The limits of bio-molecular modeling with large language models : a cross-scale evaluation

• URL: http://arxiv.org/abs/2604.03361v1
• Date: Fri, 03 Apr 2026 17:38:42 GMT
• Title: The limits of bio-molecular modeling with large language models : a cross-scale evaluation
• Abstract summary: Large language models (LLMs) are increasingly applied to bio-molecular discovery.<n>We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench.
• Score: 10.137315614435176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are increasingly applied to bio-molecular discovery, yet systematic evaluation across multi-scale biological problems and rigorous assessment of their tool-augmented capabilities remain limited. We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench, a unified framework comprising 26 downstream tasks that covers 4 distinct difficulty levels, and computational tools are integrated for a more comprehensive evaluation. Evaluation on 13 representative models reveals 4 main findings: chain-of-thought data provides limited benefit and may even reduce performance on biological tasks; hybrid mamba-attention architectures are more effective for long bio-molecular sequences; supervised fine-tuning improves specialization at the cost of generalization; and current LLMs perform well on classification tasks but remain weak on challenging regression tasks. Together, these findings provide practical guidance for future LLM-based modeling of molecular systems.

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