A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline

In three linesEmpirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. Agents solve individual stages but fail end-to-end: lack of predefined iteration criteria, weak scientific judgment, poor visual interpretation. Challenges absent from existing benchmarks: computational resource management, generalization to large held-out datasets.

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