{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:I2DDFGW3NYSL7TCTU6HMBCNHXY","short_pith_number":"pith:I2DDFGW3","canonical_record":{"source":{"id":"2310.07138","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-11T02:23:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"75309b853a6322a1d2f9c4dcd2f94e7374bc087d6acfd7a946de8fad9c631d9f","abstract_canon_sha256":"da3d16887a8acd17cda6421e12863529892f9d88ddbe921d4f1f36cca538a668"},"schema_version":"1.0"},"canonical_sha256":"4686329adb6e24bfcc53a78ec089a7be2c2a9cad242fcf1bdb050b6f7dad91f1","source":{"kind":"arxiv","id":"2310.07138","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.07138","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"arxiv_version","alias_value":"2310.07138v3","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.07138","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_12","alias_value":"I2DDFGW3NYSL","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_16","alias_value":"I2DDFGW3NYSL7TCT","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_8","alias_value":"I2DDFGW3","created_at":"2026-07-05T07:47:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:I2DDFGW3NYSL7TCTU6HMBCNHXY","target":"record","payload":{"canonical_record":{"source":{"id":"2310.07138","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-11T02:23:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"75309b853a6322a1d2f9c4dcd2f94e7374bc087d6acfd7a946de8fad9c631d9f","abstract_canon_sha256":"da3d16887a8acd17cda6421e12863529892f9d88ddbe921d4f1f36cca538a668"},"schema_version":"1.0"},"canonical_sha256":"4686329adb6e24bfcc53a78ec089a7be2c2a9cad242fcf1bdb050b6f7dad91f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:26.959339Z","signature_b64":"rnQwtj8hVX8UXKraOiiO4ROrCEvWEysoxpmsxqWJ1BcNVNDuVzhsXPXe9RY9AOLbFEdWPwdpza9ltdXs+6QCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4686329adb6e24bfcc53a78ec089a7be2c2a9cad242fcf1bdb050b6f7dad91f1","last_reissued_at":"2026-07-05T07:47:26.958821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:26.958821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.07138","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:47:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ywbJe4LPYOBS06faTG/MAuJc/v/YU0pIDhg3nbPSihTlI2CMaBKSYin/nbsdMB5lDhuqfRsdCsm7zEtCMGm5CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:38:12.410027Z"},"content_sha256":"2c5fb3adda1ed3d58c2236fc09618ccef7aa8f65790ad76ab7e64ea6581d92e3","schema_version":"1.0","event_id":"sha256:2c5fb3adda1ed3d58c2236fc09618ccef7aa8f65790ad76ab7e64ea6581d92e3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:I2DDFGW3NYSL7TCTU6HMBCNHXY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Denoising Task Routing for Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Byeongjun Park, Changick Kim, Hyojun Go, Jin-Young Kim, Sangmin Woo","submitted_at":"2023-10-11T02:23:18Z","abstract_excerpt":"Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion models. In this paper, we present Denoising Task Routing (DTR), a simple add-on strategy for existing diffusion model architectures to establish distinct information pathways for individual tasks within a single architecture by selectively acti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.07138","kind":"arxiv","version":3},"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/2310.07138/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":"2026-07-05T07:47:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p9P2iJmpdbwg/4CL+ci26UFd5Kc9wpzeENfy5bgSXFj5JrHxmKxBORVQOfgMyxGm2+QEb1Iy5UiavXs8VaMhAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:38:12.410680Z"},"content_sha256":"ad6dc33b17866cbb3e25bc31ec0202db2901cee1bc34c3917958eca97d10fc57","schema_version":"1.0","event_id":"sha256:ad6dc33b17866cbb3e25bc31ec0202db2901cee1bc34c3917958eca97d10fc57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/bundle.json","state_url":"https://pith.science/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/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":"2026-08-08T19:38:12Z","links":{"resolver":"https://pith.science/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY","bundle":"https://pith.science/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/bundle.json","state":"https://pith.science/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2DDFGW3NYSL7TCTU6HMBCNHXY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:I2DDFGW3NYSL7TCTU6HMBCNHXY","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":"da3d16887a8acd17cda6421e12863529892f9d88ddbe921d4f1f36cca538a668","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-11T02:23:18Z","title_canon_sha256":"75309b853a6322a1d2f9c4dcd2f94e7374bc087d6acfd7a946de8fad9c631d9f"},"schema_version":"1.0","source":{"id":"2310.07138","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.07138","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"arxiv_version","alias_value":"2310.07138v3","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.07138","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_12","alias_value":"I2DDFGW3NYSL","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_16","alias_value":"I2DDFGW3NYSL7TCT","created_at":"2026-07-05T07:47:26Z"},{"alias_kind":"pith_short_8","alias_value":"I2DDFGW3","created_at":"2026-07-05T07:47:26Z"}],"graph_snapshots":[{"event_id":"sha256:ad6dc33b17866cbb3e25bc31ec0202db2901cee1bc34c3917958eca97d10fc57","target":"graph","created_at":"2026-07-05T07:47:26Z","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/2310.07138/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion models. In this paper, we present Denoising Task Routing (DTR), a simple add-on strategy for existing diffusion model architectures to establish distinct information pathways for individual tasks within a single architecture by selectively acti","authors_text":"Byeongjun Park, Changick Kim, Hyojun Go, Jin-Young Kim, Sangmin Woo","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-11T02:23:18Z","title":"Denoising Task Routing for Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.07138","kind":"arxiv","version":3},"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":"sha256:2c5fb3adda1ed3d58c2236fc09618ccef7aa8f65790ad76ab7e64ea6581d92e3","target":"record","created_at":"2026-07-05T07:47:26Z","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":"da3d16887a8acd17cda6421e12863529892f9d88ddbe921d4f1f36cca538a668","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-11T02:23:18Z","title_canon_sha256":"75309b853a6322a1d2f9c4dcd2f94e7374bc087d6acfd7a946de8fad9c631d9f"},"schema_version":"1.0","source":{"id":"2310.07138","kind":"arxiv","version":3}},"canonical_sha256":"4686329adb6e24bfcc53a78ec089a7be2c2a9cad242fcf1bdb050b6f7dad91f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4686329adb6e24bfcc53a78ec089a7be2c2a9cad242fcf1bdb050b6f7dad91f1","first_computed_at":"2026-07-05T07:47:26.958821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:47:26.958821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rnQwtj8hVX8UXKraOiiO4ROrCEvWEysoxpmsxqWJ1BcNVNDuVzhsXPXe9RY9AOLbFEdWPwdpza9ltdXs+6QCCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:47:26.959339Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.07138","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c5fb3adda1ed3d58c2236fc09618ccef7aa8f65790ad76ab7e64ea6581d92e3","sha256:ad6dc33b17866cbb3e25bc31ec0202db2901cee1bc34c3917958eca97d10fc57"],"state_sha256":"c010e33439e531e535a3bf5c630b6868b4c8de3a3e6adfd2619fdee3c03e55a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j/0jbBm9Ym6Us6yajGdMUMi+73VNlQraHpmyhh4j7TNucrBx1zBY2QkkhqFEd35i7vjuc097Egz0I/A74f9WBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:38:12.416613Z","bundle_sha256":"6e2ef1ffa0b90576bcf347b9d0dbf6dbb7bb536b30983223378d58363741df41"}}