AI 中文总结
EviGraph是一种自主研究框架，通过证据图维护研究状态，修复证据链问题，在ARC-Bench-ML和NanoResearch-20上优于基线，提升了主张支持率和实验数据一致性。
AI 中文摘要
自主研究智能体可生成假设、执行实验并撰写手稿，但其输出常包含无依据的主张，且在研究问题、实验、结果与结论间存在不一致。本文认为该问题部分源于架构：现有系统将研究组织为顺序流水线，却未在整个过程中显式维护或验证不断演变的主张-证据结构。本文提出EviGraph，一个自主研究框架，将研究过程表示为包含问题、缺口、假设、实验、发现和主张节点的类型化证据图，该图作为智能体的操作状态而非事后记录。EviGraph检查证据链是否存在缺失依赖、语义错位及结果-主张不一致，定位最早的薄弱节点并重新生成其受影响的下游子图；图的检查点机制防止修复失败破坏已验证的证据。仅当所有保留的主张均基于验证后的证据时，才生成手稿。在ARC-Bench-ML和NanoResearch-20上的实验表明，EviGraph在整体研究性能上优于对比的端到端研究智能体基线，比最强基线的主张支持率提升40.19%，实验数据一致性达87.73%。这些结果证明显式维护证据状态对可靠自主研究的价值。
英文摘要
Autonomous research agents can generate hypotheses, execute experiments, and draft manuscripts, yet their outputs often contain unsupported claims and inconsistencies between research questions, experiments, results, and conclusions. We argue that this problem is partly architectural: existing systems organize research as sequential pipelines but do not explicitly maintain or validate the evolving claim-evidence structure across stages. In this paper, we introduce EviGraph, an autonomous research framework that represents the research process as a typed evidence graph containing Problem, Gap, Hypothesis, Experiment, Finding, and Claim nodes. The graph serves as the operational state of the agent rather than a post-hoc record. EviGraph inspects evidence chains for missing dependencies, semantic misalignment, and result-claim inconsistencies, localizes the earliest weak node, and regenerates its affected downstream subgraph. Graph checkpointing prevents unsuccessful repairs from corrupting previously validated evidence. Manuscripts are generated only after every retained claim is grounded in a validated evidence chain. Experiments on ARC-Bench-ML and NanoResearch-20 show that EviGraph outperforms the compared end-to-end research-agent baselines in overall research performance, improves Claim Support Rate by 40.19% over the strongest baseline, and achieves 87.73% Experimental Data Consistency. These results demonstrate the value of explicit evidence-state maintenance for reliable autonomous research.
Comments23 pages,2 figures
