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Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops

The study examines why System One models make incorrect decisions in agent loops, finding that most errors occur after correct interpretation of the situation, with accurate models making more such errors. The research uses a benchmark with various scenarios to analyze decision-making processes.

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PublishedOctober 13, 2026Yubo Li, Ramayya Krishnan, Rema Padman
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WHY IT MAY MATTER

This research helps understand why fast decision-making models fail, highlighting that errors often stem from incorrect action selection despite correct perception, which is crucial for improving AI reliability in real-world applications.

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yesterdayDaily Papers

EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment

Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. We introduce EviLens, an expert-reviewed benchmark of 10,174 supported and refuted images covering twelve factual error types.

4 days ago