New ESFP Benchmark Measures Epistemic Stance Flexibility in LLMs

Jul 15, 2026

Researchers have introduced ESFP, a behavioral benchmark designed to assess whether large language models (LLMs) can shift their epistemic register when prompted about expert beliefs versus their own beliefs. In tests of eight frontier models, results show that epistemic flexibility is largely independent of general model capability, with a 27B open-weight model performing on par with leading proprietary systems. The study also finds that stance content density is a stronger indicator of flexibility than surface-level lexical markers.

Why it matters: This benchmark fills a key gap in evaluating conversational AI by measuring models' ability to distinguish between self-attributed and externally attributed stances, which is important for trustworthy interactions.

Full story at: arXiv Computation and Language ↗