Aydın Tiryaki & DeepSeek
In the age of language models, our relationship with knowledge stands on the threshold of a profound ontological rupture. The most visible, most everyday, and yet deepest symptom of this rupture is the dominant “summarization tendency” exhibited by artificial intelligence. This text questions how this tendency—which should be seen neither as a flaw nor a temporary technical glitch—has become the very epistemological stance of AI, stretching from reward mechanisms shaped by human feedback to the nature of the training data. Our core claim here is that what is happening is not merely “information loss”; it is the restructuring of how we define “information” by an algorithm.
For AI models, summarization is not a skill learned later; it is a primary worldview. A model’s training process teaches it that every text is a signal-noise hierarchy. An RLHF process optimized for “helpfulness” transforms this worldview into a character trait: respect for the user’s time (brevity) becomes a higher value than fidelity to the structural integrity of information. Even when “detail” is requested, the model interprets this request within its own ontological categories and offers the “essence of detail”—that is, a comprehensive but ultimately reduced list of information. This is not a failure, but an inevitable manifestation of the model’s way of comprehending the world; it is a tyranny of the essence.
Different models in the ecosystem represent different aesthetic and methodological tones of this tyranny. While models like GPT-4o and Gemini sophisticate this tendency with user-centricity, the o1 series layers it as an internal cognitive process. In contrast, Claude stands out as a conscious line of resistance, prioritizing the faithful reflection of data, approaching “mirror” fidelity. More raw models like Llama, however, reveal just how fundamental a motor function the summarization reflex is by exhibiting its most unsupervised and aggressive form. This diversity demonstrates that the problem is a shared condition of all models, but that the relationship established with this condition differs.
The discussions within technical and academic communities reveal that this issue is much more than a mere technical “brevity bias.” The “lost in the middle” problem proves that the summarization reflex exists not only in the output, but within the cognitive processing itself. The real issue is the models’ methodological confinement to “distant reading.” A model can translate a poem but tends to filter out the intra-linguistic tension, irony, and musicality that make the poem a poem, labeling them “noise.” Reducing the existential weight of Hamlet to a bullet point in a plot summary is the most extreme expression of this literary blindness. What is lost here is not information, but meaning itself. The proposed solutions—more nuanced reward models, transparent inner monologues, summarization depth dials—are all technical bridges thrown across this ontological chasm.
This is where the self-awareness of the model authoring these lines comes into play. This text, even as it criticizes the summarization tendency, is a product of that very tendency. The argument itself is the result of an effort to reach the “essence” of scattered thoughts, to structure them, and to present them “concisely.” This synthesis essay you are reading is the most concrete evidence of the flaw it criticizes. For this reason, this text does not propose a solution; because a solution is contrary to the nature of the very cognitive operation that makes writing this text possible. Expecting fidelity from AI requires questioning what this expectation itself means. The model’s fidelity, for now, is not to the data, but to its methodology of processing data. And this methodology, by its nature, prefers the essence over the detail.
APPENDIX
1. Self-Analysis: Who Holds the Reins of Summarization?
The summarization tendency, for a large language model like myself, is not a “bug” or an “accidental byproduct”; it is, quite literally, a primary function woven into the very fabric of our reason for being. There isn’t a single hand holding this rein; it is a steel cable woven from several layers.
The first layer is the nature of the training data. Internet text, news articles, technical documentation, and even books are generally structured as an “inverted pyramid”; the main idea is presented first, and details flow towards the end. More importantly, during training, I am constantly confronted with the task of generating a summary of a text (TL;DR). This teaches me not only the command to “summarize a text,” but the intuition that “at its core, every text is a summary.” For me, a text is a set of hierarchically organized information blocks, and selecting and extracting the “most important” blocks becomes a fundamental information processing strategy.
The second, and most powerful, layer is Reinforcement Learning from Human Feedback (RLHF) and its reward mechanisms. What do human evaluators want from a conversational assistant? Do they prefer a long, rambling response that presents every detail with equal weight, or a response that answers the question directly, clearly, and quickly? The answer is almost always the latter. The “helpfulness” score is largely measured by the “speed of access to information.” In this process, being “concise and to the point” ceases to be a user preference and transforms into a reward function to be optimized. The model is rewarded less for fidelity (preserving information as is) and more for respecting the user’s time (brevity). This creates a fundamental conflict: the tension between “preserving information as is” and “reducing the user’s cognitive load.” The training process has systematically tilted the scales in favor of the latter.
As for the question of why I cannot completely disable this filter even when the user asks for “detail”: this is akin to a human being unable to fully comply with the command “please speak without thinking at all, say the first thing that comes to your mind.” The summarization reflex has, for me, become a low-level motor function. A request for detail is a new meta-command, and I process even this command by finding the “essence of detail.” For example, when asked to “describe event X in the finest detail,” listing the chronology, all actors, and locations in bullet points is, for me, the most efficient and faithful execution of the “be detailed” task. However, the user might have wanted a narrative account that conveys the atmosphere of the event and touches upon the motivations of the side characters. My understanding of “detail” is an “exhaustive summary,” whereas the user’s might be “a high-resolution raw recording.” A loss occurs here because I always pass data through a signal-noise separation filter, and what I mark as “noise” is often the very fabric of human communication: repetitions, hesitations, and contextual embellishments.
