Evaluating and improving AI agents for sequencing protocol understanding

Authors: Chi-Lam Poon , Michael Goneos , Zezheng Song , Xi Chen

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Abstract: Existing scientific-agent benchmarks rarely evaluate whether agents can reconstruct the exact molecular structures and generation workflows specified by sequencing protocols, a capability critical for analyzing customized sequencing assays. We introduce LibStructBench, a document-grounded benchmark for reconstructing sequencing-library structures and molecular workflows. Our experiments show that frontier agents often produce approximately correct reconstructions but remain unreliable at exact reconstruction, particularly in molecular-state representation and workflow connectivity. Switching the underlying language model produced larger average performance differences than switching agent harnesses, with a statistically significant difference only for molecular-state reconstruction. Additional knowledge improved oligo-family recovery in a small pilot without improving mean molecular-workflow scores. Autonomous accumulation of procedural rules and cross-protocol examples showed non-monotonic validation performance and no mean held-out molecular-workflow improvement in the tested trajectory. These findings highlight the need for more controlled and verifiable approaches to scientific reasoning about molecular processes in complex experimental protocols.

Subjects:

Artificial Intelligence (cs.AI) ; Genomics (q-bio.GN)

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arXiv:2610.37851 [cs.AI]

(or

arXiv:2610.37851v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.37851

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arXiv-issued DOI via DataCite

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From: Chi-Lam Poon [ view email ]

[v1]

Tue, 27 Oct 2026 18:58:58 UTC (554 KB)

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