{
  "url": "https://appliedfrontier.nl/en/research/signal-c737fff9dfe3b50c/",
  "title": "Direct Optimization of Generators for Search in Automated Theorem Proving",
  "published": "2026-09-22",
  "excerpt": "Fine-tuned Large Language Models (LLMs) significantly advance Automated Theorem Proving (ATP), but are often deployed as guiding policies within tree search rather than for single-attempt",
  "content_sha256": "f66ff955ab5e7cb1da1a869ee5848c03b46e0edd7eb8a0b501f1b50a86a47571",
  "extracted_chars": 1416,
  "artifact_links": [
    "https://arxiv.org/abs/2609.25575"
  ],
  "arxiv_id": "2609.25575",
  "id": "signal-c737fff9dfe3b50c",
  "org": "Research community",
  "topic": "Formal verification",
  "source_kind": "preprint",
  "status": "unreviewed-source-report",
  "analysis_method": "deterministic_metadata_triage_v1",
  "novelty_verified": false,
  "proof_checked": false,
  "peer_review_verified": false,
  "matched_terms": [
    "theorem"
  ],
  "checked_at": "2026-10-31T19:17:04+00:00",
  "first_seen": "2026-10-29T21:52:00+00:00",
  "source_hash": "cf8babb43d97bd0d8ceab6a7531d3014cb9e145fd3c575fe2ab5e7054a819a67",
  "source_url": "https://arxiv.org/abs/2609.25575",
  "language": "en",
  "indexing": "noindex_until_editorially_enriched"
}