{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YN5T25Z35FS37HMKNGD5U3MHBH","short_pith_number":"pith:YN5T25Z3","schema_version":"1.0","canonical_sha256":"c37b3d773be965bf9d8a6987da6d8709dbe584d50f5b6437e03d58a6e249593d","source":{"kind":"arxiv","id":"2309.01156","version":1},"attestation_state":"computed","paper":{"title":"Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"hep-lat","authors_text":"Danilo J. Rezende, Gurtej Kanwar, Kyle Cranmer, Phiala E. Shanahan, S\\'ebastien Racani\\`ere","submitted_at":"2023-09-03T12:25:59Z","abstract_excerpt":"Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective out"},"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":"2309.01156","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-lat","submitted_at":"2023-09-03T12:25:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0eec0ae077ba552de9ea1c2a14f77eaaa606df52c4a00cc621428edeb340e87b","abstract_canon_sha256":"40515757a1a78401e9f513f00be4933875a3e40bd21aa1eb965a64be6972aacd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:33.588711Z","signature_b64":"RyrNQKTyYz170hA+0AWMBP3LH8EvrcGhpQKD6JJtujOZTi7G8YvxkQ342h7ILqjYOm0XDW7S3QNtmOdBq1QBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c37b3d773be965bf9d8a6987da6d8709dbe584d50f5b6437e03d58a6e249593d","last_reissued_at":"2026-07-05T06:47:33.588147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:33.588147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"hep-lat","authors_text":"Danilo J. Rezende, Gurtej Kanwar, Kyle Cranmer, Phiala E. Shanahan, S\\'ebastien Racani\\`ere","submitted_at":"2023-09-03T12:25:59Z","abstract_excerpt":"Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective out"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.01156","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/2309.01156/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":"2309.01156","created_at":"2026-07-05T06:47:33.588210+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.01156v1","created_at":"2026-07-05T06:47:33.588210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.01156","created_at":"2026-07-05T06:47:33.588210+00:00"},{"alias_kind":"pith_short_12","alias_value":"YN5T25Z35FS3","created_at":"2026-07-05T06:47:33.588210+00:00"},{"alias_kind":"pith_short_16","alias_value":"YN5T25Z35FS37HMK","created_at":"2026-07-05T06:47:33.588210+00:00"},{"alias_kind":"pith_short_8","alias_value":"YN5T25Z3","created_at":"2026-07-05T06:47:33.588210+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08505","citing_title":"Diffusion Models for Sampling Near Criticality in Lattice Field Theories","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"2606.31492","citing_title":"Higher-order hopping-parameter expansion by human-AI collaboration","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2601.19520","citing_title":"Intrinsic Width of the Flux Tube as a tool to explore confining mechanisms in Lattice Gauge Theories","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22444","citing_title":"Normalizing flows for all-orders QED corrections in lattice field theory","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2510.25704","citing_title":"Scaling flow-based approaches for topology sampling in $\\mathrm{SU}(3)$ gauge theory","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07262","citing_title":"Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH","json":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH.json","graph_json":"https://pith.science/api/pith-number/YN5T25Z35FS37HMKNGD5U3MHBH/graph.json","events_json":"https://pith.science/api/pith-number/YN5T25Z35FS37HMKNGD5U3MHBH/events.json","paper":"https://pith.science/paper/YN5T25Z3"},"agent_actions":{"view_html":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH","download_json":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH.json","view_paper":"https://pith.science/paper/YN5T25Z3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.01156&json=true","fetch_graph":"https://pith.science/api/pith-number/YN5T25Z35FS37HMKNGD5U3MHBH/graph.json","fetch_events":"https://pith.science/api/pith-number/YN5T25Z35FS37HMKNGD5U3MHBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH/action/storage_attestation","attest_author":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH/action/author_attestation","sign_citation":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH/action/citation_signature","submit_replication":"https://pith.science/pith/YN5T25Z35FS37HMKNGD5U3MHBH/action/replication_record"}},"created_at":"2026-07-05T06:47:33.588210+00:00","updated_at":"2026-07-05T06:47:33.588210+00:00"}