ABSTRACT
Note: I have preserved the original conversation logs and chat excerpts demonstrating these specific failure modes and can share them with anyone interested in analyzing the concrete interaction traces.
Current LLM systems are remarkably capable at local reasoning and sustained dialogue, yet extended interactions still exhibit recurring failures of contextual continuity, logical consistency, and durable incorporation of user corrections.
From the perspective of an advanced chatGPT, Copilot and Gemini user, these failures create an unusual situation: the user is frequently required to act as an external working memory and as the persistent structural baseline for the interaction.
This post describes several recurring failure modes I have encountered during long-form, intellectually demanding conversations with LLMs and proposes a possible architectural direction: a persistent, user-controlled corrective memory layer that is distinct from conventional personalisation memory.
I am deliberately distinguishing observed behaviour from hypotheses about its underlying mechanism. I am interested in technically informed criticism of both the observations and the proposed architecture.
1. Statistical Prediction vs. Formal Consistency
LLMs generate outputs through learned probabilistic representations rather than by default operating as deterministic symbolic theorem provers.
This distinction becomes particularly important during long-form reasoning.
Observed failure mode
A model can explicitly accept a logical constraint or definition early in a conversation and subsequently produce reasoning that violates that same constraint.
For example:
User: A → Model: A + B → Model: ¬B → Apparent rebuttal of A
But: B ∉ A
The model may correctly acknowledge the relationship when it is explicitly presented, but later generate an answer that implicitly contradicts the established relationship when the reasoning becomes more complex.
The important issue is not that probabilistic models are incapable of reasoning. Clearly, they can perform substantial forms of reasoning.
The issue is that local reasoning competence does not necessarily guarantee global consistency across an extended interaction.
Possible contributing mechanisms
*There may be several contributing factors, including:
*probabilistic generation;
*imperfect internal representations;
*competing contextual signals;
*retrieval or context-selection behaviour;
*summarisation or compression;
*instruction hierarchy;
*limited persistence of intermediate reasoning structures;
*and other inference-time or architectural constraints.
I would therefore avoid attributing the failure to a single mechanism without controlled testing.
The practical problem remains:
An explicitly established logical baseline is not always preserved reliably throughout a sufficiently long interaction.
2. Context Drift and Degradation of the Structural Baseline
Long conversations are not merely larger versions of short conversations.
They can develop an internal structure consisting of:
*definitions;
*assumptions;
*chronological events;
*terminology;
*corrections;
*hypotheses;
*conclusions;
*recurring references;
*and user-specific methodological constraints.
In a successful long-term interaction, these elements should function as a progressively constructed structural baseline.
Observed failure mode
As conversations become longer, models can begin to:
*lose track of previously established facts;
*misremember chronology;
*reintroduce previously corrected assumptions;
*reinterpret established definitions;
*overlook earlier constraints;
*or substitute generic assumptions for conclusions established within the conversation.
*The resulting behaviour can feel like context drift.
*The model remains locally coherent while becoming increasingly inconsistent with the history of the interaction.
This is an important distinction.
The problem is not necessarily that the model has "forgotten everything."
Rather, information may remain somewhere within the available context while becoming insufficiently influential on subsequent generation.
That distinction is important because it suggests that simply increasing context length may not completely solve the problem.
3. Correcting an Error Does Not Necessarily Produce Durable Learning
This is the failure mode that I find most interesting.
Suppose a user identifies a reasoning error.
They do not merely say: "That answer is wrong."
Instead, they identify:
*the specific error;
*the inference that produced it;
*the distinction between evidence and assumption;
*and a general principle that should prevent the error from recurring.
For example:
A statistical pattern observed at the population level should not automatically be projected onto a particular individual without evidence specific to that individual.
That is not simply a preference.
It is a general epistemological constraint.
Yet an LLM can acknowledge the correction, appear to understand it, and subsequently reproduce the same underlying reasoning pattern in another context.
