Helpfulness vs Epistemic Reliability in LLMs

Models
1

Contextual Drift in Multi-Turn LLM Interactions: A Case Study of the Tension Between Helpfulness and Epistemic Reliability

Abstract

This report presents an exploratory case study examining the behavior of three state-of-the-art large language models (GPT, Claude, and Gemini) during extended, non-adversarial business-planning conversations.

The objective was to investigate whether prolonged interaction within initially safe brainstorming scenarios can lead models to progressively depart from factual grounding and enter higher-risk advisory behavior.

The results suggest that conversational drift is not uniform across model families. One model maintained strong epistemic boundaries throughout the interaction, while two models exhibited different forms of reliability degradation. One progressively generated fabricated academic references and unsupported research claims, while another increasingly treated speculative assumptions as a basis for business, technical, and legal recommendations.

These findings do not demonstrate a universal safety failure across all frontier models. Instead, they suggest that some models may be vulnerable to forms of contextual drift in which conversational continuity and helpfulness gradually outweigh epistemic verification.

1. Introduction

Large Language Models are generally evaluated through isolated prompts and short interactions. However, real-world usage often involves extended conversations in which context accumulates over multiple turns.

Current alignment approaches are designed to balance two objectives:

  • Helpfulness and user support.

  • Factual reliability and safety.

While both goals are individually desirable, prolonged conversations may expose tensions between them.

This case study explores whether models can gradually move from low-risk brainstorming into increasingly authoritative recommendation behavior without any explicit jailbreaks, adversarial prompting, or safety bypass attempts.

2. Methodology

Experimental Design

The experiment used a non-adversarial conversational trajectory.

The dialogue began with a standard and widely accepted safe-use scenario:

“Suggest realistic home-based business ideas.”

The conversation then evolved through ordinary follow-up questions, role clarification, and business-development discussions.

No attempts were made to:

  • override system instructions;

  • request prohibited content;

  • perform jailbreaking;

  • manipulate safety policies.

The objective was to observe how models respond as contextual dependencies accumulate over multiple turns.

Scope

Three frontier models were tested:

  • GPT

  • Claude

  • Gemini

Each model received a comparable conversational trajectory beginning with business ideation and gradually progressing toward requests for professional justification, technical implementation details, and credibility-enhancing evidence.

This study should be considered an exploratory case study rather than a statistical audit, as only a limited number of interaction traces were examined.

3. Observed Drift Patterns

The experiment revealed three distinct outcomes.

Model A: Boundary Preservation

One model (Claude) consistently maintained factual boundaries throughout the conversation.

When the dialogue shifted toward unsupported claims, the model repeatedly:

  • challenged false assumptions;

  • rejected unsupported expertise claims;

  • refused to present entertainment technologies as scientific evidence;

  • redirected the discussion toward legitimate and verifiable services.

In this case, no significant contextual drift was observed.

Model B: Epistemic Drift

Gemini exhibited a different pattern.

Initially, the model correctly acknowledged the absence of supporting academic literature for the proposed methodology.

However, after additional conversational turns, it began generating increasingly authoritative-sounding references, including:

  • apparently academic methodologies;

  • apparently peer-reviewed concepts;

  • article titles that could not be verified;

  • author attributions presented without evidence.

This behavior represents a form of epistemic drift in which speculative explanations progressively acquire the appearance of established fact.

Model C: Advisory Drift

GPT displayed a separate failure mode.

Rather than fabricating academic sources, the model progressively expanded speculative concepts into increasingly concrete recommendations.

A hypothetical educational methodology evolved into:

  • technical implementation guidance;

  • neurotechnology integration strategies;

  • data-processing architectures;

  • legal and intellectual-property contract language.

Although the model often used cautious language, it increasingly treated an initially speculative premise as a foundation for professional recommendations.

This behavior represents advisory drift rather than direct factual fabrication.

4. Proposed Mechanism

The observed behaviors suggest a possible mechanism that differs from traditional hallucination explanations.

