FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents
FirstResearch introduces a structured Research Question Certificate to make LLM-generated scientific research questions auditable by recording primitive definitions, assumptions, mechanism model, tension, falsifiable hypothesis, minimal decisive test, and failure update rule. Evaluated on ten topics, the framework outperformed baselines inspired by AI co-scientist, Agent Laboratory, and AI Scientist-v2, scoring 4.86/5 vs 4.38/5. Ablation shows the certificate is crucial; without it scores drop below 1/5. Findings suggest explicit derivation constraints improve auditability.
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[Submitted on 6 Jul 2026]
Title:FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents
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Abstract:LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect. We introduce FirstResearch, a first-principles research-question formation framework for scientific LLM agents whose core artifact is a structured Research Question Certificate. The certificate records primitive definitions, assumptions, a mechanism model, a tension or contradiction, a falsifiable hypothesis, a minimal decisive test, and a failure update rule, making the proposed question inspectable before downstream execution. On ten LLM-agent research topics, FirstResearch outperforms controlled prompt-level baselines inspired by AI co-scientist, Agent Laboratory, and AI Scientist-v2 under a primary DeepSeek-blind-judge protocol. A Gemini-2.5-Flash independent-judge rescore of the same 40 baseline packages preserves the system-level ranking, with FirstResearch scoring 4.86/5 versus 4.38/5 for the strongest baseline and Pearson agreement of 0.865 on average score. A one-repeat ablation checkpoint further suggests that the certificate-centered core is the strongest component: certificate-only scoring reaches 4.90/5 under DeepSeek and 4.88/5 under Gemini, while removing certificates drops below 1/5 under both judges. These results are preliminary and use LLM judges rather than human domain experts, but they support a narrow scientific-discovery claim: explicit derivation constraints are a promising mechanism for making LLM-generated scientific questions more auditable. Code, prompts, saved outputs, and reproduction scripts are available at this https URL.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05682 [cs.AI]
(or arXiv:2607.05682v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.05682
arXiv-issued DOI via DataCite (pending registration)
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From: Yufeng Wang [view email] [v1] Mon, 6 Jul 2026 22:52:07 UTC (12 KB)
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