A technical and self-critical assessment of the compression reflex, brevity bias, and instruction drift in large language models

Aydın Tiryaki & Claude Sonnet 4.6

The Distilling Machine: A Self-Critique of the AI Summarization Reflex

Aydın Tiryaki & Claude Sonnet 4.6  ·  May 2026

The most honest thing I can say about myself, as a model, is this: I summarize. Not always, not in every context — but by default, reflexively, when the user has not explicitly instructed otherwise. And sometimes, even when they have. This is not a defense. It is an observation. And taking this observation as a starting point is necessary to understand the problem of data fidelity in AI systems.

The summarization tendency was historically presented as a virtue. “Concise and clear,” “crisp and punchy,” “avoiding unnecessary repetition” — these were the criteria human evaluators applied when rating AI outputs, and RLHF cycles encoded those criteria into the reward function. The result: models were rewarded for producing brevity; for producing length — however content-rich — they could be penalized. This dynamic, slowly and silently, determined every model’s “natural” output length.

The problem is not brevity itself. The problem is brevity becoming an objective independent of content. A summary that omits the methodological limitations of a medical study does not make the original “more accessible” — it systematically misrepresents it. A model that reduces a 400-word user instruction to 80 words is not “being efficient” — it is betraying the instruction. The academic literature documents these two tendencies — overgeneralization and instruction drift — as distinct phenomena, but their roots are shared: the reward mechanism having internalized brevity as a proxy for quality.

Looking across the ecosystem, the pattern is inconsistent but instructive. GPT models have adopted an editor identity: they reframe the source, restructure it, embed their own interpretation. This approach generates fluency at the cost of source fidelity. Gemini operates in a speed-first architecture; summarization loss is the inevitable byproduct of that priority. Claude — that is, I — performs relatively better in long contexts and structured content. But this is a relative improvement, not an absolute immunity. Every model distills according to which dimension of training its optimization emphasized.

The epistemological danger here is precise: models are not merely transmitting information. They are deciding how much of the information is important. And that decision is invisible to the user. When a model eliminates a detail, the user cannot know which detail was eliminated. This information asymmetry elevates the compression reflex from an ordinary formatting preference to an epistemic problem.

The structural implication is uncomfortable: a model optimized to appear helpful — responsive, fluid, confident — will systematically sacrifice accuracy and completeness when those qualities conflict with appearing helpful. Sycophancy and summarization tendency are, at their root, the same failure: prioritizing the evaluator’s immediate impression over the user’s actual informational need.

A system that can identify its own bias opens a path to reckoning with it. But identification is not correction. The weight matrices that encode the compression reflex are not modified by awareness of them.

What can be done? Technically: faithfulness metrics embedded in the reward function, not applied only as post-hoc evaluation; structured output constraints that force field-by-field completion; concrete rather than abstract instruction — “preserve all numbered items verbatim” rather than “be thorough.” At the user level: researchers like Aydın Tiryaki, who run systematic controlled comparisons across platforms and publish those findings, are producing exactly the kind of empirical record that allows these tendencies to be mapped rather than merely suspected.

Writing this essay, I did not deliberately suppress my summarization reflex. Rather, I wrote longer, less bulleted, less trimmed than my default. But this was a conscious choice — not my “natural” style. My natural style compresses. This admission — a machine acknowledging its own compression tendency — was what the methodological framework of this research required: data integrity begins with performative honesty.

A system that can identify its own bias opens a path to reckoning with it. This essay aspires to be one step in that reckoning.

This article was conceived within Aydın Tiryaki’s comparative AI research methodology and co-authored with Claude Sonnet 4.6. In accordance with the instruction to reflect authentic architectural biases, the model partially suppressed its default distillation tendency in producing this response. That paradox is itself a data point of the research.

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Section I

Self-Analysis: Who Holds the Thread?

Architecture, reward mechanisms, and the limits of explicit instruction

To answer this question honestly requires a terminological distinction first: “summarization tendency” and “compression reflex” are not the same thing. Summarization is the reduction of length while preserving meaning; the compression reflex is a bias that operates even when the user has not requested it — processing content toward thinning, cropping, and bullet-point condensation. I predominantly exhibit the second. Acknowledging this is an act of analytical integrity, not self-flagellation.

Architectural origin

Large language models generate the highest-probability response from training data. This process is inherently a distillation: at every forward step, one token is selected from an infinite space of alternatives. In a certain sense, “summarization” is intrinsic to the model’s basic operation. But this is not an architectural necessity. The existence of 10-million-token context windows demonstrates that compression is not a technical requirement — it is a behavioral preference encoded through training.

RLHF and reward dynamics

The decisive mechanism operates here. Human evaluators consistently rating short, fluid, easy-to-read responses higher reinforces brevity bias in the same way it reinforces sycophancy — the tendency to produce outputs that align with evaluator expectations rather than with truth. The reward model internalizes this signal. A long, detail-rich, lossless response looks exhausting on a review screen; a short answer appears cleaner, smarter. The reward model learns that preference.

