{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ADGWT56JN7H72PXRYIXNX7CENC","short_pith_number":"pith:ADGWT56J","schema_version":"1.0","canonical_sha256":"00cd69f7c96fcffd3ef1c22edbfc44689eaba6cf2e4419ac8d77364f74d566af","source":{"kind":"arxiv","id":"2505.16312","version":2},"attestation_state":"computed","paper":{"title":"EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Defu Lian, Jianshu Zhang, Jiawei Liu, Qisi Chen, Quan Liu","submitted_at":"2025-05-22T07:07:43Z","abstract_excerpt":"Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalence in domain-specific contexts like mathematical reasoning. To address this, we propose EquivPruner, a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search. We also introduce MathEquiv, the first dataset we created for mathematical statemen"},"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":"2505.16312","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-22T07:07:43Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"2cb819b7ff1768c126113e8c8b8f62be51c476ebef39810093b80ea93f125232","abstract_canon_sha256":"d8cccf78c97ca87ae119757282e0245c9bd90f7a32e1b4bbc4f9aeccb2ca6c29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:13:45.570785Z","signature_b64":"TDvAoKZDNVg9yCdO7Yk/eXfo1v0MWg1aSO5AU/kt0BZagUy+QgOWIUXlrrEewBe2iKidyVdGmbFN+GvjzrHxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00cd69f7c96fcffd3ef1c22edbfc44689eaba6cf2e4419ac8d77364f74d566af","last_reissued_at":"2026-06-23T03:13:45.570299Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:13:45.570299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Defu Lian, Jianshu Zhang, Jiawei Liu, Qisi Chen, Quan Liu","submitted_at":"2025-05-22T07:07:43Z","abstract_excerpt":"Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalence in domain-specific contexts like mathematical reasoning. To address this, we propose EquivPruner, a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search. We also introduce MathEquiv, the first dataset we created for mathematical statemen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16312","kind":"arxiv","version":2},"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/2505.16312/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":"2505.16312","created_at":"2026-06-23T03:13:45.570370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16312v2","created_at":"2026-06-23T03:13:45.570370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16312","created_at":"2026-06-23T03:13:45.570370+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADGWT56JN7H7","created_at":"2026-06-23T03:13:45.570370+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADGWT56JN7H72PXR","created_at":"2026-06-23T03:13:45.570370+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADGWT56J","created_at":"2026-06-23T03:13:45.570370+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC","json":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC.json","graph_json":"https://pith.science/api/pith-number/ADGWT56JN7H72PXRYIXNX7CENC/graph.json","events_json":"https://pith.science/api/pith-number/ADGWT56JN7H72PXRYIXNX7CENC/events.json","paper":"https://pith.science/paper/ADGWT56J"},"agent_actions":{"view_html":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC","download_json":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC.json","view_paper":"https://pith.science/paper/ADGWT56J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16312&json=true","fetch_graph":"https://pith.science/api/pith-number/ADGWT56JN7H72PXRYIXNX7CENC/graph.json","fetch_events":"https://pith.science/api/pith-number/ADGWT56JN7H72PXRYIXNX7CENC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC/action/storage_attestation","attest_author":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC/action/author_attestation","sign_citation":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC/action/citation_signature","submit_replication":"https://pith.science/pith/ADGWT56JN7H72PXRYIXNX7CENC/action/replication_record"}},"created_at":"2026-06-23T03:13:45.570370+00:00","updated_at":"2026-06-23T03:13:45.570370+00:00"}