{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UUGRONKWBWXNKR5T2IWOROVHXA","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":"ba7e288962f8c60af829adb382eac0e2d9b7fbd9751ec6bf030a961e97516076","cross_cats_sorted":["cs.AI","cs.LO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T17:11:24Z","title_canon_sha256":"9c4afe502c6cbc7ec25bdbcac385358aded8f572630e5a7667a91d9b1dd8b63b"},"schema_version":"1.0","source":{"id":"2410.06209","kind":"arxiv","version":8}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.06209","created_at":"2026-07-05T10:25:11Z"},{"alias_kind":"arxiv_version","alias_value":"2410.06209v8","created_at":"2026-07-05T10:25:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06209","created_at":"2026-07-05T10:25:11Z"},{"alias_kind":"pith_short_12","alias_value":"UUGRONKWBWXN","created_at":"2026-07-05T10:25:11Z"},{"alias_kind":"pith_short_16","alias_value":"UUGRONKWBWXNKR5T","created_at":"2026-07-05T10:25:11Z"},{"alias_kind":"pith_short_8","alias_value":"UUGRONKW","created_at":"2026-07-05T10:25:11Z"}],"graph_snapshots":[{"event_id":"sha256:2bd21681eb1f7a1034b6c99653e7cc635c3e02951dbf7a3a2aa95b0e186fe1c9","target":"graph","created_at":"2026-07-05T10:25:11Z","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/2410.06209/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dataset to perform well on particular domains, such as undergraduate-level mathematics. These methods struggle with generalizability to advanced mathematics. A fundamental limitation is that these approaches operate on static domains, failing to capture how mathematicians often work across multiple domains and projects simultaneously or cyclically. We","authors_text":"Adarsh Kumarappan, Anima Anandkumar, Chaowei Xiao, Mo Tiwari, Peiyang Song, Robert Joseph George","cross_cats":["cs.AI","cs.LO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T17:11:24Z","title":"LeanAgent: Lifelong Learning for Formal Theorem Proving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06209","kind":"arxiv","version":8},"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:dbda555a9f8587c99aa9ec43a6e71d9120f7c1d0e2b7e01b8f52188e0c8898ee","target":"record","created_at":"2026-07-05T10:25:11Z","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":"ba7e288962f8c60af829adb382eac0e2d9b7fbd9751ec6bf030a961e97516076","cross_cats_sorted":["cs.AI","cs.LO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T17:11:24Z","title_canon_sha256":"9c4afe502c6cbc7ec25bdbcac385358aded8f572630e5a7667a91d9b1dd8b63b"},"schema_version":"1.0","source":{"id":"2410.06209","kind":"arxiv","version":8}},"canonical_sha256":"a50d1735560daed547b3d22ce8baa7b81cb0bb173ce408a87b48e87b3bd0fac6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a50d1735560daed547b3d22ce8baa7b81cb0bb173ce408a87b48e87b3bd0fac6","first_computed_at":"2026-07-05T10:25:11.442969Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:25:11.442969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cMrFWmZ65Bjx19Or/zZzktIFDUMf3m+diEoL+fzGoly+0DNPBomSfVZOZARyyqQg29rkxGs0UZ6+VZ+bPpfUBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:25:11.443596Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.06209","source_kind":"arxiv","source_version":8}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dbda555a9f8587c99aa9ec43a6e71d9120f7c1d0e2b7e01b8f52188e0c8898ee","sha256:2bd21681eb1f7a1034b6c99653e7cc635c3e02951dbf7a3a2aa95b0e186fe1c9"],"state_sha256":"8646c891a371f143c37bd4c31b0ff9f5774eb2d66279b9cd424ca7e5d6d24f40"}