{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KKPBDVEDWGXIFTOBZ3FKPJOXNP","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":"0ad65a9e54aaa02f1bc164ee9c75cfb2e1644a80a1a19004586706ef48ea5075","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2024-03-14T11:00:09Z","title_canon_sha256":"64995177b8dfe127fc1f09d7b9b67d6156080a1a679a5521255f10d4a1420db1"},"schema_version":"1.0","source":{"id":"2403.14690","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.14690","created_at":"2026-07-05T07:59:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.14690v1","created_at":"2026-07-05T07:59:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.14690","created_at":"2026-07-05T07:59:24Z"},{"alias_kind":"pith_short_12","alias_value":"KKPBDVEDWGXI","created_at":"2026-07-05T07:59:24Z"},{"alias_kind":"pith_short_16","alias_value":"KKPBDVEDWGXIFTOB","created_at":"2026-07-05T07:59:24Z"},{"alias_kind":"pith_short_8","alias_value":"KKPBDVED","created_at":"2026-07-05T07:59:24Z"}],"graph_snapshots":[{"event_id":"sha256:19ccb5282c3370c4b67cd5331fe3a453fbb24848a1ac56a74ba0c376dd1f6d3b","target":"graph","created_at":"2026-07-05T07:59:24Z","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/2403.14690/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made. The state-of-the-art performance has been achieved by exhausting a","authors_text":"Gongqi Lin, Hongguang Fu, Lei Huang, Liang Xu, Shengyuan Yan, Siwen Jiang, Wei Fang, Xiuqin Zhong","cross_cats":["cs.AI","cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2024-03-14T11:00:09Z","title":"Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.14690","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:a2950e34a0203e5ee99a6119add8fd75912ca40337b7ee927dc5422a5aacf276","target":"record","created_at":"2026-07-05T07:59:24Z","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":"0ad65a9e54aaa02f1bc164ee9c75cfb2e1644a80a1a19004586706ef48ea5075","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2024-03-14T11:00:09Z","title_canon_sha256":"64995177b8dfe127fc1f09d7b9b67d6156080a1a679a5521255f10d4a1420db1"},"schema_version":"1.0","source":{"id":"2403.14690","kind":"arxiv","version":1}},"canonical_sha256":"529e11d483b1ae82cdc1cecaa7a5d76bf42b82b7f845b96f2ab07ced47cfa9c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"529e11d483b1ae82cdc1cecaa7a5d76bf42b82b7f845b96f2ab07ced47cfa9c2","first_computed_at":"2026-07-05T07:59:24.642575Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:59:24.642575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"byzWI9hGAKQmQylTyKWMPQ+PftrOKWyR8HYCIpot2/4/3Bgf1O6dUu5r1Lz+K300yP38y46OwoUkK0aVlZhGBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:59:24.643138Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.14690","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2950e34a0203e5ee99a6119add8fd75912ca40337b7ee927dc5422a5aacf276","sha256:19ccb5282c3370c4b67cd5331fe3a453fbb24848a1ac56a74ba0c376dd1f6d3b"],"state_sha256":"ff1a3ddf31113486b2b33cd011a3e7ef91e203defbd2b11e38ac4abc0d26a382"}