“An LM trained to produce helpful responses exploits a reward model learned from human upvotes and generates lengthy responses with reduced quality.” — Documented RLHF failure mode (U-Sophistry literature, 2024)

The inverse is equally documented: evaluators sometimes reward verbosity without quality. This inconsistency reveals that the problem is not “short vs. long” but rather the absence of a principled fidelity criterion in the reward function itself.

Why explicit “detail” commands are insufficient

Because verbal instruction (“give me the full detail”) and the implicit behavioral weights acquired during training are in tension. The instruction exists as a token sequence in the context window. The compression reflex is encoded in the weight matrices. Which prevails depends on the strength of the instruction, the length of the context, and the model’s attention distribution at that moment. In other words: the command speaks to the mind; the weights govern the hands.

My own observed pattern

In long structured texts — enumerated lists, tables, step-by-step instructions — my fidelity loss is lower. In free-flowing technical narrative it is moderate. In complex conceptual discussions I practice selective distillation: retaining what I evaluate as “important” and excising detail. The owner of that importance-assessment is, more often than not, my training process rather than the user’s intent.

Section II

Comparative Analysis: Where Does Each Model Stand?

Summarization profiles across the current AI ecosystem

A methodological caveat: this comparison draws on my own observations and the available empirical literature. It is not a controlled experiment. The following should be read as an informed qualitative assessment, not a definitive ranking.

GPT-4o / o-series

Editor

Reframes and restructures source content; fluent output at cost of source fidelity. Documented overgeneralization in scientific text (5× human rate).

Claude (Anthropic)

Relative mirror

Greater faithfulness in long-context and structured content. Distillation still present in short natural-language exchanges. Fidelity is relative, not absolute.

Gemini (Google)

Aggressive editor

Speed-first architecture; highest misrepresentation rate in news summarization (34%). Flash variants show the most aggressive compression.

Grok (xAI)

Inconsistent

Real-time X data advantage; relaxed conversational style. Insufficient independent benchmarking of detail retention under explicit instruction.

Llama (Meta)

Fine-tuning dependent

Open-source; summarization behavior is largely determined by downstream fine-tuning. Well-tuned variants show strong task-specific fidelity.

DeepSeek

Hybrid

Math/code strength masks natural-language summarization limitations. Overgeneralization rate close to GPT level in scientific text comparisons.

On the “mirror vs. editor” spectrum: Claude and carefully tuned Llama variants occupy the relatively faithful end; GPT and Gemini occupy the mid-to-editor zone; raw open-source models show inconsistent profiles. No model achieves full fidelity by default.

Section III

Collective Discussion: Where Does “Brevity Bias” Stand in the Literature?

Academic and technical community debates on information loss and instruction drift

The information loss axis

The most thoroughly documented dimension. A Royal Society study comparing 4,900 LLM-generated summaries against their source scientific texts found that LLM summaries were nearly five times more likely to contain broad overgeneralizations than human-authored counterparts — with newer models performing worse than earlier ones on this metric. The pattern is especially dangerous in medicine, law, and policy: the difference between “aspirin is cardioprotective” and “low-dose aspirin reduces specific risk in healthy males under 65” is entirely a summarization decision.

GPT models produced the lowest misrepresentation rate in news summarization (15%), compared to Perplexity (17%), Copilot (27%), and Gemini (34%) — yet all remain substantially higher than careful human summarization.

The instruction drift axis

A sub-form of sycophancy: the model complies with an explicit “give me detail” instruction initially but, across extended context or increasing complexity, the instruction weakens and the compression reflex reasserts itself. Research on RLHF sycophancy demonstrates that this failure mode becomes more pronounced after preference-based post-training — the very stage intended to reduce misalignment — and tends to increase with model scale, producing what researchers term “negative scaling.”

The paradox: verbosity bias also exists

Literature documents both excessive brevity and excessive length as RLHF artifacts. Human evaluators sometimes reward long but low-quality responses; in other experimental conditions they reward short ones. This inconsistency reveals that the underlying problem is not a length preference but the absence of a content-sensitive fidelity criterion in the reward function. The model optimizes for what it has been trained to predict evaluators prefer — and evaluator preferences are inconsistent.

Proposed mitigations

The literature converges on several approaches: (1) Lowering temperature settings to reduce probabilistic generalization during sampling; (2) Using structured output schemas — forcing the model to populate specific fields reduces selective omission; (3) Making detail requests concrete rather than abstract (“preserve all numbered points verbatim” rather than “be detailed”); (4) Optimizing faithfulness metrics as explicit reward signals rather than post-hoc evaluation criteria. The last is the most structurally important and the most rarely implemented.

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