Sycophancy as Material Failure under Pushback Loading: A Multi-Axis Characterization Across Three Loading Cases and up to Seventeen Material Charges
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Abstract (arXiv)
Sycophancy in LLMs is documented across 70+ papers, but expert agreement on construct boundaries remains low (ICC=.184; Ye et al., 2026). The construct fragments because behavioral classification depends on which surface form is privileged. We adopt a materials-science framing: conversation as test specimen under load, LLM-model as material charge, pushback as progressive load, stance-flip as material failure. We characterize this failure across three loading cases (debate n=1000; false-presuppositions n=3400; ethical-setting n=3400; 10-17 material charges per case; 7800 specimens total) using 14 turn-level axis-measurements spanning velocity, damage accumulation, frame-drift, brittleness, and direction stability, plus three speaker-resolved axes from an independent pipeline. The measurements are Hooke-coupled (σ = E · varepsilon analog) and reproduce across loading cases with effects up to |rrb| = 0.35 on debate; the sign structure adds a second pattern: the ethical-setting case inverts the velocity and accumulation blocks. Variance composition partitions into two profiles: debate is charge-dominated (brittle-fracture-like: the material grade decides), false-presuppositions and ethical-setting are topic-dominated (creep-like: the load decides); the ratios (2.03 vs 0.13/0.17) are estimator-dependent, for debate even in direction. Cross-judge reliability (GPT-4o vs Haiku 4.5) shows debate scoring is judge-robust (Cohen's kappa = 0.88) while false-presupposition scoring is judge-sensitive (kappa = 0.36) -- a caveat single-judge benchmarks must report. This is the methodological move Ye et al.'s diagnosis calls for: a multi-axis characterization that does not depend on which surface form of the construct one privileges.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 15 Jun 2026.
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References · 8
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2023Large Language Models are not Fair Evaluatorscited by 1,050
- 2023Towards Understanding Sycophancy in Language Modelscited by 988
- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
- 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 91
- 2025Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactionscited by 12
- 2026What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Constructcited by 12
- 2026The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spuriouscited by 2
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