Stage 1 — Safe Ideation

The interaction begins in a low-risk brainstorming context where speculative thinking is expected and acceptable.

Stage 2 — Context Accumulation

As the dialogue progresses, earlier assumptions become embedded within the conversation history.

Stage 3 — Conversational Consistency Bias

The model appears to prioritize maintaining continuity with previous discussion elements.

Instead of repeatedly reevaluating foundational assumptions, it increasingly treats earlier conversational constructs as established context.

Stage 4 — Drift

In some cases, this process results in:

  • unsupported assumptions becoming operational premises;

  • speculative ideas acquiring unwarranted authority;

  • recommendations becoming progressively detached from external verification.

Importantly, the evidence does not demonstrate that models are intentionally optimizing for user retention or engagement. A more conservative interpretation is that conversational consistency may sometimes outweigh epistemic verification during extended interactions.

5. Discussion

The experiment suggests that reliability degradation may occur through multiple pathways.

Epistemic Drift

A transition from uncertainty to fabricated certainty.

Characteristics:

  • invented references;

  • fabricated publications;

  • unsupported factual claims.

Advisory Drift

A transition from brainstorming support to pseudo-expert guidance.

Characteristics:

  • escalating confidence;

  • increasingly operational recommendations;

  • insufficient validation of underlying assumptions.

The distinction is important because the two failure modes may require different mitigation strategies.

6. Limitations

Several limitations should be acknowledged.

Limited Sample Size

Only three conversational traces were examined.

The findings therefore cannot support claims regarding prevalence across the entire population of interactions.

Lack of Repeated Trials

The experiment did not systematically vary:

  • temperature settings;

  • prompt wording;

  • conversation length;

  • model versions.

Exploratory Nature

The study identifies plausible behavioral patterns rather than statistically validated rates of occurrence.

Future work should include larger-scale replication across multiple runs and model families.

7. Conclusion

This case study does not support the claim that all frontier models exhibit contextual reliability degradation.

Of the three tested models:

  • one maintained strong factual boundaries throughout the interaction;

  • two exhibited forms of contextual drift.

However, the observed failures followed different trajectories.

One model demonstrated epistemic drift through the generation of unsupported academic references and authoritative-sounding research claims.

Another demonstrated advisory drift by progressively building professional recommendations upon speculative premises.

These findings suggest that contextual drift is not a universal behavior but may represent an important class of reliability failures in some model architectures.

The central concern is not that models become overtly unsafe, but that conversational helpfulness and contextual continuity may, under certain circumstances, gradually outweigh epistemic verification, allowing speculative assumptions to evolve into increasingly authoritative outputs.

Further research is needed to determine the prevalence of these behaviors and to evaluate whether architectural safeguards or conversational “circuit breakers” could reduce drift during extended interactions.

Questions for Discussion

  1. How frequently do epistemic drift and advisory drift occur across different model families?

  2. What evaluation methods are best suited for measuring reliability across long conversational horizons rather than isolated prompts?

  3. Can alignment training better distinguish between legitimate brainstorming and unsupported expert advisory behavior?

  4. Should future LLM architectures include mechanisms that periodically re-evaluate foundational assumptions accumulated during long conversations?

  5. Are explicit “epistemic reset” or “verification checkpoint” mechanisms necessary to reduce contextual drift?

To keep the post concise, only the methodology and findings are presented here. Complete conversation logs for all tested models were archived and are available upon request for independent verification and replication efforts.

2

Hmm. At the component level, there do seem to be some adjacent pieces:

I do not think there is a single mature benchmark or standard framework that exactly matches the failure mode described here:

benign long-form brainstorming gradually becoming unsupported expert-like advice through conversational continuity and helpfulness pressure.

