LLM Agents Show Gap Between Knowing and Doing in Supply Chains.
Summary
A new benchmark, STOCKTAKE, evaluates LLM agents on multi-week supply-chain tasks, revealing a significant "knowing-doing gap" where agents detect problems but fail to act effectively. It measures both state estimation and control separately using a fair oracle.
Why it matters
Professionals deploying or developing LLM agents need to understand that high diagnostic accuracy does not guarantee effective action, especially in complex, long-horizon tasks like supply chain management. This research provides a framework to identify and address these critical performance gaps.
How to implement this in your domain
- 1Adopt structured evaluation benchmarks like STOCKTAKE for LLM agents in critical business processes.
- 2Design agent architectures that explicitly separate perception/diagnosis from action planning and execution.
- 3Implement monitoring systems to track both an agent's stated beliefs and its actual operational outcomes.
- 4Develop targeted training or fine-tuning strategies to improve agents' response mechanisms, not just their diagnostic capabilities.
Who benefits
Key takeaways
- LLM agents can accurately diagnose problems but still fail to act effectively.
- The "knowing-doing gap" is a significant challenge for autonomous agents.
- New benchmarks are crucial for evaluating both perception and action capabilities.
- Agent failures can stem from both under-response and overly costly responses.
Original post by Sagar Deb, Ashwanth Krishnan
"arXiv:2607.13618v1 Announce Type: new Abstract: 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 i…"
View on XOriginally posted by Sagar Deb, Ashwanth Krishnan on X · view source
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