[Submitted on 27 Oct 2026]
Title:Evaluating and improving AI agents for sequencing protocol understanding
View a PDF of the paper titled Evaluating and improving AI agents for sequencing protocol understanding, by Chi-Lam Poon and 3 other authors
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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)
Cite as: arXiv:2610.37851 [cs.AI]
(or arXiv:2610.37851v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.37851
arXiv-issued DOI via DataCite
Submission history
From: Chi-Lam Poon [view email] [v1] Tue, 27 Oct 2026 18:58:58 UTC (554 KB)
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