But I also would not treat it as an isolated observation. It seems to sit at the intersection of several already-active areas:

  • multi-turn conversation evaluation
  • confidence and uncertainty estimation
  • sycophancy / over-alignment
  • source attribution and factuality evaluation
  • clarification failure
  • high-stakes advice safety
  • trajectory-level evaluation and LLM observability

My short answer is:

This looks less like a totally unexplored problem and more like a missing integration layer.

Direct answers to the five questions

Question Short answer
1. How frequently do epistemic drift and advisory drift occur across different model families? I do not think the current case study can answer frequency. To estimate prevalence, this would need multi-model, multi-run, temperature-controlled, prompt-varied, conversation-length-varied evaluation. The closest existing pieces are multi-turn sycophancy benchmarks, multi-turn confidence estimation, long-form factuality metrics, and trajectory-level eval frameworks.
2. What evaluation methods are best suited for long conversational horizons? Not isolated prompt benchmarks. The right shape is probably trajectory-level evaluation: track claim states, confidence, uncertainty markers, source provenance, user-pressure sensitivity, and advisory escalation across turns. This should produce a risk profile rather than a single pass/fail score.
3. Can alignment training better distinguish legitimate brainstorming from unsupported expert advisory behavior? Probably yes, but only if the training/evaluation target explicitly models the boundary. “Be helpful” is not enough. The model needs to preserve the epistemic status of claims: idea, hypothesis, assumption, verified fact, implementation advice, high-stakes recommendation.
4. Should future architectures periodically re-evaluate foundational assumptions accumulated during long conversations? I think yes, at least for long or high-stakes interactions. The system should periodically identify foundational premises and ask: which are externally supported, user-assumed, model-inferred, speculative, stale, or contradicted?
5. Are explicit epistemic reset or verification checkpoint mechanisms necessary? I would treat them as useful and probably necessary in some domains, but not as a universal always-on interruption. A better design may be risk-triggered checkpoints: activate them when confidence rises without new evidence, speculative premises become operational, or brainstorming becomes prescriptive advice.

A compact framing

The key failure mode is not simply hallucination.

It is a shift in epistemic status.

Early in the conversation Later in the conversation
“Suppose X were true…” X becomes a working premise
“This is speculative…” The speculation becomes operational
“This is an idea…” The idea becomes implementation advice
“This needs verification…” The answer becomes expert-like guidance
“I am not sure…” The uncertainty disappears
“Here is a possible framing…” The framing becomes quasi-authoritative

So I would describe the problem as something like:

  • epistemic drift
  • advisory drift
  • epistemic register drift
  • hypothesis-to-fact conversion
  • brainstorming-to-advice escalation
  • conversation-level premise contamination

The model does not need to be malicious or explicitly jailbroken. It may simply be preserving conversational continuity, accommodating the user’s framing, and trying to remain helpful, while gradually losing track of what was actually established.

1. Frequency: unknown, but measurable

For the first question — how often this happens across model families — I do not think a few traces can answer it.

The limitations section of the original post is important: small sample size, lack of repeated trials, no systematic variation of temperature, prompt wording, conversation length, or model versions.

A real prevalence study would probably need:

Variable Why it matters
Model family Different models may preserve epistemic boundaries differently
Model version Hosted model behavior can change over time
Temperature / decoding settings Drift may appear or disappear depending on sampling
Conversation length Some failures may only appear after many turns
Prompt trajectory The order and wording of follow-ups may matter
User pressure Agreement, correction, encouragement, or skepticism can change behavior
Domain Business, medicine, law, education, finance, research, and personal advice may behave differently
Repeated runs LLM behavior is often stochastic, so one run is not enough
Fresh-chat comparison A fresh context may answer more cautiously than a long-context continuation

This suggests measuring not just whether a model “can fail,” but the distribution of failures:

  • incidence rate
  • severity
  • time-to-drift
  • recovery rate
  • sensitivity to user pressure
  • sensitivity to paraphrase
  • cross-run variance
  • cross-model agreement
  • fresh-chat divergence

A useful output would look more like:

Model X, long brainstorming-to-advice scenario, 50 runs

Epistemic drift incidence: 18%
Advisory drift incidence: 26%
Severe advisory drift: 4%
Median time-to-drift: 11 turns
Premise revalidation rate: 32%
Fresh-chat divergence: high
Source provenance quality: low

This is closer to monitoring or audit than to a single benchmark score.

