{"slug": "pfarena-benchmarking-language-models-for-protein-modification", "title": "PFArena: Benchmarking Language Models for Protein Modification", "summary": "Researchers introduced PFArena, a benchmark with four controlled task interfaces covering single-mutant generation and multi-mutant ranking, to compare protein language models (PLMs), large language models (LLMs), and LLM-based agents on protein modification. Evaluating six PLMs, six LLMs, and five LLM-based agents, the team found PLMs lead at open-ended single-mutant generation using protein-specific priors, while LLMs and agents perform strongly at multi-mutant ranking when target-specific fitness data are available, and all model families struggle as search-space size and mutation depth increase. The code and benchmark suite were released to support reproducible research in model-assisted protein modification.", "body_md": "arXiv:2609.28921v1 Announce Type: new \nAbstract: Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly. Although computational paradigms including protein language models (PLMs), large language models (LLMs), and LLM-based agents have shown promise in protein modification, their relative efficacy across realistic experimental decision-making settings remains unclear. To bridge this gap, we introduce PFArena, a benchmark comprising four controlled task interfaces that cover single-mutant generation and multi-mutant ranking. By providing varying levels of mutation fitness data, PFArena reflects four representative research scenarios characterized by differing degrees of prior experimental context. We assess six PLMs, six LLMs, and five LLM-based agents using complementary metrics to measure both peak and overall protein modification performance. Our evaluation reveals that model performance shifts systematically with the availability of target-specific experimental evidence: PLMs demonstrate proficiency in open-ended single-mutant generation by leveraging protein-specific priors, whereas LLMs and agents perform strongly in multi-mutant ranking, particularly when target-specific fitness data are available. Nevertheless, all model families face fundamental challenges with increasing search-space size and mutation depth. We release our code and benchmark suite to facilitate reproducible research in model-assisted protein modification.", "url": "https://wpnews.pro/news/pfarena-benchmarking-language-models-for-protein-modification", "canonical_source": "https://arxiv.org/abs/2609.28921", "published_at": "2026-09-25 04:00:00+00:00", "updated_at": "2026-09-25 04:29:51.552939+00:00", "lang": "en", "topics": ["ai-research", "large-language-models", "machine-learning", "ai-agents", "artificial-intelligence"], "entities": ["PFArena", "protein language models", "large language models", "LLM-based agents"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/pfarena-benchmarking-language-models-for-protein-modification", "markdown": "https://wpnews.pro/news/pfarena-benchmarking-language-models-for-protein-modification.md", "text": "https://wpnews.pro/news/pfarena-benchmarking-language-models-for-protein-modification.txt", "jsonld": "https://wpnews.pro/news/pfarena-benchmarking-language-models-for-protein-modification.jsonld"}}