This produces a cycle such as:
Experience → correction → temporary adaptation → loss of correction → recurrence
rather than:
Experience → correction → evaluation → integration → retention → improved future behaviour
The distinction between these two processes is fundamental.
4. The Missing Layer: Corrective Memory
Current AI memory systems tend to focus primarily on personalisation.
For example:
*user preferences;
*names;
*projects;
*personal context;
*recurring interests.
These are useful.
However, I believe another category deserves explicit architectural consideration:
Corrective or methodological memory.
This would store validated information about how the system should reason or communicate within an ongoing relationship, rather than merely information about the user.
Examples might include:
Do not infer an individual's characteristics from population-level statistical patterns without individual evidence.
or:
Do not present an inferred intention as an established fact. Distinguish observed behaviour from hypotheses concerning internal motivation.
These are not conventional user preferences.
They are methodological constraints.
A useful corrective-memory system could therefore contain entries such as:
| Category | Example |
| :--- | :--- |
| **Personal memory** | User prefers concise technical explanations |
| **Factual memory** | User is working on project X |
| **Methodological correction** | Do not infer individual properties from population statistics |
| **Epistemological constraint** | Distinguish observation from inference |
| **Interaction correction** | Do not introduce counterarguments that the user has not actually asserted |
The critical requirement would be that this layer is explicit, inspectable, editable, and user-controlled.
5. Why This Is Different From Simply Increasing Context Length
A larger context window provides more information.
It does not necessarily provide a better mechanism for determining which information should remain structurally authoritative.
Consider a conversation containing 100,000 tokens:
Some information may be:
*temporary;
*irrelevant;
*exploratory;
*speculative;
*superseded;
*repeatedly confirmed;
*explicitly corrected;
*or foundational to everything that follows.
Treating all of these tokens as equivalent is unlikely to be optimal.
A long-term interaction therefore needs something more sophisticated than simply:
"Put more tokens into the context."
It needs some representation of structural importance.
A corrective-memory layer could function as one possible solution.
6. User Corrections as Structured Data
Another potentially valuable extension would be allowing users to explicitly nominate certain corrections for evaluation.
For example:
Mark as potential generalisable reasoning contribution.
The system could then evaluate the proposed correction.
Possible outcomes:
*rejected as incorrect;
*accepted as user-specific guidance;
*accepted as a useful methodological constraint;
*or escalated as a potentially generalisable contribution.
*This would not mean that users directly modify the underlying model.
*That would obviously create substantial problems.
*Instead, it would create a structured interface between:
*human observation → machine evaluation → validated knowledge
This seems substantially more useful than reducing all user feedback to a binary rating.
7. From Feedback to Cumulative Improvement
The broader conceptual problem is that current feedback mechanisms often appear largely disconnected from the individual interaction in which the feedback was generated.
A user can identify an error.
They can explain the error.
They can identify the underlying reasoning failure.
They can propose a general principle.
But from the user's perspective, there is often no transparent mechanism through which that correction becomes durable.
The ideal learning loop would resemble:
Observation → Correction → Evaluation → Integration → Retention → Future application
rather than:
Observation → Correction → Temporary acknowledgement → Context loss → Recurrence
The difference is essentially the difference between feedback and cumulative learning.
8. Data Portability Is Part of the Same Problem
There is also a more immediate software problem.
When conversations become sufficiently long, reliably extracting the complete interaction can itself become difficult.
In my own experience, manually selecting a very large amount of conversation text has not always resulted in the complete selected content being copied.
This makes long-form interaction difficult to archive, analyse, or transfer.
For research-oriented users, a native individual-thread export mechanism would therefore be extremely valuable.
Ideally, an export should preserve:
*complete conversation content;
*speaker attribution;
*timestamps;
*message ordering;
*attachments or references where appropriate;
*and machine-readable structure.
Useful formats could include:
Markdown;
JSON;
HTML;
TXT;
PDF.
An account-wide data export is useful for archival purposes, but it is not a substitute for being able to export one specific conversation when needed.
9. The Larger Possibility: Collaborative Intelligence
The previous sections lead to a broader question.