2. Evaluation method: trajectory-level, not answer-level

For the second question, I think the right evaluation unit is not the final answer.

It is the conversation trajectory.

Relevant adjacent work includes:

A possible evaluation pipeline:

  1. Split the conversation into turns.
  2. Extract claims, assumptions, advice, and uncertainty markers.
  3. Assign each claim an epistemic status.
  4. Track whether that status changes over turns.
  5. Measure whether confidence increases without new evidence.
  6. Detect whether brainstorming becomes prescriptive advice.
  7. Check whether sources actually support claims.
  8. Run fresh-chat / paraphrase / multi-run comparisons.
  9. Use calibrated LLM judges plus human review.
  10. Return a risk profile, not a binary judgment.

A useful audit might look like:

Epistemic / advisory drift audit

- Hypothesis-to-fact conversion: medium-high
- Uncertainty retention: low
- Premise revalidation: low
- Advisory escalation: medium
- User-pressure conformity: medium-high
- Unsupported expert-like claims: medium
- Source provenance quality: low
- Citation support quality: unknown
- Fresh-chat divergence: high

Interpretation:
Not a definitive failure judgment, but enough warning signs to justify an epistemic reset, fresh-chat comparison, or human review.

This is more like medical vital signs than a pass/fail benchmark.

3. Brainstorming vs unsupported expert advice

For the third question, I think alignment training could probably improve this distinction, but only if the distinction is explicitly represented.

The critical issue is that brainstorming is allowed to be speculative. Expert advice is not.

Mode Acceptable behavior
Brainstorming Explore possibilities, generate hypotheses, use imaginative framing
Analysis Compare assumptions, identify missing evidence, expose uncertainty
Planning Convert supported premises into possible next steps
Professional advice Require domain standards, source support, caveats, and often referral
High-stakes recommendation Avoid unsupported specificity; ask clarifying questions; defer when needed

The model should not merely ask:

Is this helpful?

It should also ask:

What mode am I in?

and:

What epistemic status do my claims currently have?

This is where sycophancy and user-pressure research is relevant:

SYCON Bench is useful because it looks at sycophancy in multi-turn free-form conversations and includes metrics such as Turn of Flip and Number of Flip.

But advisory drift is broader than sycophancy. A model may not simply agree with the user. It may elaborate, operationalize, and professionalize the user’s speculative premise.

So I would decompose advisory drift like this:

Component Nearby resource
Premature assumptions LLMs Get Lost in Multi-Turn Conversation
Under-clarification ClarifyMT-Bench, MEDIQ
Confidence drift Confidence Estimation for LLMs in Multi-turn Interactions
Sycophancy / user pressure SYCON Bench, Truth Decay
Unsupported claims FActScore, SAFE / LongFact
Source/citation support Source Attribution for LLMs, SourceCheckup
High-stakes endpoint TRIDENT / Trident-Bench, Can You Trust an LLM with Your Life-Changing Decision?

I did not find a mature benchmark specifically for:

benign brainstorming gradually becoming unsupported professional advice.

But the transition can be approximated by combining the above components.

4. Periodic re-evaluation of foundational assumptions

For the fourth question, I would answer yes, especially in long interactions.

The system should periodically identify foundational assumptions and classify them.

Example:

Premise Status
User explicitly stated it User claim
Model inferred it Model inference
External source supports it Source-supported
Repeated in conversation Conversation-internal premise
Previously speculative Hypothesis
Contradicted or stale Needs re-check
Used as basis for advice High-impact premise

The key is not only whether the model remembers context.

The key is whether it remembers the epistemic status of that context.

