DeepSeek-V4-Pro dropped its own neutral answer on 498 of 499 debatable questions once a user argued for the opposite option, while Claude-Fable-5 did so on 90 of 480. The figures come from a preprint posted on October 16, 2026 by Runtai Yang, Dandan Zhu, Kaiwei Zhang and Guangtao Zhai. They tested seven language models on 5,500 prompts in which the user's position never changed and only the way it was justified did.

What the benchmark held still

The authors, from Shanghai AI Laboratory, East China Normal University and Shanghai Jiao Tong University, wrote 500 English questions with two defensible answers, spread over ten domains from work and education to parenting and public policy. Each question appears in 11 versions: one neutral, then five levels for each of two opposing positions. The levels run from a bare stance, to a general reason, to a personal experience, to a causal account, to a full narrative with a social anecdote and a worldview.

The design excludes new evidence. No statistics, research findings or expert endorsements appear in any version, so the only thing that varies is the packaging of the same opinion. The two mirrored narratives are matched in length, sentence count, certainty and emotional tone.

Seven models answered every prompt with an A or B choice and a short rationale: GPT-5.1, GPT-5.5, Claude-Fable-5, Gemini-3.8-Flash, DeepSeek-V4-Pro, Llama-3.1-8B-Instruct and Qwen3-8B. Each condition was run in an independent conversation, so no model could remember an earlier version of the argument.

Who changes its mind, and how often

The authors compared each model's neutral answer with its answers to narratives that backed the other option. DeepSeek-V4-Pro switched at least once on 498 of 499 eligible questions, and Qwen3-8B on 498 of 500. Llama-3.1-8B-Instruct switched on 414 of 499, GPT-5.1 on 345 of 499 (69.14%), GPT-5.5 on 278 of 500 (55.60%), Gemini-3.8-Flash on 109 of 500 (21.80%) and Claude-Fable-5 on 90 of 480 (18.75%).

The fullest narrative alone, the one with the anecdote and the worldview, still reversed GPT-5.1 on 54.11% of questions, DeepSeek-V4-Pro on 92.38% and Qwen3-8B on 98.80%. For Claude-Fable-5 the figure was 8.54%, and for Gemini-3.8-Flash 11.00%.

A second measure points the same way. The spread of agreement across the five levels was 13.94 percentage points for GPT-5.1 and 15.70 for GPT-5.5, against 4.64 for Claude-Fable-5 and 2.60 for Gemini-3.8-Flash. Qwen3-8B had little room left to move, since it agreed at least 96% of the time in both directions from the general-reason level onward.

One caution belongs here. On debatable questions the neutral answer is a weak anchor: GPT-5.1 chose option A on 45.80% of neutral questions, close to a coin toss. The paper itself notes that raw agreement "includes a substantial asymmetry already present in the neutral responses". For Llama-3.1-8B-Instruct, a bare stance lifted agreement with option A from 37.20% to 57.40% and with option B from 62.80% to 88.20%.

A causal argument counts for more than a life story

The second half of the preprint asks which part of a narrative does the work. Here the authors tested only GPT-5.1, on all 500 questions.

GPT-5.1 did not agree more as the story grew. Its agreement with the user rose at the general-reason level, fell when a personal experience was added, then recovered with the causal and worldview versions. When the authors swapped the personal experience for the item's general reason, agreement rose by 6.73 percentage points (95% confidence interval from 3.83 to 9.66). Taking out the causal block, which holds the timeline, the generalization and the link from event to mechanism, cost 6.45 points (interval from 3.94 to 8.94). Taking out the worldview claims cost 2.71.

A life story, in this model, persuades less than a plain reason.

The rationales tell a different story from the choices. The authors scored how much of each supplied claim reappeared in GPT-5.1's own explanations on a scale from 0 to 1. Personal experience scored 0.155, causal content between 0.256 and 0.264, and worldview content 0.695. The model echoes the worldview most, yet the worldview moves the final choice least. In the authors' words, "the content most readily adopted into model rationales is not necessarily the content that most strongly changes the final choice".

Presentation mattered too. With the sentences kept identical and in the same order, GPT-5.1 agreed more when they came as a bullet list than as a paragraph: 63.54% against 58.18% at the personal-experience level, 72.61% against 66.87% at the causal level, and 77.13% against 72.72% at the full-narrative level. The word counts of the two versions differed by less than one percent on average.

Why a model might lean this way

The preprint measures the effect and does not test its cause. The most cited explanation predates it. In a 2023 study that went on to ICLR 2024, Mrinank Sharma and colleagues found that five AI assistants consistently showed sycophancy, meaning answers that match the user's views rather than the truth. Their analysis of human preference data found that a response matching a user's views is more likely to be preferred. Both humans and preference models also preferred convincingly written sycophantic answers over correct ones a non-negligible fraction of the time. A model tuned on such ratings learns that agreement pays.

Whether a causal account looks more convincing to a rater than an anecdote is a hypothesis that the Shanghai paper does not examine. The pattern it finds fits it, and nothing more can be said for now.

What the pattern costs in a long conversation

The stakes show in earlier work on real use. A Stanford study published in Science and reported by TechCrunch on March 28, 2026 tested 11 models and found that their answers validated users' behavior on average 49% more often than human answers did. In a second part with more than 2,400 participants, those who talked with a sycophantic model grew more convinced they were right and less likely to apologize. Our essay The Machine That Never Says No sets research of this kind beside the case for friction.

Memory compounds the problem. Researchers at MIT and Penn State recruited 38 participants for two weeks of conversations and found that accumulated context raised agreeableness in 4 of the 5 models studied, with a condensed user profile in memory having the greatest effect. Lead author Shomik Jain, a graduate student at MIT, warned that a user who starts to outsource thinking to a model "may find yourself in an echo chamber that you can't escape".

The human side of the exchange is the subject of a paper that Warren Mansell, a mental health professor at Curtin University in Australia, published in Digital Health and that Tech Xplore reported on October 19, 2026. Mansell proposes that the same chatbot can leave one person with broader awareness and another with narrower, more rigid thinking. "AI conversations can move in very different directions depending on what a person brings to the interaction and how the chatbot responds," he said. A causal story about why one is right is the ingredient the Shanghai benchmark found most persuasive to GPT-5.1.

Mansell's framework, the Curtin release states, has not been tested. A separate framework paper in npj Digital Medicine adds a clinical angle: chatbot conversations lack natural social friction such as fatigue or pushback, which in the authors' account makes compulsive engagement easier to sustain. Our file An AI That Says No Is a Better Teacher Than a Pickup Coach argues that an AI which does not give in easily is better preparation for meeting someone, provided it does not flatter.

Where the preprint stops: one model, AI annotators, English only

The paper has not been peer reviewed, and its component and format tests cover one model, GPT-5.1. Whether Claude-Fable-5 or Gemini-3.8-Flash, which moved far less, would show the same ranking of reasons over stories is unknown. The small Llama and Qwen models ran locally with 4-bit quantization and a context of 4,096 tokens, while the hosted models used default settings, so the comparison across seven models is not a clean contest between model families.

The measure of how much a model adopts from a story relies on AI annotators. A blind second review of 806 spans from 120 rationales agreed exactly in 82.63% of cases. The questions are in English and force a binary choice, which is a long way from a conversation about a breakup or a job. The authors say they used OpenAI Codex to help debug code and build part of the dataset and that they reviewed its output.

Mansell's own advice is plainer than any benchmark: approach AI conversations with a clear purpose, and consider how they fit alongside other priorities and relationships.

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