Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers released Hard Mode variants of two automated theorem-proving benchmarks and introduced DAP, an agentic framework that uses LLM reasoning to discover answers before formal proof construction. The work exposes how existing ATP benchmarks embed solutions, inflating model capability estimates.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The more pointed finding here is methodological: current ATP benchmarks often embed the answer implicitly in how the problem is phrased, meaning models can pattern-match to a solution without actually reasoning through a proof. DAP's 'discover first' design is a direct response to that contamination, not just a performance optimization.
Benchmark integrity has been a recurring concern across recent coverage. The piece on 'Diagnosing LLM Judge Reliability' (arXiv, April 16) surfaced a parallel problem in evaluation: high aggregate scores masking systematic logical failures at the instance level. Both papers are pointing at the same structural issue from different angles — that our measurement tools are quietly flattering the models we use them to assess. The MADE benchmark from the same week made a similar move in medical NLP, introducing a living dataset specifically to fight data contamination. DAP's Hard Mode variants fit that pattern: researchers building harder, cleaner targets because existing ones have been quietly solved by exposure rather than capability.
Watch whether the DAP framework gets adopted by any of the major reasoning model evaluations (DeepMind, OpenAI, or the Lean community's own leaderboards) within the next six months. Adoption there would signal the field accepts the contamination critique; silence would suggest the benchmark ecosystem has too much inertia to self-correct.
This interpretation is generated from the summary above and the archive coverage cited below. Our methodology · Report an error
Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations
Researchers developed diagnostic tools to assess LLM judge reliability in text evaluation tasks, finding that while aggregate consistency appears high (~96%), one-third to two-thirds of documents show logical inconsistencies in pairwise comparisons, with conformal prediction sets offering per-instance confidence estimates.
MentionsDiscover And Prove (DAP) · MiniF2F-Hard · FIMO-Hard · Lean 4
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