Explore or Confirm? LLM Agents Misallocate Measurements Under Noise
As model reasoning gets cheaper, the physical experiment becomes the slow and costly step in discovery. Each experiment returns a noisy reading: a high value can mean a good candidate or a lucky draw. With a fixed budget, an AI agent must keep choosing between exploring a new candidate and confirming a promising one by re-testing it. We measure that choice in Wellocate, a simulated assay campaign with known true values. It is built on a generated landscape and on two real protein datasets, GB1 and PhoQ, scrambled so no model can answer from memory. We test five language models as minimal agents, with no tools and no memory beyond the history in each prompt. They misallocate in both direct...
| Authors | Abdul Karim, Matt Keon |
|---|---|
| Primary category | cs.AI |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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