{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZZWIW6ZQYDU3VO2RTCUZ7LAC4F","short_pith_number":"pith:ZZWIW6ZQ","canonical_record":{"source":{"id":"2209.02606","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T15:52:51Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"a1e2f1e21d17c74faec3c61f785745cd175186d93e28103bfae55f5d43e21251","abstract_canon_sha256":"64754a13fb4f5ec702786658c321ab4c3692d2bf959db8838795e897b906942a"},"schema_version":"1.0"},"canonical_sha256":"ce6c8b7b30c0e9babb5198a99fac02e165250a0eac7dadf5a85c72df5d3b465c","source":{"kind":"arxiv","id":"2209.02606","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.02606","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"arxiv_version","alias_value":"2209.02606v2","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.02606","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_12","alias_value":"ZZWIW6ZQYDU3","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZZWIW6ZQYDU3VO2R","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZZWIW6ZQ","created_at":"2026-07-05T05:37:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZZWIW6ZQYDU3VO2RTCUZ7LAC4F","target":"record","payload":{"canonical_record":{"source":{"id":"2209.02606","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T15:52:51Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"a1e2f1e21d17c74faec3c61f785745cd175186d93e28103bfae55f5d43e21251","abstract_canon_sha256":"64754a13fb4f5ec702786658c321ab4c3692d2bf959db8838795e897b906942a"},"schema_version":"1.0"},"canonical_sha256":"ce6c8b7b30c0e9babb5198a99fac02e165250a0eac7dadf5a85c72df5d3b465c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:07.685229Z","signature_b64":"s7AsASRYA6l5JfElUv9Ro/nzbhLwNTk9ll0KQOkcDlIyef7pFb9FjCmDXdZoWzd3hRta4NtpOhR1UOFJQReWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce6c8b7b30c0e9babb5198a99fac02e165250a0eac7dadf5a85c72df5d3b465c","last_reissued_at":"2026-07-05T05:37:07.684734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:07.684734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.02606","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:37:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RsM9Nu/dXmiCPE40tRnhxq0t/yDGZ49Tt5gz5YF58JjisT0V3Rwkk8K8cqVaE4+mP+Y+QF8mFyVBUj6b7MXKBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:15:24.220977Z"},"content_sha256":"14eb24390e1057d57cd2c372e94b136abe6b53c071d7f19891773f71ce2ec41f","schema_version":"1.0","event_id":"sha256:14eb24390e1057d57cd2c372e94b136abe6b53c071d7f19891773f71ce2ec41f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZZWIW6ZQYDU3VO2RTCUZ7LAC4F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unifying Generative Models with GFlowNets and Beyond","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dinghuai Zhang, Nikolay Malkin, Ricky T. Q. Chen, Yoshua Bengio","submitted_at":"2022-09-06T15:52:51Z","abstract_excerpt":"There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02606","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/2209.02606/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:37:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Srlz7kT96PMdxyrCZWuslZxv/fzTNP9lRx1uAkGFLpkoSKb79q0srar//AyV4jYHBSWmURazYPE2WFatX/NmBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:15:24.221515Z"},"content_sha256":"86130448cbffdf619ff8b93f5a4f8085f9091ef815429ce548b9d0ad72e7c8f2","schema_version":"1.0","event_id":"sha256:86130448cbffdf619ff8b93f5a4f8085f9091ef815429ce548b9d0ad72e7c8f2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/bundle.json","state_url":"https://pith.science/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T17:15:24Z","links":{"resolver":"https://pith.science/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F","bundle":"https://pith.science/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/bundle.json","state":"https://pith.science/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZZWIW6ZQYDU3VO2RTCUZ7LAC4F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZZWIW6ZQYDU3VO2RTCUZ7LAC4F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"64754a13fb4f5ec702786658c321ab4c3692d2bf959db8838795e897b906942a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T15:52:51Z","title_canon_sha256":"a1e2f1e21d17c74faec3c61f785745cd175186d93e28103bfae55f5d43e21251"},"schema_version":"1.0","source":{"id":"2209.02606","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.02606","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"arxiv_version","alias_value":"2209.02606v2","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.02606","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_12","alias_value":"ZZWIW6ZQYDU3","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZZWIW6ZQYDU3VO2R","created_at":"2026-07-05T05:37:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZZWIW6ZQ","created_at":"2026-07-05T05:37:07Z"}],"graph_snapshots":[{"event_id":"sha256:86130448cbffdf619ff8b93f5a4f8085f9091ef815429ce548b9d0ad72e7c8f2","target":"graph","created_at":"2026-07-05T05:37:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2209.02606/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route","authors_text":"Dinghuai Zhang, Nikolay Malkin, Ricky T. Q. Chen, Yoshua Bengio","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T15:52:51Z","title":"Unifying Generative Models with GFlowNets and Beyond"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.02606","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:14eb24390e1057d57cd2c372e94b136abe6b53c071d7f19891773f71ce2ec41f","target":"record","created_at":"2026-07-05T05:37:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"64754a13fb4f5ec702786658c321ab4c3692d2bf959db8838795e897b906942a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-06T15:52:51Z","title_canon_sha256":"a1e2f1e21d17c74faec3c61f785745cd175186d93e28103bfae55f5d43e21251"},"schema_version":"1.0","source":{"id":"2209.02606","kind":"arxiv","version":2}},"canonical_sha256":"ce6c8b7b30c0e9babb5198a99fac02e165250a0eac7dadf5a85c72df5d3b465c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce6c8b7b30c0e9babb5198a99fac02e165250a0eac7dadf5a85c72df5d3b465c","first_computed_at":"2026-07-05T05:37:07.684734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:07.684734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s7AsASRYA6l5JfElUv9Ro/nzbhLwNTk9ll0KQOkcDlIyef7pFb9FjCmDXdZoWzd3hRta4NtpOhR1UOFJQReWAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:07.685229Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.02606","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14eb24390e1057d57cd2c372e94b136abe6b53c071d7f19891773f71ce2ec41f","sha256:86130448cbffdf619ff8b93f5a4f8085f9091ef815429ce548b9d0ad72e7c8f2"],"state_sha256":"46b770b9ca5af8f05b42ce525e5c008ab8683b961eac193fa5efe9922811326c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jfFPIG2SXM64dL5wZI3v8WupItifOh1LBb1HAccro84gZGepfCWcKyYe7m5iM5EZdGNB5Xv4KH1lsl1lQhQdAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:15:24.227054Z","bundle_sha256":"c884facca66ddeac0c22022094a36868d9c06545cbe7f4fb3591ede7b7ac2d62"}}