aiqbench analysis
BioAgent Bench establishes a critical benchmark for AI agents operating within bioinformatics, demonstrating their ability to execute complex multi-step workflows like RNA-seq and variant calling without extensive custom scaffolding. However, the suite also exposes significant failure modes under controlled perturbations such as corrupted inputs or prompt bloat, highlighting that high-level pipeline success does not guarantee reliable step-by-step reasoning in sensitive data environments. This evaluation framework is essential for advancing cost-effective local agents capable of handling proprietary biological research while maintaining robustness against adversarial conditions.
BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics
arXiv:2601.21800v4 Announce Type: replace Abstract: We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The suite consists of manually curated end-to-end tasks (e.g., RNA-seq, variant calling, metagenomics) accompanied by task-specific prompts and concrete output artifacts to support automated assessment. We evaluate frontier closed- and open-weight models across multiple agent harnesses, and use an LLM-based grader to score pipeline progress and outcome validity. We find that agents based on frontier LLMs can complete multi-step bioinformatics pipelines without elaborate custom scaffolding, often producing the requested final artifacts reliably. However, robustness tests reveal failure modes under controlled perturbations (corrupted inputs, decoy files, and prompt bloat), indicating that correct high-level pipeline construction does not guarantee reliable step-level reasoning. By releasing the code and the complementary resources constituting our suite, our primary goal is to accelerate the development of cost-effective yet reliable local agents, capable of handling complex bioinformatics workflows often involving sensitive patient data or unpublished intellectual property.
Abstract:We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The suite consists of manually curated end-to-end tasks (e.g., RNA-seq, variant calling, metagenomics) accompanied by task-specific prompts and concrete output artifacts to support automated assessment. We evaluate frontier closed- and open-weight models across multiple agent harnesses, and use an LLM-based grader to score pipeline progress and outcome validity. We find that agents based on frontier LLMs can complete multi-step bioinformatics pipelines without elaborate custom scaffolding, often producing the requested final artifacts reliably. However, robustness tests reveal failure modes under controlled perturbations (corrupted inputs, decoy files, and prompt bloat), indicating that correct high-level pipeline construction does not guarantee reliable step-level reasoning. By releasing the code and the complementary resources constituting our suite, our primary goal is to accelerate the development of cost-effective yet reliable local agents, capable of handling complex bioinformatics workflows often involving sensitive patient data or unpublished intellectual property.
Artificial Intelligence (cs.AI)
arXiv:2601.21800 [cs.AI]
(or arXiv:2601.21800v4 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2601.21800 Focus to learn more arXiv-issued DOI via DataCite
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