When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
This paper introduces CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering, explicit ambiguity closure, and residual-based library inadequacy detection. It outperforms raw projection in five testbeds and demonstrates the ability to tentatively identify and then revoke out-of-library mechanisms. In a retrospective audit, it flagged all inconclusive claims from the A-Lab autonomous materials system while passing most confirmed claims.
[2606.07576] When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
[Submitted on 26 May 2026]
Title:When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
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Abstract:We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure (resolve), and residual-based library inadequacy detection (refuse). Under a local linear-Gaussian bridge, raw unresolved projection is the isotropic unresolved Fisher-information trace, while CARTOGRAPH-A is the exact unresolved A-optimal rule; closed-form EIG and Box-Hill arise as local comparators rather than global equivalents. Across five testbeds, CARTOGRAPH-A beats raw projection 129W/0T/15L at d = 8 (p
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