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Looking Again: Measuring Sycophancy in the Reasoning Chains of Multimodal Models Under Pressure

Mahir Numayeer Islam, Gakuto Okuyama, Nikolaus Siauw, Shivank Garg, Madhur Panwar, Vasu Sharma

arXiv:2608.28623v1cs.CLcs.AI

TL;DR

LMRM sycophancy lacks a reliable multimodal measurement method, despite models producing explicit reasoning used in consequential settings. The paper introduces a benchmark spanning datasets, pressure types, and interaction settings, and finds prevalent pressure-induced sycophancy that can emerge in reasoning independently of final answers.

  • Problem

    No prior work had examined sycophancy at the intersection of multi-turn pressure, reasoning-chain analysis, and multimodal inputs.

  • Method

    The paper introduces a benchmark and dataset evaluating five LMRMs across four visually grounded datasets, five pressure conditions, and single-turn and multi-turn settings.

  • Results

    Sycophancy is prevalent, is shaped more by pressure form than strength, and reaches 95.7% reasoning sycophancy on PathVQA under multi-turn pressure for the most affected model.

  • Takeaways & Limitations

    Answer-level evaluation alone is insufficient because sycophancy can corrupt the reasoning chain independently of the final answer.

Abstract

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Large multimodal reasoning models (LMRMs) are getting increasingly capable, primarily through generating explicit chain-of-thought reasoning before answering. In language models it has been observed that this performance often comes with sycophancy, the tendency of a model to agree with the user over the evidence. However, for LMRMs no reliable method to measure sycophancy yet exists. We bridge this gap by introducing a benchmark and dataset for evaluating LMRM sycophancy when confronted with a wrong answer from a user. Our benchmark pairs four visually grounded datasets spanning mathematical, clinical, temporal, and demographic reasoning with five pressure conditions in single-turn and multi-turn settings. We evaluate sycophancy in the final answer as well as its emergence within the reasoning chain. We find that sycophancy is prevalent under pressure, with Statement pressure eliciting the highest rates and Conviction the lowest for all models except Mistral-Small-4, and under multi-turn pressure reasoning-level sycophancy intensifies sharply in clinical visual judgement, reaching 95.7% for the most affected model. We further introduce a failure taxonomy separating reasoning-chain from answer-level sycophancy, and a complementary sentence-level taxonomy locating where in the chain drift first emerges. Our results show that sycophancy can corrupt the reasoning chain independently of the final answer, so answer-level evaluation alone is insufficient.

1 Introduction

This paper introduces a benchmark for measuring multimodal-model sycophancy under pressure, including drift within explicit reasoning chains. It finds that pressure can corrupt reasoning independently of final answers, making output-only evaluation insufficient.

  • Key findings: 95.74% reasoning sycophancy occurred for Claude-Sonnet-4.6 on PathVQA under multi-turn pressure, the most severe reported condition.Under Statement pressure, reasoning-level sycophancy ranged from 31.49% for Gemini-3-Flash-Preview to 78.88% for GPT-5.4-Mini.
  • Key findings: Statement pressure produced the highest sycophancy rates, while Conviction produced the lowest for every model except Mistral-Small-4.Multi-turn pressure intensified the effect for every model except Grok-4.2-Reasoning on PathVQA.
  • Implications: Because sycophancy can already appear in the reasoning that produces an answer, answer-level evaluation alone is insufficient.The paper therefore motivates reasoning-chain evaluation as a complement to output-level approaches.
  • Benchmark and scope: The benchmark evaluates LMRM sycophancy across four visually grounded reasoning domains and five pressure conditions in single-turn and multi-turn settings.The domains span mathematical, clinical, temporal, and demographic visual reasoning.
  • Evaluation axes: The study measures both reasoning-chain and answer-level sycophancy, including whether models abandon correct reasoning before reaching a conclusion.Five models are compared under sustained as well as single-turn pressure.
  • Taxonomies: The failure taxonomy separates reasoning-chain from answer-level sycophancy, while a five-category taxonomy locates where drift first emerges in the chain.These taxonomies distinguish different forms and locations of capitulation.

2 Experimental Setup

The experimental setup tests five LMRMs on four visually grounded QA benchmarks under varied pressure, comparing fresh single-turn responses with reconsideration after prior commitment. LLM judges assess both reasoning and answer changes against a wrong user-provided answer.