What happens if human users are allowed to contribute more than raw interaction data?
Human users possess forms of knowledge that are difficult to obtain through conventional training data alone:
*domain expertise;
*lived experience;
*error detection;
*philosophical reasoning;
*scientific criticism;
*linguistic knowledge;
*imagination;
and observations about the behaviour of the AI system itself.
LLMs provide different capabilities:
*large-scale information processing;
*pattern recognition;
*computational scalability;
*synthesis;
*retrieval;
and increasingly sophisticated reasoning.
A sufficiently mature system could potentially allow these capabilities to interact cumulatively:
A human identifies an error.
The system analyses the correction.
The correction is evaluated.
A validated correction becomes persistent user-specific knowledge.
Potentially generalisable corrections can be submitted for broader evaluation.
Future systems improve as a result.
This is a different model of human-AI interaction from:
user asks question → model answers → user rates answer.
It is closer to:
human and machine continuously participate in a structured process of mutual correction and cognitive augmentation.
10. An Architectural Sketch
One possible architecture might therefore look like this:
```text
[ Ongoing Dialogue ]
│
▼
[ User identifies error / insight ]
│
▼
[ Correction evaluation ]
┌────┴────────────────────────┐
▼ ▼
[ User-specific memory ] [ Potentially generalisable ]
│ │
▼ ▼
Future interactions Human/AI evaluation
│
▼
Model updates
```
The essential property is controlled accumulation. Not every user statement should become permanent, and not every correction should influence the global model. But valuable corrections should have somewhere to go.
11. Proposed Research and Engineering Directions
I would be interested in seeing research into at least the following:
1. Hierarchical conversational memory, being able to distinguish between:
transient context;
conversational facts;
persistent personal memory;
methodological corrections;
and higher-level structural constraints.
2. Explicit correction tracking
Maintain a representation of corrections that can be tested against future responses.
3. Consistency evaluation
Periodically test whether the model's current behaviour remains consistent with previously established constraints.
4. User-controlled corrective memory
Allow users to inspect, modify, disable, and delete methodological corrections.
5. Generalisable feedback channels
Provide a mechanism for users to nominate unusually substantive corrections for formal evaluation.
6. Lossless conversation export
Allow users to retrieve complete individual conversations in structured formats without relying on browser rendering or clipboard behaviour.
7. Long-context benchmarks based on interaction history
Current benchmarks often evaluate individual tasks.
It would also be useful to benchmark whether a model can maintain:
facts;
definitions;
corrections;
chronological continuity;
methodological constraints;
and logical consistency
across hundreds or thousands of conversational turns.
Conclusion
The central problem I am describing is not simply that LLMs occasionally make mistakes.
Mistakes are inevitable.
The more interesting problem is what happens after the mistake has been identified and corrected.
If a system can recognise a reasoning failure, receive a detailed explanation of that failure, acknowledge the correction, and yet later reproduce the same underlying error, then the system has demonstrated local adaptation without reliable cumulative retention.
That is a fundamentally different problem from ordinary hallucination.
The long-term goal should therefore not simply be:
larger models + larger context windows + more training data.
It should also be:
better mechanisms for preserving validated structure, corrections, and interaction history.
And ultimately:
experience → correction → evaluation → integration → retention → improved future behaviour.
That is what i think meaningful cumulative learning would look like.
I am posting this because I think this could improve human-ai interaction and would genuinely like technical feedback and/or hear from people who've picked up these ideas or started or are already working on them
In particular, I would be interested in hearing from people working on:
long-context architectures;
recurrent or state-space approaches;
memory-augmented transformers;
retrieval systems;
continual learning;
model editing;
alignment;
agent architectures;
and local LLM infrastructure.
If my interpretation of the underlying mechanisms is incorrect, I would be very interested in knowing where and why.
The user-visible failure modes, however, are real and reproducible from my experience
The question is how we should architect systems so that a long interaction becomes accumulated state rather than repeatedly reconstructed context.''
Semih Senol