{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IBIAJPHF6RB3CJQEWQ65K3VK6S","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"789738931ccba981e06ebc64c2e957e2ef31dbb97028b5dfcd24070b407591e3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LO","submitted_at":"2025-02-16T06:20:39Z","title_canon_sha256":"04cce39ba78ff182a795119668b6217bc881cf3b4e1e04c7c70f39b226db2348"},"schema_version":"1.0","source":{"id":"2503.04772","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04772","created_at":"2026-07-05T10:25:48Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04772v1","created_at":"2026-07-05T10:25:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04772","created_at":"2026-07-05T10:25:48Z"},{"alias_kind":"pith_short_12","alias_value":"IBIAJPHF6RB3","created_at":"2026-07-05T10:25:48Z"},{"alias_kind":"pith_short_16","alias_value":"IBIAJPHF6RB3CJQE","created_at":"2026-07-05T10:25:48Z"},{"alias_kind":"pith_short_8","alias_value":"IBIAJPHF","created_at":"2026-07-05T10:25:48Z"}],"graph_snapshots":[{"event_id":"sha256:1d9f6c88f57372f5f43f28c775dffd9b3a63a9ae05f8796a1121631871f6f516","target":"graph","created_at":"2026-07-05T10:25:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.04772/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have demonstrated significant potential in generating mathematical proofs. However, a persistent challenge is that LLMs occasionally make mistakes, while even a minor mistake can invalidate an entire proof. Proof assistants like Lean offer a great remedy. They are designed for verifying each step of a proof in a formal language, and in recent years researchers have created AI models to generate proofs in their languages. However, the scarcity of large-scale datasets of Lean proofs restrict the performance of such Automated Theorem Proving (ATP) models.\n  We develop","authors_text":"David Yin, Jing Gao","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LO","submitted_at":"2025-02-16T06:20:39Z","title":"Generating Millions Of Lean Theorems With Proofs By Exploring State Transition Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04772","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ac4a70fb730b8381fc577e44700a24339b8effb7a0e379edeb3013e1cbe4f272","target":"record","created_at":"2026-07-05T10:25:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"789738931ccba981e06ebc64c2e957e2ef31dbb97028b5dfcd24070b407591e3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LO","submitted_at":"2025-02-16T06:20:39Z","title_canon_sha256":"04cce39ba78ff182a795119668b6217bc881cf3b4e1e04c7c70f39b226db2348"},"schema_version":"1.0","source":{"id":"2503.04772","kind":"arxiv","version":1}},"canonical_sha256":"405004bce5f443b12604b43dd56eaaf4b9bc45ab4df60476b0453bc8a4692d2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"405004bce5f443b12604b43dd56eaaf4b9bc45ab4df60476b0453bc8a4692d2e","first_computed_at":"2026-07-05T10:25:48.622653Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:25:48.622653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/BQrahhBp1mrpuwt7SY/wZ6k9RtNFaYmaVClVpS9fhvJbh9K2bsZ5e5Bhzc6EzXJhhHbKR5Ds6fOgb++WOkwAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:25:48.623815Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04772","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac4a70fb730b8381fc577e44700a24339b8effb7a0e379edeb3013e1cbe4f272","sha256:1d9f6c88f57372f5f43f28c775dffd9b3a63a9ae05f8796a1121631871f6f516"],"state_sha256":"6717ad8a4e89585f9c489c38f80ac165b0a3e45339827fd82c0c7e7a05b83d21"}