New Wellocate Benchmark Shows LLM Agents Over-Confirm and Misallocate Measurement Budgets
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A study introduces Wellocate, a benchmark that tests how large language model agents split a fixed measurement budget between exploring untried options and confirming promising ones. Evaluated on a generated landscape and two real protein datasets, GB1 and PhoQ, five agents were compared. Three of them over-confirmed, and on the generated landscape one agent placed too much trust in single readings. The researchers also tested remedies: giving the over-confirming agents a list of untried candidates substantially improved the final selections of two models on the real protein data, while deferring re-testing to a final round recovered only about a quarter of the loss caused by wrong picks. The results suggest targeted prompting can partially correct measurement misallocation, though recovery remains incomplete.
Written by a model from the reports · updated 3 hours ago
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- arxiv.orgPickExplore or Confirm? LLM Agents Misallocate Measurements Under Noise
The authors propose the Wellocate benchmark, testing five language model agents on a generated landscape and two real protein datasets, GB1 and PhoQ, to see how they allocate a fixed measurement budget between exploring and confirming. Three agents over-confirm, and one over-trusts single readings on the generated landscape; giving the former a list of untried candidates clearly improves the final picks of two models on real protein data, while deferring re-testing to a final round recovers only about a quarter of the loss from wrong picks.
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