Explore or Confirm? LLM Agents Misallocate Measurements Under Noise
How to prepare an accurate paper press kit? Illustrated guide
Abdul Karim, Matt Keon · arXiv: 2610.25111v1 · Summary page
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The next experiment is a decision, too
A promising laboratory measurement may identify a good candidate—or merely a lucky reading. The Wellocate preprint studies whether language-model agents can divide a fixed experimental budget between exploring new candidates and checking previous results. Its simulated campaigns use a generated landscape and two real protein datasets, scrambled to prevent answers from memory. Five models were tested as minimal agents, without tools or memory beyond the history supplied in each prompt.
The models made different mistakes. Three spent too much of their budget repeating measurements; on the generated landscape, one did too little confirmation, and every model ultimately selected a worse candidate than a basic scripted strategy. Giving agents a list of untried candidates reduced excessive retesting and substantially improved two models’ choices on real protein data. For the under-confirming model, a final round of checks recovered only about a quarter of its loss. The lesson is practical: choosing what to measure, and when to measure it again, deserves evaluation before an agent is entrusted with expensive experiments.
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Talk about a paper in public without overstating it: yours, one you are asked to comment on, or one you write about as a journalist.
- Your paper: a draft release for your press office, the questions journalists will ask with what the paper lets you answer, the likely misreadings, a lay summary and a short thread.
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- Writing about it as a journalist: reporting angles, the wording to check before publication, and the questions to ask the authors.
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