{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:S7DTTWGTP5AZPTNUKWTJ6T244K","short_pith_number":"pith:S7DTTWGT","schema_version":"1.0","canonical_sha256":"97c739d8d37f4197cdb455a69f4f5ce2b727bdeb2b73488dccbacf5aca783e4b","source":{"kind":"arxiv","id":"2606.10757","version":1},"attestation_state":"computed","paper":{"title":"Dynamical Partition Functions of Stochastic Dynamics via Variational Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.stat-mech","authors_text":"Ying Tang, Zequn Lin","submitted_at":"2026-06-09T12:10:24Z","abstract_excerpt":"Nonequilibrium thermodynamics is governed by the dynamical partition function, and its computation in high-dimensional continuous-state dynamics is a longstanding challenge. The Feynman-Kac formalism provides a rigorous representation for generating functions of arbitrary path observables; however, practical evaluation beyond low dimensions or the weak-noise limit is hindered by the curse of dimensionality and the exponentially growing replica demands of trajectory-based methods. Here we develop a mesh-free neural variational framework that realizes the Feynman-Kac theorem with generative flow"},"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":"2606.10757","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2026-06-09T12:10:24Z","cross_cats_sorted":[],"title_canon_sha256":"01c3f8e654134ba30417f0fa11e727f96052350f8a49f5aa987e11ac83df580c","abstract_canon_sha256":"a91b916bfb069015f3c2b37d6a450130b82bbe23b840cd88661d5b8c8118724a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:38.803603Z","signature_b64":"Fuy1MqvhMJBondPmmHr7tnJz1ftLak0mpbqOYKlBl5ZILj5PJEj+EK+ex/amvSaj2K9u3oQs/4Gv3eduWQkjCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97c739d8d37f4197cdb455a69f4f5ce2b727bdeb2b73488dccbacf5aca783e4b","last_reissued_at":"2026-06-10T01:10:38.802789Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:38.802789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamical Partition Functions of Stochastic Dynamics via Variational Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.stat-mech","authors_text":"Ying Tang, Zequn Lin","submitted_at":"2026-06-09T12:10:24Z","abstract_excerpt":"Nonequilibrium thermodynamics is governed by the dynamical partition function, and its computation in high-dimensional continuous-state dynamics is a longstanding challenge. The Feynman-Kac formalism provides a rigorous representation for generating functions of arbitrary path observables; however, practical evaluation beyond low dimensions or the weak-noise limit is hindered by the curse of dimensionality and the exponentially growing replica demands of trajectory-based methods. Here we develop a mesh-free neural variational framework that realizes the Feynman-Kac theorem with generative flow"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10757","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/2606.10757/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":"2606.10757","created_at":"2026-06-10T01:10:38.802913+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10757v1","created_at":"2026-06-10T01:10:38.802913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10757","created_at":"2026-06-10T01:10:38.802913+00:00"},{"alias_kind":"pith_short_12","alias_value":"S7DTTWGTP5AZ","created_at":"2026-06-10T01:10:38.802913+00:00"},{"alias_kind":"pith_short_16","alias_value":"S7DTTWGTP5AZPTNU","created_at":"2026-06-10T01:10:38.802913+00:00"},{"alias_kind":"pith_short_8","alias_value":"S7DTTWGT","created_at":"2026-06-10T01:10:38.802913+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.10757","citing_title":"Dynamical Partition Functions of Stochastic Dynamics via Variational Flows","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K","json":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K.json","graph_json":"https://pith.science/api/pith-number/S7DTTWGTP5AZPTNUKWTJ6T244K/graph.json","events_json":"https://pith.science/api/pith-number/S7DTTWGTP5AZPTNUKWTJ6T244K/events.json","paper":"https://pith.science/paper/S7DTTWGT"},"agent_actions":{"view_html":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K","download_json":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K.json","view_paper":"https://pith.science/paper/S7DTTWGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10757&json=true","fetch_graph":"https://pith.science/api/pith-number/S7DTTWGTP5AZPTNUKWTJ6T244K/graph.json","fetch_events":"https://pith.science/api/pith-number/S7DTTWGTP5AZPTNUKWTJ6T244K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K/action/storage_attestation","attest_author":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K/action/author_attestation","sign_citation":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K/action/citation_signature","submit_replication":"https://pith.science/pith/S7DTTWGTP5AZPTNUKWTJ6T244K/action/replication_record"}},"created_at":"2026-06-10T01:10:38.802913+00:00","updated_at":"2026-06-10T01:10:38.802913+00:00"}