{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PLEXSAJKX5H5WPA7LZ5QPFEF77","short_pith_number":"pith:PLEXSAJK","canonical_record":{"source":{"id":"2605.19317","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:53:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6313533c960541ec05e94daba6fa57481fbc257dbb9e6a31c10e888c1646da48","abstract_canon_sha256":"b1248d0aab66a77537e3105e3a743b3563c1fbc6050efee497133e263f97699f"},"schema_version":"1.0"},"canonical_sha256":"7ac979012abf4fdb3c1f5e7b079485ffe9985980d3a3079a3f51f585e9781bfb","source":{"kind":"arxiv","id":"2605.19317","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19317","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19317v1","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19317","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_12","alias_value":"PLEXSAJKX5H5","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_16","alias_value":"PLEXSAJKX5H5WPA7","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_8","alias_value":"PLEXSAJK","created_at":"2026-05-20T01:05:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PLEXSAJKX5H5WPA7LZ5QPFEF77","target":"record","payload":{"canonical_record":{"source":{"id":"2605.19317","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:53:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6313533c960541ec05e94daba6fa57481fbc257dbb9e6a31c10e888c1646da48","abstract_canon_sha256":"b1248d0aab66a77537e3105e3a743b3563c1fbc6050efee497133e263f97699f"},"schema_version":"1.0"},"canonical_sha256":"7ac979012abf4fdb3c1f5e7b079485ffe9985980d3a3079a3f51f585e9781bfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:38.965973Z","signature_b64":"63/aQf2mgEIATyTCofZKX0UjenDq0MU83TMSJEqPLp41bYENTEV55/eI8i5FnQYwbHseXlDUuajDj+mPj3sEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ac979012abf4fdb3c1f5e7b079485ffe9985980d3a3079a3f51f585e9781bfb","last_reissued_at":"2026-05-20T01:05:38.965217Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:38.965217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.19317","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:05:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6IfbOtAkGDkW4/HhMRWPIDwBsczYtZtxfV1rsDDrjVPElGB4qOcbpCIBnV713UrUWZfbObZZslgFOdgGUNWdAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:19:25.670136Z"},"content_sha256":"e53b05e3f55ab9cd253e59fe858aed1c728f834d9abd490f541f14e5b0c86956","schema_version":"1.0","event_id":"sha256:e53b05e3f55ab9cd253e59fe858aed1c728f834d9abd490f541f14e5b0c86956"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PLEXSAJKX5H5WPA7LZ5QPFEF77","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jaesik Yoon, Sungjin Ahn, Taegu Kang","submitted_at":"2026-05-19T03:53:50Z","abstract_excerpt":"Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference-time scaling methods for diffusion models typically rely on external verifiers or reward models to rank and select samples, limiting their scalability to settings where such evaluators are available and reliable. Moreover, while recent diffusion models perform sequential inference with region-wise, mixed-noise conditioning, inference-time scaling tailored to this setting remains relatively underexplored. We propose Itera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19317","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/2605.19317/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":"2026-05-20T01:05:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QUVtbJrTnXRFniHDHAKkv/HfxktcTaK9byVYshLwWMFyY2poXfwuxM/8D7ikhBnnEPxRlwv8xtippiqleNZICQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:19:25.670543Z"},"content_sha256":"d62c3c5c9573ffa7c9bcf28f5fc7ee894bb0c14d9441c8f8f2fe0fe53c0ef1ab","schema_version":"1.0","event_id":"sha256:d62c3c5c9573ffa7c9bcf28f5fc7ee894bb0c14d9441c8f8f2fe0fe53c0ef1ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/bundle.json","state_url":"https://pith.science/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/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":"2026-05-26T21:19:25Z","links":{"resolver":"https://pith.science/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77","bundle":"https://pith.science/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/bundle.json","state":"https://pith.science/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PLEXSAJKX5H5WPA7LZ5QPFEF77/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PLEXSAJKX5H5WPA7LZ5QPFEF77","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":"b1248d0aab66a77537e3105e3a743b3563c1fbc6050efee497133e263f97699f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:53:50Z","title_canon_sha256":"6313533c960541ec05e94daba6fa57481fbc257dbb9e6a31c10e888c1646da48"},"schema_version":"1.0","source":{"id":"2605.19317","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19317","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19317v1","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19317","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_12","alias_value":"PLEXSAJKX5H5","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_16","alias_value":"PLEXSAJKX5H5WPA7","created_at":"2026-05-20T01:05:38Z"},{"alias_kind":"pith_short_8","alias_value":"PLEXSAJK","created_at":"2026-05-20T01:05:38Z"}],"graph_snapshots":[{"event_id":"sha256:d62c3c5c9573ffa7c9bcf28f5fc7ee894bb0c14d9441c8f8f2fe0fe53c0ef1ab","target":"graph","created_at":"2026-05-20T01:05:38Z","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/2605.19317/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference-time scaling methods for diffusion models typically rely on external verifiers or reward models to rank and select samples, limiting their scalability to settings where such evaluators are available and reliable. Moreover, while recent diffusion models perform sequential inference with region-wise, mixed-noise conditioning, inference-time scaling tailored to this setting remains relatively underexplored. We propose Itera","authors_text":"Jaesik Yoon, Sungjin Ahn, Taegu Kang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:53:50Z","title":"Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19317","kind":"arxiv","version":1},"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:e53b05e3f55ab9cd253e59fe858aed1c728f834d9abd490f541f14e5b0c86956","target":"record","created_at":"2026-05-20T01:05:38Z","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":"b1248d0aab66a77537e3105e3a743b3563c1fbc6050efee497133e263f97699f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T03:53:50Z","title_canon_sha256":"6313533c960541ec05e94daba6fa57481fbc257dbb9e6a31c10e888c1646da48"},"schema_version":"1.0","source":{"id":"2605.19317","kind":"arxiv","version":1}},"canonical_sha256":"7ac979012abf4fdb3c1f5e7b079485ffe9985980d3a3079a3f51f585e9781bfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ac979012abf4fdb3c1f5e7b079485ffe9985980d3a3079a3f51f585e9781bfb","first_computed_at":"2026-05-20T01:05:38.965217Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:38.965217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"63/aQf2mgEIATyTCofZKX0UjenDq0MU83TMSJEqPLp41bYENTEV55/eI8i5FnQYwbHseXlDUuajDj+mPj3sEBA==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:38.965973Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.19317","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e53b05e3f55ab9cd253e59fe858aed1c728f834d9abd490f541f14e5b0c86956","sha256:d62c3c5c9573ffa7c9bcf28f5fc7ee894bb0c14d9441c8f8f2fe0fe53c0ef1ab"],"state_sha256":"7777836b8186f69ee24ca1b8e99f0d479100015eeace1f1f49fd469d2d1efe23"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KIFfRGlEqUmPZQUTrjkg3ySPx2WufUn1BGUTM+w4BvD/D0F6ZxeV9c44916O0ER4FHVzETLlP9G/P2mbfr87Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T21:19:25.672844Z","bundle_sha256":"174106f71ff5c82dfc80383cf3fdd575567e5b1361b9545632e0f8982e5e92ec"}}