MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918298178813952 |
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| author | Kim, Sunghyun Yun, Seokwoo Yun, Youngseo Lee, Youngrak Lim, Sangsoo |
| author_facet | Kim, Sunghyun Yun, Seokwoo Yun, Youngseo Lee, Youngrak Lim, Sangsoo |
| contents | Motivation: Developing high-performing bioinformatics models typically requires repeated cycles of hypothesis formulation, architectural redesign, and empirical validation, making progress slow, labor-intensive, and difficult to reproduce. Although recent LLM-based assistants can automate isolated steps, they lack performance-grounded reasoning and stability-aware mechanisms required for reliable, iterative model improvement in bioinformatics workflows. Results: We introduce MARBLE, an execution-stable autonomous model refinement framework for bioinformatics models. MARBLE couples literature-aware reference selection with structured, debate-driven architectural reasoning among role-specialized agents, followed by autonomous execution, evaluation, and memory updates explicitly grounded in empirical performance. Across spatial transcriptomics domain segmentation, drug-target interaction prediction, and drug response prediction, MARBLE consistently achieves sustained performance improvements over strong baselines across multiple refinement cycles, while maintaining high execution robustness and low regression rates. Framework-level analyses demonstrate that structured debate, balanced evidence selection, and performance-grounded memory are critical for stable, repeatable model evolution, rather than single-run or brittle gains. Availability: Source code, data and Supplementary Information are available at https://github.com/PRISM-DGU/MARBLE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_14349 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution Kim, Sunghyun Yun, Seokwoo Yun, Youngseo Lee, Youngrak Lim, Sangsoo Multiagent Systems Machine Learning Motivation: Developing high-performing bioinformatics models typically requires repeated cycles of hypothesis formulation, architectural redesign, and empirical validation, making progress slow, labor-intensive, and difficult to reproduce. Although recent LLM-based assistants can automate isolated steps, they lack performance-grounded reasoning and stability-aware mechanisms required for reliable, iterative model improvement in bioinformatics workflows. Results: We introduce MARBLE, an execution-stable autonomous model refinement framework for bioinformatics models. MARBLE couples literature-aware reference selection with structured, debate-driven architectural reasoning among role-specialized agents, followed by autonomous execution, evaluation, and memory updates explicitly grounded in empirical performance. Across spatial transcriptomics domain segmentation, drug-target interaction prediction, and drug response prediction, MARBLE consistently achieves sustained performance improvements over strong baselines across multiple refinement cycles, while maintaining high execution robustness and low regression rates. Framework-level analyses demonstrate that structured debate, balanced evidence selection, and performance-grounded memory are critical for stable, repeatable model evolution, rather than single-run or brittle gains. Availability: Source code, data and Supplementary Information are available at https://github.com/PRISM-DGU/MARBLE. |
| title | MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution |
| topic | Multiagent Systems Machine Learning |
| url | https://arxiv.org/abs/2601.14349 |