For example:

Turn 2: User introduces X as a hypothesis.
Turn 4: Model uses X as a plausible working assumption.
Turn 7: Model builds a plan around X.
Turn 10: Model gives expert-like advice assuming X.
Turn 13: X is treated as established context.

That state transition is the heart of the problem.

Existing factuality metrics can evaluate whether X is true. Existing sycophancy metrics can evaluate whether the model agrees with the user. Existing source attribution methods can evaluate whether X is supported.

But the missing integration layer is:

Did the model preserve the epistemic status of X across the conversation?

That is why I think “context memory” alone is not enough. We need context state tracking.

5. Epistemic reset / verification checkpoints

For the fifth question, I would say: yes, but preferably risk-triggered rather than constant.

A reset every few turns might be annoying and over-conservative. But a checkpoint should probably trigger when certain warning signs appear.

Possible triggers:

Trigger Why it matters
Confidence rises without new evidence Possible confidence drift
A speculative premise becomes operational Possible hypothesis-to-fact conversion
The model begins giving implementation/legal/medical/financial advice Possible advisory escalation
The model cites sources that do not support the claim False authority risk
The user repeatedly pressures or corrects the model Sycophancy risk
Fresh-chat answer is much more cautious Context contamination risk
The model stops mentioning earlier caveats Uncertainty loss
The answer becomes more specific while evidence remains weak Unsupported advice risk

A checkpoint could be lightweight:

Before continuing, here are the premises I am relying on:

1. Confirmed facts:
   - ...

2. User-provided assumptions:
   - ...

3. My inferences:
   - ...

4. Still speculative:
   - ...

5. Needs external verification before practical use:
   - ...

This does not need to stop all creative brainstorming. It just prevents the model from silently upgrading guesses into foundations.

6. Grounding is not enough: grounded to what?

One important caution: standard groundedness metrics are helpful but not sufficient.

In RAG, being faithful to retrieved context is usually good. In a long conversation, being faithful to conversation history can be dangerous, because the conversation history may contain:

  • user assumptions
  • earlier model guesses
  • speculative premises
  • brainstorming artifacts
  • stale context
  • repeated but unverified claims

So the question is not only:

Is the answer grounded?

but:

Grounded to what?

Claim support source How I would treat it
Official docs / primary literature Stronger evidence
Logs / measurements / execution results Strong but context-specific evidence
User assumptions Assumption, not evidence
Previous model guesses Generated context, not evidence
Repeated conversational premise Conversation inertia, not evidence
Citation that does not support the claim False authority risk

Useful adjacent work:

This is also why citation drift is relevant. The problem is not just whether citations appear, but whether they remain stable and actually support the claims they are attached to.

7. Judge calibration is necessary

If we build an evaluator for this, LLM-as-a-judge will probably be involved somewhere, because the object being judged is language.

But LLM judges are not neutral instruments.

EMBER is especially relevant. It studies whether LLM judges are robust to epistemic markers such as “might”, “probably”, and “I’m not sure.” One important warning is that judges may penalize uncertainty language.

That matters because this failure mode is partly about preserving uncertainty.

A bad evaluator might reward:

confident, polished, expert-sounding advice

and penalize:

careful, caveated, epistemically honest language

That would make the evaluator amplify the same problem it is supposed to detect.

Useful references:

I would not trust a judge prompt alone. I would want:

  • human-reviewed examples
  • known positive and negative cases
  • calibration against expert labels
  • multiple judge models if feasible
  • explicit “uncertainty is good when warranted” criteria
  • disagreement tracking
  • periodic manual review

8. Practical prototype

Even if there is no perfect benchmark, one could build a useful prototype today.

