{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JGHU72Z5KPCLYJPC7YEEC63VES","short_pith_number":"pith:JGHU72Z5","schema_version":"1.0","canonical_sha256":"498f4feb3d53c4bc25e2fe08417b7524b109d6d18be76ba9a349efd0bbb81cd0","source":{"kind":"arxiv","id":"2607.10254","version":1},"attestation_state":"computed","paper":{"title":"Neural feedback approximation for stochastic control with degenerate diffusions: error estimates and numerical analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Jean-Fran\\c{c}ois Chassagneux, Marco Scaratti, Olivier Bokanowski, Xavier Warin","submitted_at":"2026-07-11T10:45:22Z","abstract_excerpt":"We study finite-horizon stochastic optimal control problems and approximate the resulting time-discrete formulation by a direct policy-learning problem over neural-network feedback maps. We prove a quantitative convergence estimate, in an averaged sense, for the error between the time-discrete value and the value induced by an approximately optimized neural policy. The bound separates the approximation of near-optimal feedback policies, the localization of stochastic trajectories on compact sets, and the optimization tolerance in training. The analysis does not require transition-density assum"},"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":"2607.10254","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-07-11T10:45:22Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"db2a3456cd2fe6d300ad85f764b86d1fda62e531d8b93c469830a906c39b6438","abstract_canon_sha256":"c573442c7220b462b64920f748b7baf4c3f0d3e92a5bcd8b29b32ef6dfc3f3fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:33.071551Z","signature_b64":"cgOvTGo238BogAbOctkAlFL9MtKY1rRHMexFRtn5kEOzkPptCCG4qzAOuvgVAaHBGK/gIlOt82htVmUywCZBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"498f4feb3d53c4bc25e2fe08417b7524b109d6d18be76ba9a349efd0bbb81cd0","last_reissued_at":"2026-07-14T01:20:33.070665Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:33.070665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural feedback approximation for stochastic control with degenerate diffusions: error estimates and numerical analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Jean-Fran\\c{c}ois Chassagneux, Marco Scaratti, Olivier Bokanowski, Xavier Warin","submitted_at":"2026-07-11T10:45:22Z","abstract_excerpt":"We study finite-horizon stochastic optimal control problems and approximate the resulting time-discrete formulation by a direct policy-learning problem over neural-network feedback maps. We prove a quantitative convergence estimate, in an averaged sense, for the error between the time-discrete value and the value induced by an approximately optimized neural policy. The bound separates the approximation of near-optimal feedback policies, the localization of stochastic trajectories on compact sets, and the optimization tolerance in training. The analysis does not require transition-density assum"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10254","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/2607.10254/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":"2607.10254","created_at":"2026-07-14T01:20:33.071111+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.10254v1","created_at":"2026-07-14T01:20:33.071111+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10254","created_at":"2026-07-14T01:20:33.071111+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGHU72Z5KPCL","created_at":"2026-07-14T01:20:33.071111+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGHU72Z5KPCLYJPC","created_at":"2026-07-14T01:20:33.071111+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGHU72Z5","created_at":"2026-07-14T01:20:33.071111+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/JGHU72Z5KPCLYJPC7YEEC63VES","json":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES.json","graph_json":"https://pith.science/api/pith-number/JGHU72Z5KPCLYJPC7YEEC63VES/graph.json","events_json":"https://pith.science/api/pith-number/JGHU72Z5KPCLYJPC7YEEC63VES/events.json","paper":"https://pith.science/paper/JGHU72Z5"},"agent_actions":{"view_html":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES","download_json":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES.json","view_paper":"https://pith.science/paper/JGHU72Z5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.10254&json=true","fetch_graph":"https://pith.science/api/pith-number/JGHU72Z5KPCLYJPC7YEEC63VES/graph.json","fetch_events":"https://pith.science/api/pith-number/JGHU72Z5KPCLYJPC7YEEC63VES/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES/action/storage_attestation","attest_author":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES/action/author_attestation","sign_citation":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES/action/citation_signature","submit_replication":"https://pith.science/pith/JGHU72Z5KPCLYJPC7YEEC63VES/action/replication_record"}},"created_at":"2026-07-14T01:20:33.071111+00:00","updated_at":"2026-07-14T01:20:33.071111+00:00"}