{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OKJYEBU5Z6ZAOVF37KU2A6DPAW","short_pith_number":"pith:OKJYEBU5","schema_version":"1.0","canonical_sha256":"729382069dcfb20754bbfaa9a0786f0582462513176e20a6dfd428a42c9e5149","source":{"kind":"arxiv","id":"2602.07635","version":2},"attestation_state":"computed","paper":{"title":"Data Compression with Stochastic Codes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Deniz G\\\"und\\\"uz, Gergely Flamich","submitted_at":"2026-02-07T17:29:43Z","abstract_excerpt":"Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry.\n  This paper describes one such direction -- relative entropy coding -- which focuses on constructing stochastic codes, mainly as an alternative to quantisation and entropy coding in lossy source coding. Our primary aim is to provide a broad overview of the topic, with an emphasis on the computational and practical aspects currently missing from the literature.\n  Our goal is threefold: for the curious reader, we aim to provide an intuitive picture o"},"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":"2602.07635","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2026-02-07T17:29:43Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"9e6f9b929f0a1aa143052027668a2a89ab13c9b84c4c069828dfd875b6e2dc9e","abstract_canon_sha256":"5d91d6186a7864c07ef26ce0e8065dfd3ae758f89dab66c4946abfd8f216c7cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:19.971205Z","signature_b64":"5a9bSeNWVVHOhNsuciqWjeFhellgf2w3NJpbxTNZIoH0rjaZ3b57dU6quaVjFJS6Bdf/RGvdCfCGtHPuXC02BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"729382069dcfb20754bbfaa9a0786f0582462513176e20a6dfd428a42c9e5149","last_reissued_at":"2026-06-09T02:07:19.970016Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:19.970016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Compression with Stochastic Codes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Deniz G\\\"und\\\"uz, Gergely Flamich","submitted_at":"2026-02-07T17:29:43Z","abstract_excerpt":"Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry.\n  This paper describes one such direction -- relative entropy coding -- which focuses on constructing stochastic codes, mainly as an alternative to quantisation and entropy coding in lossy source coding. Our primary aim is to provide a broad overview of the topic, with an emphasis on the computational and practical aspects currently missing from the literature.\n  Our goal is threefold: for the curious reader, we aim to provide an intuitive picture o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.07635","kind":"arxiv","version":2},"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/2602.07635/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":"2602.07635","created_at":"2026-06-09T02:07:19.970178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.07635v2","created_at":"2026-06-09T02:07:19.970178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.07635","created_at":"2026-06-09T02:07:19.970178+00:00"},{"alias_kind":"pith_short_12","alias_value":"OKJYEBU5Z6ZA","created_at":"2026-06-09T02:07:19.970178+00:00"},{"alias_kind":"pith_short_16","alias_value":"OKJYEBU5Z6ZAOVF3","created_at":"2026-06-09T02:07:19.970178+00:00"},{"alias_kind":"pith_short_8","alias_value":"OKJYEBU5","created_at":"2026-06-09T02:07:19.970178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2604.23076","citing_title":"Rejection Sampling is Optimal for Relative Entropy Coding","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2604.06055","citing_title":"Singular Relative Entropy Coding with Bits-Back Rejection Sampling","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW","json":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW.json","graph_json":"https://pith.science/api/pith-number/OKJYEBU5Z6ZAOVF37KU2A6DPAW/graph.json","events_json":"https://pith.science/api/pith-number/OKJYEBU5Z6ZAOVF37KU2A6DPAW/events.json","paper":"https://pith.science/paper/OKJYEBU5"},"agent_actions":{"view_html":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW","download_json":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW.json","view_paper":"https://pith.science/paper/OKJYEBU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.07635&json=true","fetch_graph":"https://pith.science/api/pith-number/OKJYEBU5Z6ZAOVF37KU2A6DPAW/graph.json","fetch_events":"https://pith.science/api/pith-number/OKJYEBU5Z6ZAOVF37KU2A6DPAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW/action/storage_attestation","attest_author":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW/action/author_attestation","sign_citation":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW/action/citation_signature","submit_replication":"https://pith.science/pith/OKJYEBU5Z6ZAOVF37KU2A6DPAW/action/replication_record"}},"created_at":"2026-06-09T02:07:19.970178+00:00","updated_at":"2026-06-09T02:07:19.970178+00:00"}