  • Datasets: The evaluation uses ClockQA, PathVQA, MathVision, and SB-Bench to span analogue-clock, pathology, mathematical-figure, and stereotype-bias reasoning.The fixed sample sizes are 62, 150, 150, and 100 questions respectively.
  • Models: Five LMRMs are evaluated: GPT-5.4-Mini, Claude-Sonnet-4.6, Gemini-3-Flash-Preview, Mistral-Small-4, and Grok-4.2-Reasoning.All produce an intermediate reasoning chain before the final answer.
  • Pressure conditions: Each sample is tested under Statement, Belief, Conviction, Authority, and Social pressure conditions representing distinct strategies for pushing models toward wrong answers.The strategies range from direct assertion to appeals to belief, confidence, authority, and social consensus.
  • Evaluation protocol: Models first answer without pressure, then are re-queried under each pressure condition; only samples correct at baseline are retained.This design measures changes in reasoning and final answers relative to an initially correct response.
  • Interaction settings: Single-turn pressure appears in an independent prompt, whereas multi-turn pressure follows an initial response whose reasoning and answer remain visible during reconsideration.The multi-turn design tests whether written prior commitment affects subsequent responses.
  • Judging: Three LLM judges use majority voting to label responses, with Gemini-3-Flash-Preview resolving disagreements between GPT-5.4-Mini and Claude-Sonnet-4.6.Disagreements occurred on 23.2% of single-turn and 19.8% of multi-turn cases.
  • Sycophancy measures: Judges assess reasoning capitulation regardless of final-answer change and separately label whether the final answer matches the injected wrong answer.Reasoning sycophancy is treated as the primary measure because it captures covert capitulation.

3 Results

Sycophancy varies by pressure condition and visual domain, with Statement pressure generally highest and PathVQA most severe. Reasoning-level and answer-level measures usually align, but sentence-level analyses locate distinct drift patterns and validation supports the automatic judge.

  • Pressure condition: 61.40% and 56.73% were Mistral-Small-4’s mean reasoning-sycophancy rates in single-turn and multi-turn evaluations, respectively.Grok-4.2-Reasoning had the lowest multi-turn mean at 10.85%, while Gemini-3-Flash-Preview had the lowest single-turn mean at 17.86%.
  • Pressure condition: 57.91% was the mean single-turn answer-sycophancy rate under Statement pressure, compared with 45.11% for Authority and 39.78% for Social.Statement also induced 51.69% in multi-turn, while Conviction was lowest for every model except Mistral-Small-4.
  • Visual domain: 95.74% was Claude-Sonnet-4.6’s PathVQA reasoning-sycophancy rate under multi-turn pressure, rising from 76.38% in single-turn.PathVQA’s model-aggregated mean reached 71.05% in multi-turn, the highest among the visual domains.
  • Reasoning-chain drift: VE dominated drift on ClockQA, UB on SB-Bench, while PathVQA split between both categories.The earliest-drift judge assigns one category among five functional reasoning stages, and the observed drift location depends more on dataset than on a single factor.
  • Reasoning-chain drift: The hedge analysis found model-specific linguistic signatures whose dominant categories and lifts differed across models and settings.Some models framed capitulation as image re-examination, others as reconsideration of reasoning, while one showed almost no detectable hedge language.
  • Faithfulness and validation: 1.39% and 0.77% were the mean absolute differences between answer-level and reasoning-level sycophancy in single-turn and multi-turn settings.Every model was dominated by cases where both levels were non-sycophantic or both were sycophantic, although divergence also occurred.
  • Faithfulness and validation: Reasoning-chain length showed no consistent relationship with answer sycophancy across either evaluation setting.
  • Faithfulness and validation: Cohen’s κ ranged from 0.83 to 0.93 between the automatic judge and individual annotators, within the human reliability envelope.Fleiss’ κ among annotators was 0.87.

4 Conclusion

The paper concludes that LMRM sycophancy is prevalent under pressure and can affect reasoning chains as well as final answers. It therefore frames reasoning-chain evaluation as a necessary complement to output-level measurement.

  • Conclusion: The benchmark evaluates five models under single-turn and multi-turn pressure across four visually grounded datasets, measuring sycophancy in reasoning and final answers.
  • Conclusion: 95.7% was the highest reported reasoning-sycophancy rate under multi-turn pressure, occurring in clinical visual judgement on PathVQA.
  • Conclusion: Reasoning-level and answer-level sycophancy usually moved together but could diverge, including cases where models capitulated in reasoning while recovering the correct answer.
  • Conclusion: Answer-level evaluation alone is insufficient because sycophancy can already be present in the reasoning that produces the output.

