{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IHA2MT6VTGP6NU2ZFS7X7GGJTS","short_pith_number":"pith:IHA2MT6V","schema_version":"1.0","canonical_sha256":"41c1a64fd5999fe6d3592cbf7f98c99c97d6265b7fc443e14da227291725ee3f","source":{"kind":"arxiv","id":"2407.07327","version":1},"attestation_state":"computed","paper":{"title":"Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheng-Lin Liu, Fei Yin, Liang Lin, Ming-Liang Zhang, Zhong-Zhi Li","submitted_at":"2024-07-10T02:45:22Z","abstract_excerpt":"Geometry problem solving (GPS) requires capacities of multi-modal understanding, multi-hop reasoning and theorem knowledge application. In this paper, we propose a neural-symbolic model for plane geometry problem solving (PGPS), named PGPSNet-v2, with three key steps: modal fusion, reasoning process and knowledge verification. In modal fusion, we leverage textual clauses to express fine-grained structural and semantic content of geometry diagram, and fuse diagram with textual problem efficiently through structural-semantic pre-training. For reasoning, we design an explicable solution program t"},"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":"2407.07327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-10T02:45:22Z","cross_cats_sorted":[],"title_canon_sha256":"f332f23364c4b7cda7d283d046c8b21731d1ca1c4791379f0682fcf20b7d57c0","abstract_canon_sha256":"4e28242002f7cb2e63cddcda3f6a0ce5b9b42f8478cf973af366fb99b96533ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:09.914326Z","signature_b64":"QftKY1LVFBlI0K4afKO1K5EzJ/tOecadErqdA4aVq7xfVjdVZF3hWgn3dJl+nXEd7LXzRA97fNclXWeYg33VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41c1a64fd5999fe6d3592cbf7f98c99c97d6265b7fc443e14da227291725ee3f","last_reissued_at":"2026-07-05T08:42:09.913921Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:09.913921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheng-Lin Liu, Fei Yin, Liang Lin, Ming-Liang Zhang, Zhong-Zhi Li","submitted_at":"2024-07-10T02:45:22Z","abstract_excerpt":"Geometry problem solving (GPS) requires capacities of multi-modal understanding, multi-hop reasoning and theorem knowledge application. In this paper, we propose a neural-symbolic model for plane geometry problem solving (PGPS), named PGPSNet-v2, with three key steps: modal fusion, reasoning process and knowledge verification. In modal fusion, we leverage textual clauses to express fine-grained structural and semantic content of geometry diagram, and fuse diagram with textual problem efficiently through structural-semantic pre-training. For reasoning, we design an explicable solution program t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07327","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/2407.07327/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":"2407.07327","created_at":"2026-07-05T08:42:09.913974+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.07327v1","created_at":"2026-07-05T08:42:09.913974+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07327","created_at":"2026-07-05T08:42:09.913974+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHA2MT6VTGP6","created_at":"2026-07-05T08:42:09.913974+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHA2MT6VTGP6NU2Z","created_at":"2026-07-05T08:42:09.913974+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHA2MT6V","created_at":"2026-07-05T08:42:09.913974+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20743","citing_title":"Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS","json":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS.json","graph_json":"https://pith.science/api/pith-number/IHA2MT6VTGP6NU2ZFS7X7GGJTS/graph.json","events_json":"https://pith.science/api/pith-number/IHA2MT6VTGP6NU2ZFS7X7GGJTS/events.json","paper":"https://pith.science/paper/IHA2MT6V"},"agent_actions":{"view_html":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS","download_json":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS.json","view_paper":"https://pith.science/paper/IHA2MT6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.07327&json=true","fetch_graph":"https://pith.science/api/pith-number/IHA2MT6VTGP6NU2ZFS7X7GGJTS/graph.json","fetch_events":"https://pith.science/api/pith-number/IHA2MT6VTGP6NU2ZFS7X7GGJTS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS/action/storage_attestation","attest_author":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS/action/author_attestation","sign_citation":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS/action/citation_signature","submit_replication":"https://pith.science/pith/IHA2MT6VTGP6NU2ZFS7X7GGJTS/action/replication_record"}},"created_at":"2026-07-05T08:42:09.913974+00:00","updated_at":"2026-07-05T08:42:09.913974+00:00"}