EMNLP paper: higher-accuracy LLM agents fail to flag uncertainty under knowledge conflict
DanielKhashabi · x · 2026-10-13
The EMNLP 2026 Findings paper "Accurate but Not Humble" introduces an epistemic-humility eval for LLM agents along three trajectory-level dimensions: Identify, Solve, Escalate (ISE). Testing four agents under controlled and naturally occurring knowledge conflicts, it finds higher task accuracy doesn't imply greater epistemic humility — some high-accuracy agents detect conflicts early yet omit unresolved uncertainty from wrong final answers.
More from coding & agent
- RustGIS lands on PyPI: pure-Rust open-source GIS now runs inside Jupyter — giswqs · 2026-10-13
- AI creator reveals 5 unreleased Claude Skills, from video editing to personal agents — op7418 · 2026-10-13
- LangSmith launches AI Search: query agent traces in natural language — LangChain · 2026-10-13
- Dev builds a web design research skill that gets Claude to create bold branding concepts — round · 2026-10-13
- Kernel Panic conference in Madrid packs house, slides from four production-AI talks published — SergioPaniego · 2026-10-13
- This Hermes agent auto-filters weekly GitHub AI repos, search and fetch are free — prasenx · 2026-10-13