{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FZ3DVK3OHUEFE7EKTHOS7OWO2G","short_pith_number":"pith:FZ3DVK3O","schema_version":"1.0","canonical_sha256":"2e763aab6e3d08527c8a99dd2fbaced1b2f3adbe9204169f6ce6f164a9e74b59","source":{"kind":"arxiv","id":"1801.03562","version":1},"attestation_state":"computed","paper":{"title":"Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Paul Smolensky, Paul Tupper, Pyeong Whan Cho","submitted_at":"2018-01-04T21:30:05Z","abstract_excerpt":"Gradient Symbolic Computation is proposed as a means of solving discrete global optimization problems using a neurally plausible continuous stochastic dynamical system. Gradient symbolic dynamics involves two free parameters that must be adjusted as a function of time to obtain the global maximizer at the end of the computation. We provide a summary of what is known about the GSC dynamics for special cases of settings of the parameters, and also establish that there is a schedule for the two parameters for which convergence to the correct answer occurs with high probability. These results put "},"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":"1801.03562","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-04T21:30:05Z","cross_cats_sorted":[],"title_canon_sha256":"822152f9567b80f11e526115fde7efaf5244a7c050c75c11337f8300f40889de","abstract_canon_sha256":"1d95cb9859881420bea08d9dc5d19ef4321ecc96dcd14b5966b5db336983ddc2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:13.922774Z","signature_b64":"3uwy1E6BGdSOCgws9Ox8uihYaeK5unbJBjT5nghUFnpkXEpJ7/a6YnOIIvgH1Ox7zkCUEqSSFUp8hxLWwKA5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e763aab6e3d08527c8a99dd2fbaced1b2f3adbe9204169f6ce6f164a9e74b59","last_reissued_at":"2026-05-18T00:26:13.922088Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:13.922088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Paul Smolensky, Paul Tupper, Pyeong Whan Cho","submitted_at":"2018-01-04T21:30:05Z","abstract_excerpt":"Gradient Symbolic Computation is proposed as a means of solving discrete global optimization problems using a neurally plausible continuous stochastic dynamical system. Gradient symbolic dynamics involves two free parameters that must be adjusted as a function of time to obtain the global maximizer at the end of the computation. We provide a summary of what is known about the GSC dynamics for special cases of settings of the parameters, and also establish that there is a schedule for the two parameters for which convergence to the correct answer occurs with high probability. These results put "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.03562","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":""},"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":"1801.03562","created_at":"2026-05-18T00:26:13.922201+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.03562v1","created_at":"2026-05-18T00:26:13.922201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.03562","created_at":"2026-05-18T00:26:13.922201+00:00"},{"alias_kind":"pith_short_12","alias_value":"FZ3DVK3OHUEF","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"FZ3DVK3OHUEFE7EK","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"FZ3DVK3O","created_at":"2026-05-18T12:32:25.280505+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/FZ3DVK3OHUEFE7EKTHOS7OWO2G","json":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G.json","graph_json":"https://pith.science/api/pith-number/FZ3DVK3OHUEFE7EKTHOS7OWO2G/graph.json","events_json":"https://pith.science/api/pith-number/FZ3DVK3OHUEFE7EKTHOS7OWO2G/events.json","paper":"https://pith.science/paper/FZ3DVK3O"},"agent_actions":{"view_html":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G","download_json":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G.json","view_paper":"https://pith.science/paper/FZ3DVK3O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.03562&json=true","fetch_graph":"https://pith.science/api/pith-number/FZ3DVK3OHUEFE7EKTHOS7OWO2G/graph.json","fetch_events":"https://pith.science/api/pith-number/FZ3DVK3OHUEFE7EKTHOS7OWO2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G/action/storage_attestation","attest_author":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G/action/author_attestation","sign_citation":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G/action/citation_signature","submit_replication":"https://pith.science/pith/FZ3DVK3OHUEFE7EKTHOS7OWO2G/action/replication_record"}},"created_at":"2026-05-18T00:26:13.922201+00:00","updated_at":"2026-05-18T00:26:13.922201+00:00"}