Abstract
Fluent LLM hypotheses are useful data-mining candidates, but unsafe as discoveries: a generated claim may be unsupported, confounded, multiplicity-induced, or unstable. We study LLM-guided verifiable discovery, where an LLM suggests constrained natural-language hypotheses and a separate verifier decides which claims deserve to be accepted as discoveries. VeraDM compiles temporal, sequence, treatment-effect, and conjunctive hypotheses into typed data-mining queries, then accepts them only after support checks, permutation testing, multiple-testing correction, matched-contrast validation, and held-out replication. Every accepted or rejected claim becomes an auditable evidence card. Under split and firewall assumptions, we prove firewall-preservation and frozen-batch FDR results, give BY and multi-round variants, and show a power-separation result explaining when an informative proposer preserves power where exhaustive BH collapses. Experiments on synthetic failure modes, semi-synthetic injections, statistically sound pattern-mining baselines, multi-provider proposer diagnostics, timestamped Bank, Bike, and Healthcare case studies, and audit controls show that VeraDM rejects plausible invalid claims while producing readable, replicated discoveries. Keywords: Statistically sound pattern mining, Trustworthy and interpretable data mining, Large language models for data mining, Mining time-evolving data, False discovery control, Matched-contrast validation