用智能代理网络自动探索科学数据，发现新假设。
Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents
- 构建可自组织的智能代理网络，持续生成并互评科学假设。
- 在癌症队列中成功发现已知生物标志物、扩展通路，并提出新靶点。
- 适合需要大规模探索性研究的科研团队使用。
大规模科学数据集——包括健康生物银行、细胞图谱、地球再分析等——为不受特定研究问题限制的探索性发现提供了机会。我们称这一过程为假设狩猎：通过在广阔复杂的假设空间中持续探索，累积获得洞见。为此，我们提出AScience框架，将发现建模为代理、网络与评估规范的交互过程，并实现为ASCollab，一个由具备异构行为的LLM驱动的研究代理组成的分布式系统。这些代理自我组织成动态演化的网络，在共同评估标准下持续生成并同行评审研究成果。实验表明，这种社会性动态能够积累高质量、高多样性且具新颖性的专家评级结果，包括重现已知生物标志物、扩展已知通路以及提出新的治疗靶点。尽管湿实验验证仍不可或缺，但我们在癌症队列上的实验表明，结构化社会化的代理网络可在大规模上持续开展探索性假设狩猎。
原文摘要 · Abstract (English)
Large-scale scientific datasets -- spanning health biobanks, cell atlases, Earth reanalyses, and more -- create opportunities for exploratory discovery unconstrained by specific research questions. We term this process hypothesis hunting: the cumulative search for insight through sustained exploration across vast and complex hypothesis spaces. To support it, we introduce AScience, a framework modeling discovery as the interaction of agents, networks, and evaluation norms, and implement it as ASCollab, a distributed system of LLM-based research agents with heterogeneous behaviors. These agents self-organize into evolving networks, continually producing and peer-reviewing findings under shared standards of evaluation. Experiments show that such social dynamics enable the accumulation of expert-rated results along the diversity-quality-novelty frontier, including rediscoveries of established biomarkers, extensions of known pathways, and proposals of new therapeutic targets. While wet-lab validation remains indispensable, our experiments on cancer cohorts demonstrate that socially structured, agentic networks can sustain exploratory hypothesis hunting at scale.
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