STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle
LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed: it may have misread the world, or read it correctly and still failed to act (the knowing-doing gap). Existing evaluations cannot separate these two failures; their reference policies either read privileged information the agent never sees, or are missing altogether. We introduce STOCKTAKE, a 26-week supply-chain replenishment benchmark built as a factored partially observable Markov decision process with six hidden factor processes, designed so that a fair reference policy is computable: an exact Bay...
| Authors | Sagar Deb, Ashwanth Krishnan |
|---|---|
| Primary category | cs.AI |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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