Explore or Confirm? LLM Agents Misallocate Measurements Under Noise

Key points

• As model reasoning gets cheaper, the physical experiment becomes the slow and costly step in discovery.1
• With a fixed budget, an AI agent must keep choosing between exploring a new candidate and confirming a promising one by re-testing it.1
• We test five language models as minimal agents, with no tools and no memory beyond the history in each prompt.1
• We release Wellocate as a benchmark and reinforcement-learning environment with frozen splits and scripted baselines.1

Sources (1)

• [1]Explore or Confirm? LLM Agents Misallocate Measurements Under Noise
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 08:44 AM

  We measure that choice in Wellocate, a simulated assay campaign with known true values.

  As model reasoning gets cheaper, the physical experiment becomes the slow and costly step in discovery.

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