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New benchmark ABLE evaluates LLM agents for protein design tasks

By PulseAugur Editorial · [1 sources] ·

A new benchmark called ABLE has been developed to evaluate the capabilities of Large Language Model (LLM) agents in utilizing biological AI models for protein design tasks. The benchmark assesses performance across structure retrieval, sequence generation, and design validation. Out of 15 evaluated frontier models, seven refused all tasks, while others showed significant performance variations. Claude Sonnet 4 and Gemini 3 Pro demonstrated the highest scores in information retrieval, tool selection, and tool use, suggesting that while LLMs can reduce barriers in protein design, they still struggle with planning and integrating biological knowledge with tool application. AI

IMPACT This benchmark could accelerate the development of more capable AI agents for scientific discovery in fields like protein design.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating LLM agents. [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 benchmark ABLE evaluates LLM agents for protein design tasks

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The cluster describes a new academic paper introducing a benchmark for evaluating LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi, Jake Yukich, Seth Donoughe ·

    Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools

    arXiv:2609.05818v1 Announce Type: new Abstract: We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use protein design workflows. ABLE assesses agent performance through a set of tasks …

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