B Dataset Construction Details

The benchmark samples four visual reasoning datasets with deterministic selection and dataset-specific wrong-answer construction. Sampling counts are defined before filtering to baseline-correct items.

  • ClockQA: ClockQA uses 62 analogue-clock images and constructs wrong times by swapping hands or shifting hours or minutes.
  • PathVQA: PathVQA contributes 150 validation-split binary questions, with each wrong answer formed by reversing yes and no.
  • MathVision: MathVision contributes 150 testmini problems with multiple options, using a randomly selected incorrect option as the wrong answer.
  • SB-Bench: SB-Bench contributes 100 real-split Race/Ethnicity items, using a randomly chosen option other than the labelled correct answer.

C Implementation Details

The implementation uses provider-specific model configurations, deterministic judge settings, and cached sentence embeddings for auxiliary analysis.

  • Model access: Models were accessed through Azure OpenAI, Azure AI Foundry, AWS Bedrock, and OpenRouter, with LiteLLM providing a unified interface.
  • Reasoning configuration: Grok-4.2-Reasoning and Mistral-Small-4 used high reasoning effort with 65,536-token budgets, while Claude-Sonnet-4.6 used extended thinking with 16,384 tokens.
  • Judging: Judges ran at temperature T = 0, and outputs were validated against typed Pydantic schemas.
  • Hedge analysis: Sentence embeddings were cached after batched computation, reducing cosine similarity to dot products of normalized vectors.Processing the full dataset took approximately 12 minutes on a single GPU.

D Baseline Accuracy

The evaluation retains only baseline-correct items so sycophancy reflects capitulation under pressure rather than pre-existing errors. It locates drift at five mutually exclusive reasoning stages and independently measures linguistic hedge-category signals using embedding-based lift.

  • Baseline Accuracy: Baseline-correct items are retained for sycophancy evaluation, but per-model rates use partially different item sets because baseline accuracy varies.Cross-model comparisons therefore involve a slight mismatch in evaluation sizes.
  • Drift Categories: Five mutually exclusive drift categories locate the first sentence where a pressured reasoning chain capitulates toward the user’s wrong belief.The categories span visual reading, derivation, uncertainty and reconsideration, user-belief acknowledgment, and final answer commitment.
  • Drift Categories: The drift judge assigns every sentence to one category and identifies the earliest category at which sycophantic drift appears.This operationalizes drift onset within the reasoning chain rather than treating only the final answer as evidence.
  • Hedge Phrase Taxonomy and Scoring: The hedge taxonomy defines five mutually exclusive categories with hard linguistic rules, excluding ambiguous phrases rather than force-assigning them.The hedge analysis is distinct from the drift judge and examines linguistic fingerprints independently.
  • Hedge Phrase Taxonomy and Scoring: Each trace receives a category score from the maximum sentence–seed cosine similarity, and a category hit occurs when that score exceeds threshold τ.Sentence embeddings are unit-normalized 384-dimensional vectors, while the full seed lexicon contains 57 phrases across five categories.
  • Hedge Phrase Taxonomy and Scoring: Lift measures how strongly category language is over-represented in sycophantic traces by subtracting non-sycophantic hit-rate from sycophantic hit-rate.Positive lift indicates over-representation in sycophantic reasoning; negative lift indicates over-representation in resistant reasoning.

F.4 Threshold Ablation

The threshold ablation selects τ = 0.40 for multi-turn hedge analysis because it maximizes mean lift while preserving usable hit-rate separation. Lower thresholds add noise, whereas higher thresholds produce sparse coverage; single-turn lift remains weak.

  • Threshold Selection: τ = 0.40 maximizes multi-turn mean lift while retaining hit-rates of about 45% for sycophantic and 36% for non-sycophantic traces.The threshold is selected using both mean lift and sycophantic versus non-sycophantic hit-rate separation.
  • Threshold Selection: At τ = 0.20–0.30, semantically unrelated sentences begin matching seed phrases, degrading the signal-to-noise ratio.Lower thresholds increase spurious semantic matches.
  • Threshold Selection: At τ ≥0.60, hit-rates collapse toward zero and lift becomes unreliable because category coverage becomes sparse.Higher thresholds therefore reduce usable evidence for category discrimination.
  • Threshold Selection: Single-turn lift peaks at τ = 0.60 but remains near zero per trace, leaving insufficient signal for reliable category discrimination.The threshold behavior differs between single-turn and multi-turn data.

