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arXiv · 2610.37851

Evaluating and improving AI agents for sequencing protocol understanding

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.

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Chi-Lam Poon, Michael Goneos, Zezheng Song, Xi Chen. 2026-10-27. Evaluating and improving AI agents for sequencing protocol understanding. https://arxiv.org/abs/2610.37851

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