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New OmicsBench benchmark evaluates AI agents in complex bioinformatics tasks

Researchers have introduced OmicsBench, a new benchmark designed to evaluate AI agents' capabilities in complex, multi-turn bioinformatics tasks. The benchmark features 97 tasks, each comprising a directed acyclic graph of up to 662 dependent subtasks, simulating research-study-scale analyses. Initial evaluations across 12 models showed that even the slowest agents averaged 3.6 hours per task, with average scores ranging from 0.43 to 0.67, indicating significant room for improvement. The benchmark also includes a failure mode discovery pipeline to qualitatively analyze agent shortcomings, revealing issues such as agents deviating from specified instructions or generating answers from memory. AI

IMPACT This benchmark could drive the development of more capable AI agents for scientific research, particularly in complex fields like bioinformatics.

RANK_REASON The cluster describes a new benchmark paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New OmicsBench benchmark evaluates AI agents in complex bioinformatics tasks

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The cluster describes a new benchmark paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Gideon Shaked, Siddharth Nagisetty ·

    OmicsBench: a long-horizon multi-turn benchmark of AI agents on research-study-scale bioinformatics

    arXiv:2610.21074v1 Announce Type: new Abstract: Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most benchmarks of agents' bioinformatics capabilities pose a single short task with a single-shot prompt. We…