EPOCH: Reliable Discovery through Evidence-Governed Search

arXiv:2610.06986v1 Announce Type: new Abstract: AI research agents are increasingly used to search over programs, mathematical constructions, and proofs. However, existing systems typically optimize evaluator feedback without adequately governing how that feedback is interpreted, challenged, and reused. As a result, promising but fragile candidates can be promoted as discoveries, while benchmark improvements, finite certificates, and theorem-level claims are too easily conflated. We introduce EPOCH, an evidence-governed architecture designed to close this gap. EPOCH implements an evidence-governed discovery loop by combining explicit task contracts, typed memory, active falsification, admission checks, and independent replay, so that each candidate is evaluated against the strength and scope of the claim it supports. EPOCH achieves state-of-the-art aggregate performance on AlgoTune, substantially exceeding the strongest baseline in mean normalized score (0.65 vs. 0.53), and attains the highest mean score on the internal Math14 suite (0.57). It further shows favorable held-out behavior under official-test replay and leads the descriptive aggregate on AgentHPO. Across ten discovery problems, EPOCH delivers substantial task-specific advances, including improved executable constructions, optimized algorithms, counterexamples, and proof-supported results. These advances demonstrate its ability to convert search into concrete progress across mathematical and computational domains. Together, the results suggest that evidence governance is a necessary step toward AI research agents

EPOCH improves AI research agents' reliability and trustworthiness.

This is an incremental methodological advance toward more trustworthy automated research agents, not a claim about broad societal effect. It could modestly accelerate scientific discovery automation but offers no direct evidence of large-scale human impact yet.

The paper describes a new AI architecture, EPOCH, designed to improve the reliability and trustworthiness of discoveries made by AI research agents. By enhancing the rigor of AI-generated scientific findings, EPOCH directly addresses a potential limitation of AI in scientific research, making AI contributions more robust. This development generally supports human flourishing through more reliable scientific progress.

The paper presents a new AI architecture for more reliable discovery. It shows strong performance in specific benchmarks. The implications for human flourishing are uncertain.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   6a3f19b7-5f13-4d85-895f-09bc698c0f94
           https://arxiv.org/abs/2610.06986
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -29  (Adverse)
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CONFIDENCE 68%