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This paper introduces EviGraph, an innovative framework for autonomous research agents that organizes the research process as a typed evidence graph, addressing the common issue of unsupported claims and inconsistencies in research outputs. By maintaining and validating the evolving claim-evidence structure throughout the research pipeline, EviGraph significantly enhances the reliability of generated hypotheses and manuscripts. Experimental results show that EviGraph improves Claim Support Rate by 40.19% and achieves 87.73% Experimental Data Consistency compared to existing end-to-end research-agent baselines.
EviGraph boosts the reliability of autonomous research agents by ensuring every claim is grounded in a validated evidence chain, leading to a 40% increase in claim support.
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.