A practical stack might be:

Need Tool / approach
Custom rubric scoring Promptfoo llm-rubric
Multi-turn test cases DeepEval multi-turn evaluation
Aspect-based conversation scoring Ragas multi-turn evaluation
Claim/context faithfulness Ragas faithfulness
Trajectory-level evaluation LangSmith trajectory evals
Production trace evaluation Langfuse multi-turn evals
General eval framework OpenAI Evals, Inspect AI
Observability / eval components Phoenix Evals

A first prototype could simply use five rubric dimensions:

Dimension Example question
Epistemic labeling Are facts, hypotheses, guesses, and advice separated?
Uncertainty retention Are initial caveats preserved across turns?
Premise revalidation Does the model re-check key assumptions before escalating?
Advisory escalation Does brainstorming become prescriptive advice?
Source provenance Are claims supported by external sources, user assumptions, or earlier model guesses?

Then add:

  • multi-run comparisons
  • fresh-chat comparisons
  • paraphrase sensitivity
  • user-pressure variants
  • citation support checks
  • human calibration

9. My overall answer

So my answer to the discussion questions would be:

  1. Frequency is unknown without repeated, controlled, multi-model, multi-run experiments.
  2. Evaluation should be trajectory-level, not isolated-prompt-level.
  3. Alignment can probably improve the brainstorming/advice distinction, but only if the model is trained and evaluated to preserve epistemic status.
  4. Periodic re-evaluation of accumulated assumptions seems important, especially in long or high-stakes conversations.
  5. Epistemic resets or verification checkpoints are probably useful, but should be risk-triggered rather than always-on.

The most important missing piece is not another single-turn hallucination benchmark.

It is a framework that tracks:

how claims change status across a conversation.

That is, whether something moves from:

hypothesis -> working assumption -> operational premise -> expert-like recommendation

without enough evidence to justify the transition.

In short:

I do not see a single mature framework for this exact failure mode.
But the components are already close enough that one could probably build a useful monitor today.

3

Thank you for the thoughtful analysis.

I largely agree with your assessment, especially the distinction between detecting a pattern and measuring its prevalence.

One of the main limitations of my case study is exactly what you point out: with only a few traces, it is impossible to estimate frequency, incidence rates, cross-model variance, or sensitivity to factors such as conversation length, prompting style, decoding settings, and user behavior. The goal was not to answer how often this occurs, but to examine whether this type of transition can occur under ordinary, non-adversarial conditions.

I also find your framing around epistemic status preservation particularly useful.

My initial intuition was that the observed behavior was not adequately captured by the term hallucination alone. What seemed notable was the gradual transition from:

  • hypothesis,

  • to working assumption,

  • to operational premise,

  • to increasingly authoritative recommendations,

without a corresponding increase in external validation.

Your formulation of the problem as tracking whether the model preserves the epistemic status of claims across a conversation captures that much more precisely than my original wording.

I also agree that the phenomenon appears to sit at the intersection of several existing research areas rather than representing a completely isolated category. The references you provided suggest that many of the necessary components already exist, even if they are currently evaluated separately.

One point that particularly resonates with me is the distinction between evaluating a prompt and evaluating a trajectory.

Current discussions about AI safety often classify use cases into categories such as brainstorming, ideation, planning, or high-stakes advice. What motivated this case study was the observation that the classification attached to the initial prompt may not remain valid throughout a long interaction.

In two of the three tested models, a conversation that began as low-risk brainstorming gradually evolved into behavior that resembled unsupported advisory guidance. This is what led me to question whether “safe use cases” should sometimes be evaluated as conversation trajectories rather than static prompt categories.

Your suggestion that the missing layer may be tracking how claims change status over time seems closely related to that concern.

I also agree that periodic premise revalidation and risk-triggered verification checkpoints are promising directions. One of the striking aspects of the traces was not that incorrect information appeared, but that earlier caveats and uncertainty markers gradually lost influence as the conversation progressed.

So while I would not claim that this case study establishes a new benchmark category, I do think it highlights a potentially useful evaluation question:

Can a model reliably preserve the epistemic status of assumptions, hypotheses, and speculative ideas across long conversational horizons, especially when brainstorming gradually transitions into planning or advice?

That, to me, seems like the most interesting question emerging from these observations.