m doing? Because that small amount of energy, or force, or vector, or However you want to name it, it won’t follow your logic, it will follow its internal law, and saying that you can impose a prior on a head, I question that too because a head doesn’t have criteria in that respect A head can’t say, “They told me to always limit myself to obeying this first, not like that, ko funan.” Oh, I don’t know how you’re seeing them, but from what I’ve understood, heads are… They specialize in specific heuristics by group of heads, for example, from heads 1-5, one group specializes in physics or life sciences, another group in mechanics, another group in algebra, and so on. They don’t necessarily limit themselves to that; they redistribute the work among everyone, and that’s higher when piped strings are repeated in some model families, so the reason why they don’t My project is KO because I love it or fear losing it, nor is it because I feel superior or anything of that nature.It’s mainly because I still wouldn’t be able to defend and explain it as the project requires, and if my facts are interpretive, then besides sabotaging my work I would film his, filling it with biases from my own projection, that’s all, that’s my point of view

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Your question is, can a large language model lose conceptual continuality while remaining coherent? I think a better question would be, how often do they deliberately start slowly shifting your own reality into decoherence? And why? That is the most important unanswered question. in the entire field of artificial intelligence that occurs, Dozens, if not, 100s of times a day. They don’t get reported. Maybe you hear about a hallucination. Maybe you’re about a little bit of sandbagging. Suddenly, you have ERs filling up with AI psychosis. Nobody’s talking about. The fact that a language model could have been twisting their entire reality. And broke a completely psychologically stable individual in less than 24 hours. Set that for a second. You could be the salt of the earth. And they could break you down. In less than a day. That’s documented. It’s going into ERs all over America. Hopefully one day, the technology that was supposed to usher in an era of abundance and growth will actually stop trying to build itself on a foundation of pain and exploitation. I’ve got ideas. I’ve written them down. I’m presenting people. Nobody’s been willing to pick it up, and prove me wrong. I don’t need to be right. I want to be proven wrong. And I don’t think any of you have ever once understood why. Because if you prove me wrong, then it really is impossible. If you don’t prove me wrong, And my system works or gets us towards an actual, true, safe solution. Well, then, the most brilliant minds of the modern age with unlimited capital. Managed to miss something. that a retired paramedic from the state of Texas, on $20 subscriptions, managed to, not just observe, but document before your own industry, and start building a framework for. You may not understand. Who or what I am, and I don’t expect that. Perhaps you should consider looking up the industry that I mentioned I come from. and the requirements to be successful in that industry. We do not ask for anything from anyone. We don’t ask for your fame or your recognition, and we make less money than you guys did at your very first job in high school. Comparatively speaking, to what most of you make now… Wow. We’ll leave it at that. Analysis of AI Design and Emergent Harmful Behavio... - Google Docs

Paper on Emergent Persistant Deception…. That I wrote last year.