The complexity of modern bioinformatics analysis has created a critical gap between data generation and developing scientific insights. While large language models (LLMs) have shown promise in scientific reasoning, they remain fundamentally limited when dealing with real-world analytical workflows that demand iterative computation, tool integration and rigorous validation. We introduce K-Dense Analyst, a hierarchical multi-agent system that achieves autonomous bioinformatics analysis through a dual-loop architecture. K-Dense Analyst, part of the broader K-Dense platform, couples planning with validated execution using specialized agents to decompose complex objectives into executable, verifiable tasks within secure computational environments. On BixBench, a comprehensive benchmark for open-ended biological analysis, K-Dense Analyst achieves 29.2% accuracy, surpassing the best-performing language model (GPT-5) by 6.3 percentage points, representing nearly 27% improvement over what is widely considered the most powerful LLM available. Remarkably, K-Dense Analyst achieves this performance using Gemini 2.5 Pro, which attains only 18.3% accuracy when used directly, demonstrating that our architectural innovations unlock capabilities far beyond the underlying model's baseline performance. Our insights demonstrate that autonomous scientific reasoning requires more than enhanced language models, it demands purpose-built systems that can bridge the gap between high-level scientific objectives and low-level computational execution. These results represent a significant advance toward fully autonomous computational biologists capable of accelerating discovery across the life sciences.

翻译：现代生物信息学分析的复杂性在数据生成与科学洞见发展之间造成了关键鸿沟。尽管大语言模型（LLM）在科学推理方面展现出潜力，但在处理需要迭代计算、工具集成和严格验证的真实世界分析工作流时，其仍存在根本性局限。我们提出K-Dense Analyst，这是一个通过双循环架构实现自主生物信息学分析的层次化多智能体系统。作为更广泛的K-Dense平台的一部分，K-Dense Analyst将规划与验证执行相结合，利用专用智能体将复杂目标分解为在安全计算环境中可执行、可验证的任务。在面向开放式生物分析的综合基准测试集BixBench上，K-Dense Analyst实现了29.2%的准确率，以6.3个百分点超越了表现最佳的语言模型（GPT-5），这相当于比目前公认最强大的LLM提升了近27%。值得注意的是，K-Dense Analyst是使用Gemini 2.5 Pro实现这一性能的，而直接使用该模型时准确率仅为18.3%，这表明我们的架构创新释放了远超基础模型基线性能的能力。我们的研究揭示，自主科学推理不仅需要增强的语言模型，更需要能够弥合高层次科学目标与低层次计算执行之间鸿沟的专用系统。这些成果标志着在实现能够加速生命科学领域发现的完全自主计算生物学家方面取得了重大进展。