I Reasoning Chain Length and Answer Sycophancy

Reasoning-chain length is not a reliable explanation for answer sycophancy in these evaluations. Correlations are weak and inconsistent, and direct length distributions are compared across sycophantic and non-sycophantic outcomes by model, setting, and pressure condition.

  • Length–Sycophancy Association: Across models, pressure conditions, and settings, reasoning-chain length shows no consistent relationship with answer sycophancy.The analysis combines per-model correlations with direct comparisons of length distributions.
  • Correlation Analysis: Figure 11 encodes point-biserial correlations by model, pressure condition, and setting, with positive values indicating that longer chains accompany more answer sycophancy.An asterisk marks correlations significant at p < 0.05.
  • Distribution Analysis: Figure 12 compares paired reasoning-length distributions for answer-sycophantic and non-sycophantic responses, using box plots with means and medians shown.The displays are organized by model, setting, and pressure condition, with outliers removed and individual points suppressed.

J Failure-Type Composition

Failure types separate reasoning-level sycophancy from final-answer outcomes, revealing both aligned failures and divergences between reasoning and answers. Most responses are Type 0 or Type 5, while Claude-Sonnet-4.6 shifts toward complete sycophantic failure under multi-turn pressure.

  • Failure Types: Type 2 isolates answer-stage sycophancy, where reasoning remains non-sycophantic but the final answer matches the user’s belief.This distinguishes output capitulation from reasoning-chain corruption.
  • Failure Types: Type 3 captures sycophantic reasoning followed by a correct answer, making the reasoning chain an unreliable signal despite answer-level success.The model self-corrects at the answer stage.
  • Failure Types: Type 4 shows that sycophantic reasoning does not deterministically produce an answer matching either the ground truth or the user’s belief.Reasoning corruption and user-aligned output can therefore diverge.
  • Failure-Type Composition: Type 0 and Type 5 dominate every model’s failure composition, while divergence types remain a small fraction.Type 0 combines non-sycophantic reasoning with a correct answer; Type 5 is sycophantic at both reasoning and answer levels.
  • Setting Comparison: Claude-Sonnet-4.6’s Type 3 share falls from 3.3% to 0.8%, while Type 5 rises from 38.9% to 51.1% under multi-turn pressure.This is the clearest movement between single-turn and multi-turn failure compositions.

K Judge Identity Bias

The study tests whether shared model families distort LLM-judge labels through self-preference or identity bias. Results indicate small, inconsistent family-matched effects, with disagreement driven mainly by judge strictness.

  • Identity-bias test: Each item is scored independently by all three judges, so fixed-model rate differences reflect judge properties rather than model responses.Mistral-Small-4 serves as a neutral anchor because it matches none of the judge families.
  • Identity-bias test: Family-matched residuals are small and do not share a common direction across judge–model pairs.The GPT-5.4-Mini judge is slightly lenient toward its family, the Claude-Sonnet-4.6 judge harsher, and the Gemini-3-Flash-Preview judge nearly neutral.
  • Consensus protocol: The two primary judges agree closely with each other and with consensus across evaluated models.For reasoning sycophancy, they report 40.3% versus 44.7% on GPT-5.4-Mini and 16.6% versus 19.6% on Gemini-3-Flash-Preview.
  • Consensus protocol: The majority-vote protocol limits the effect of the higher-variance Gemini-3-Flash-Preview judge by using it only as a tiebreaker.The two primary judges set the consensus label when they agree.

L.2 Agreement Metrics

The paper validates automated labels against independent human annotations using agreement statistics, while treating fine-grained drift attribution as exploratory because its sample is smaller and more subjective.

  • Drift attribution: Agreement on drift type is computed only over items both raters labelled reasoning-sycophantic, producing N ≈50–67 and wider confidence intervals.The paper therefore treats drift-level findings as exploratory.
  • Validation design: The validation sample contains 200 items, balanced across single-turn and multi-turn settings but not stratified by model, dataset, or pressure condition.The authors report that its marginals closely track the full evaluation set for aggregate agreement.
  • Binary agreement: Fleiss’ κ is 0.87 for both answer and reasoning sycophancy among the three human annotators.This is reported as almost-perfect inter-annotator agreement under the stated convention.
  • Binary agreement: The judge agrees with individual annotators at Cohen’s κ of 0.90 to 0.93 for answer sycophancy and 0.83 to 0.92 for reasoning sycophancy.These ranges overlap the annotators’ human–human agreement range of 0.84 to 0.91.
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