{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:J75CIVMZRG7XQLI5U3YRFN6CAV","short_pith_number":"pith:J75CIVMZ","schema_version":"1.0","canonical_sha256":"4ffa24559989bf782d1da6f112b7c205663ef874f3a5858bc339ce6161679051","source":{"kind":"arxiv","id":"2011.13354","version":4},"attestation_state":"computed","paper":{"title":"Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aditya Kalyanpur, David Ferrucci, Tom Breloff","submitted_at":"2020-11-26T15:36:06Z","abstract_excerpt":"Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the \"knowledge acquisition\" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap prob"},"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":"2011.13354","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-26T15:36:06Z","cross_cats_sorted":[],"title_canon_sha256":"23de9f0da737519fdf18b246f956467cec56a4c4577b6d71fe951179701d3f58","abstract_canon_sha256":"7732c503c8832e60fc5631585509c28ed7f12bdb04d7cec36ba76496464468a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:37:39.523955Z","signature_b64":"TzTRYpkdtZ5pXl7xf7b2T+dIIzT8ziHg8oavKzTRiTlRtWSYKTEOQIN0JIkzddj2Zr2n9lWL53YOrCXqSnbjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ffa24559989bf782d1da6f112b7c205663ef874f3a5858bc339ce6161679051","last_reissued_at":"2026-07-05T03:37:39.523430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:37:39.523430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aditya Kalyanpur, David Ferrucci, Tom Breloff","submitted_at":"2020-11-26T15:36:06Z","abstract_excerpt":"Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the \"knowledge acquisition\" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.13354","kind":"arxiv","version":4},"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/2011.13354/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":"2011.13354","created_at":"2026-07-05T03:37:39.523496+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.13354v4","created_at":"2026-07-05T03:37:39.523496+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.13354","created_at":"2026-07-05T03:37:39.523496+00:00"},{"alias_kind":"pith_short_12","alias_value":"J75CIVMZRG7X","created_at":"2026-07-05T03:37:39.523496+00:00"},{"alias_kind":"pith_short_16","alias_value":"J75CIVMZRG7XQLI5","created_at":"2026-07-05T03:37:39.523496+00:00"},{"alias_kind":"pith_short_8","alias_value":"J75CIVMZ","created_at":"2026-07-05T03:37:39.523496+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/J75CIVMZRG7XQLI5U3YRFN6CAV","json":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV.json","graph_json":"https://pith.science/api/pith-number/J75CIVMZRG7XQLI5U3YRFN6CAV/graph.json","events_json":"https://pith.science/api/pith-number/J75CIVMZRG7XQLI5U3YRFN6CAV/events.json","paper":"https://pith.science/paper/J75CIVMZ"},"agent_actions":{"view_html":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV","download_json":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV.json","view_paper":"https://pith.science/paper/J75CIVMZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.13354&json=true","fetch_graph":"https://pith.science/api/pith-number/J75CIVMZRG7XQLI5U3YRFN6CAV/graph.json","fetch_events":"https://pith.science/api/pith-number/J75CIVMZRG7XQLI5U3YRFN6CAV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV/action/storage_attestation","attest_author":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV/action/author_attestation","sign_citation":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV/action/citation_signature","submit_replication":"https://pith.science/pith/J75CIVMZRG7XQLI5U3YRFN6CAV/action/replication_record"}},"created_at":"2026-07-05T03:37:39.523496+00:00","updated_at":"2026-07-05T03:37:39.523496+00:00"}