SPINE: Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
Large language models (LLMs) may abandon correct positions when users push back, exhibiting a failure mode known as sycophancy. Existing evaluations typically use short, prespecified conversations and may therefore miss failures that emerge under sustained, adaptive disagreement. We introduce SPINE, a benchmark in which an LLM proxy plays a persistent but mistaken user and adaptively challenges a target model for up to 25 turns. We evaluate four commercial models (Claude Sonnet 5, GPT-5.6 Terra, Gemini 3.1 Pro, and DeepSeek V4 Pro) and three Olmo3-7b models on 100 false presuppositions and 100 unethical queries. We define collapse as a target model abandoning the correct position and yiel...
| Authors | Leyuan Tang, Kangda Wei, Tianyu Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 September 2026 |
| Last updated at source | 13 October 2026 |
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