STOCKTAKE: New Benchmark Reveals Knowing-Doing Gap in LLM Agents

Jul 16, 2026

A new benchmark, STOCKTAKE, evaluates large language model (LLM) agents on a 26-week supply-chain replenishment task, explicitly separating state estimation from action. The study finds that while models can detect 84-88% of hidden failures, their actions often underperform a symptom-blind baseline, exposing a knowing-doing gap where correct diagnosis does not ensure effective intervention.

Why it matters: STOCKTAKE offers a novel method to disentangle perception from action failures in LLM agents, which is crucial for improving their reliability in complex, real-world decision-making tasks.

Full story at: arXiv AI/ML ↗