{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZBJFLFIEA374NC5SZSFQ5HEFPF","short_pith_number":"pith:ZBJFLFIE","schema_version":"1.0","canonical_sha256":"c85255950406ffc68bb2cc8b0e9c85794bc1bab1a4da165d87c60f9dc24c29e6","source":{"kind":"arxiv","id":"2507.14335","version":1},"attestation_state":"computed","paper":{"title":"ProofCompass: Enhancing Specialized Provers with LLM Guidance","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Claudio Mayrink Verdun, Francesco Noseda, Gabriel Poesia, Nicolas Wischermann","submitted_at":"2025-07-18T19:28:01Z","abstract_excerpt":"Language models have become increasingly powerful tools for formal mathematical reasoning. However, most existing approaches rely exclusively on either large general-purpose models or smaller specialized models, each with distinct limitations, while training specialized large models still requires significant computational resources. This paper introduces ProofCompass, a novel hybrid methodology that achieves remarkable computational efficiency by strategically guiding existing specialized prover methods, such as DeepSeek-Prover-v1.5-RL (DSP-v1.5) with a Large Language Model (LLM) without requ"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.14335","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-18T19:28:01Z","cross_cats_sorted":[],"title_canon_sha256":"e54a55e8b8c1b8b602509f09e908628719a8575e7623c6a488c9fa8b73d7f126","abstract_canon_sha256":"14b0f839933e472e775b6c4bb145be95277328438649bcca928a53fdb1ab7dd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:04.463183Z","signature_b64":"F1HNhpewRdYWSUwFjfE/Ydm7z7YuTaS2P82xbUK87RkcMjV/NUIhSTNCrr8o/q/C3YeamSSTs12ZXfHQQTKrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c85255950406ffc68bb2cc8b0e9c85794bc1bab1a4da165d87c60f9dc24c29e6","last_reissued_at":"2026-07-05T11:40:04.462729Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:04.462729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ProofCompass: Enhancing Specialized Provers with LLM Guidance","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Claudio Mayrink Verdun, Francesco Noseda, Gabriel Poesia, Nicolas Wischermann","submitted_at":"2025-07-18T19:28:01Z","abstract_excerpt":"Language models have become increasingly powerful tools for formal mathematical reasoning. However, most existing approaches rely exclusively on either large general-purpose models or smaller specialized models, each with distinct limitations, while training specialized large models still requires significant computational resources. This paper introduces ProofCompass, a novel hybrid methodology that achieves remarkable computational efficiency by strategically guiding existing specialized prover methods, such as DeepSeek-Prover-v1.5-RL (DSP-v1.5) with a Large Language Model (LLM) without requ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14335","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.14335/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.14335","created_at":"2026-07-05T11:40:04.462783+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.14335v1","created_at":"2026-07-05T11:40:04.462783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14335","created_at":"2026-07-05T11:40:04.462783+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZBJFLFIEA374","created_at":"2026-07-05T11:40:04.462783+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZBJFLFIEA374NC5S","created_at":"2026-07-05T11:40:04.462783+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZBJFLFIE","created_at":"2026-07-05T11:40:04.462783+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07779","citing_title":"From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier","ref_index":255,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF","json":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF.json","graph_json":"https://pith.science/api/pith-number/ZBJFLFIEA374NC5SZSFQ5HEFPF/graph.json","events_json":"https://pith.science/api/pith-number/ZBJFLFIEA374NC5SZSFQ5HEFPF/events.json","paper":"https://pith.science/paper/ZBJFLFIE"},"agent_actions":{"view_html":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF","download_json":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF.json","view_paper":"https://pith.science/paper/ZBJFLFIE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.14335&json=true","fetch_graph":"https://pith.science/api/pith-number/ZBJFLFIEA374NC5SZSFQ5HEFPF/graph.json","fetch_events":"https://pith.science/api/pith-number/ZBJFLFIEA374NC5SZSFQ5HEFPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF/action/storage_attestation","attest_author":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF/action/author_attestation","sign_citation":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF/action/citation_signature","submit_replication":"https://pith.science/pith/ZBJFLFIEA374NC5SZSFQ5HEFPF/action/replication_record"}},"created_at":"2026-07-05T11:40:04.462783+00:00","updated_at":"2026-07-05T11:40:04.462783+00:00"}