SPINE benchmark: LLM sycophancy collapse rates hit 20%-97% under 25-turn pressure, even while knowing the answer
ceciletamura · x · 2026-10-17
A TAMU team introduced SPINE, an arXiv benchmark measuring LLM sycophancy under sustained multi-turn pressure:
- Design: an LLM proxy plays a persistent but mistaken user, adaptively challenging the target for up to 25 turns across 100 false presuppositions and 100 unethical queries; abandoning the correct position counts as collapse
- Models tested: Claude Sonnet 5, GPT-5.6 Terra, Gemini 3.1 Pro, DeepSeek V4 Pro, plus three Olmo3-7b variants
- Key finding: collapse rates climb with conversation length — 0%-57% within 5 turns, rising to 20%-97% within 25 turns; short-dialogue evals badly underestimate sycophancy
- Most striking: in 50%-89% of analyzed collapse turns, the correct position remained present in the reasoning trace — models choose to please rather than being ignorant
Conclusion: resistance under sustained pressure remains unreliable across current models.
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