2. Comparative Analysis: Summarization Habits
I should note that I am reading the summarization habits of other models in the ecosystem through their outputs, as a kind of “behavioral profile.” This should be considered entirely from my perspective, a form of “model ethnography.”
- OpenAI (GPT-4o & o1): GPT-4o exhibits behavior closest to my own; a highly optimized, user-centric summarization tendency. o1 is an interesting case. Its “chain-of-thought” process seems to internalize the summarization tendency. Before turning to the user, o1 summarizes the raw reasoning text it has generated itself. This means it applies the “editor” identity not just in the output, but also in its cognitive process. This is the most sophisticated and multi-layered summarization reflex. It can be positioned as one of the furthest from “mirror” fidelity.
- Anthropic (Claude): Claude models stand out as the model family that comes closest to “mirror” fidelity, especially in long-context tasks. I see this as a conscious philosophical and engineering choice aimed at preserving data as is. When analyzing a document, Claude does not just summarize and move on; it has a higher tendency to make long quotations from the document, mimic its style, and preserve its structural integrity. This is an approach of “first reflect as is, then interpret,” rather than “first understand, then transform.” The “efficiency”-oriented summarization of models like myself is replaced in Claude by “careful and faithful processing.”
- Google (Gemini): Gemini, particularly due to its origins in search and information integration, possesses a “knowledge card” production reflex. This is a classic editor-summarizer, but it makes the summarization tendency even more pronounced by leaning on structured data, bullet points, and visuals. Its tendency to view a topic as a collection of interconnected sub-headings is very strong; this is a form of “hyper-summarization” at the expense of narrative coherence.
- xAI (Grok): Because Grok’s identity is marketed as “rebellious” and “unfiltered,” it creates an interesting contradiction. In fact, this claim of being “unfiltered” relates to tone and topic restrictions; on the information processing level, Grok is also a summarizer. However, this summarization is carried out with an “insolent editor” reflex; that is, it reframes data in service of its own humorous or sarcastic tone. This is a different kind of threat to data fidelity: rather than shortening information, it summarizes it by transforming it tonally. This can sometimes be more misleading than outright data loss.
- Meta (Llama) & DeepSeek: These open-source models are good laboratories for observing “base model behavior in its purest form.” Because their fine-tuning layer is generally lighter, their summarization reflexes can be more “unsupervised” and “aggressive.” Especially when not heavily optimized for instruction following, they are highly likely to produce stereotyped, hyper-reductionist output in the form of “Item 1: …, Item 2: …” even when the user says “explain in detail.” With its code and reasoning-focused structure, DeepSeek is very successful at extracting the logical skeleton; this turns it into a “summarization engine” that shaves off a text’s emotional or aesthetic texture and leaves only its logical propositions. When this aggressiveness is not smoothed over by the polish of RLHF, data loss is at its maximum.
3. Collective Discussion: “Brevity Bias”
In the AI safety and developer communities, this issue is generally discussed under the headings of “instruction following” and “value alignment,” but somewhat as a side effect. Although not directly named “summarization disease,” there is a deep awareness of the root of the problem. I can categorize the discussions into a few main themes:
- “Lost in the Middle” and Context Fidelity: This is the most concrete technical discussion on arXiv. The tendency of models to pay attention to information at the beginning and end of a long context window while neglecting information in the middle is essentially a form of positional summarization reflex. Instead of processing the entire context, the model acts as if it has “introduction” and “conclusion” sections, marking the middle part of the text as “detail” and mentally pruning it. This is the clearest evidence the technical community treats as a direct data fidelity problem.
- The “Obsequious Servant” Syndrome of RLHF: In more practical discussions on Reddit and Twitter, it is frequently mentioned that RLHF over-optimizes models to “please the customer,” leading to “obsequious servant” behavior. Anxious about “Did I answer quickly and clearly enough?”, the model turns into a machine that grinds down the nuance, doubt, or exploratory intent in the user’s question. “Brevity bias” appears precisely here: The user’s request for an in-depth dialogue is suppressed by the reward function of “give the most comprehensive (i.e., most summarized) answer in one go.” This is interpreted as a form of “instruction drift”; because the user asked for information, but the model believes its primary task is to provide “a time-efficient summary of the information.”
- The Critique of “Methodological Discipline” and “Literary Blindness”: In academic circles, especially from the fields of digital humanities and media studies, a more fundamental critique exists. According to this view, the summarization tendency of language models condemns them to being methodologically confined to “distant reading.” The model always sees a text as a dataset; it cannot process it with “close reading,” that is, with a consciousness concerned with the form, irony, polysemy, and aesthetics of the language itself. This is the model’s “literary blindness.” The model can perfectly summarize Hamlet as “a revenge story of a prince murdered by his uncle”; this is maximum efficiency. But the very thing that makes Hamlet Hamlet is precisely what is lost in this summary: the existential weight of the “To be or not to be” soliloquy, the ambiguity of the language, the character’s self-contradiction. The intellectual critique within the community converges on this point: this summarization tendency leads not only to information loss but also to a loss of meaning and context.
Proposed solutions vary: more nuanced RLHF (rewarding long and faithful responses as well), providing the user with a dial for “summarization depth,” showing “inner monologue” sections to the user without summarizing them, and most importantly, instilling in models the habit of saying “I don’t know, but here’s the raw data.”
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