{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:CHS4PVKDMP2FL7P2MYDAJVL7EF","short_pith_number":"pith:CHS4PVKD","canonical_record":{"source":{"id":"2607.26715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T10:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"08a88985c010d73514e3016ced42d4e911be54f82f37892288297a0ddb505836","abstract_canon_sha256":"4171d1b045266d31410a96306c8277775569a9287ba652cd690b9c22d05360b1"},"schema_version":"1.0"},"canonical_sha256":"11e5c7d54363f455fdfa660604d57f214fa52c2cf8456e8af63aa72ebe9cf854","source":{"kind":"arxiv","id":"2607.26715","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26715","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26715v1","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26715","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"CHS4PVKDMP2F","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_16","alias_value":"CHS4PVKDMP2FL7P2","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_8","alias_value":"CHS4PVKD","created_at":"2026-07-30T01:22:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:CHS4PVKDMP2FL7P2MYDAJVL7EF","target":"record","payload":{"canonical_record":{"source":{"id":"2607.26715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T10:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"08a88985c010d73514e3016ced42d4e911be54f82f37892288297a0ddb505836","abstract_canon_sha256":"4171d1b045266d31410a96306c8277775569a9287ba652cd690b9c22d05360b1"},"schema_version":"1.0"},"canonical_sha256":"11e5c7d54363f455fdfa660604d57f214fa52c2cf8456e8af63aa72ebe9cf854","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11e5c7d54363f455fdfa660604d57f214fa52c2cf8456e8af63aa72ebe9cf854","last_reissued_at":"2026-07-30T01:22:01.083357Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:22:01.083357Z"},"source_kind":"arxiv","source_id":"2607.26715","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":"2026-07-30T01:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UL6oJlw/d15l+1Q6TPYKAn42pSYa4BgUGgznme6MNeDqqHK9Pa6HMPFAHQFBo9o5mpFwbTx/4ieIwwLe633jDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:30:49.197188Z"},"content_sha256":"f3b254a340b45e6b8b6850fa17b9dd3f058f658d942d8a33d53e5ec718d9826a","schema_version":"1.0","event_id":"sha256:f3b254a340b45e6b8b6850fa17b9dd3f058f658d942d8a33d53e5ec718d9826a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:CHS4PVKDMP2FL7P2MYDAJVL7EF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Patrick Le Callet, Yan Huang, Yinan Wang, Yong Xu","submitted_at":"2026-07-29T10:05:36Z","abstract_excerpt":"Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization. For illumination recovery, we propose an illumination transfer attention (ITA), which re-weights the self-attention m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26715","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/2607.26715/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":"2026-07-30T01:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"imt2H9hXk+Reaa4rqyjXVhqUbkJOUcWcqMXq2AyEfVA16cqkDRY75KAOD/QjRQuNwlTuFeLW3K9+2/b/O0lpBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:30:49.198171Z"},"content_sha256":"3f4aca9d05c79f81c6bd9f54f0ea3c631ac3d26279aca7e98cda8104a6df1289","schema_version":"1.0","event_id":"sha256:3f4aca9d05c79f81c6bd9f54f0ea3c631ac3d26279aca7e98cda8104a6df1289"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/bundle.json","state_url":"https://pith.science/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/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":"2026-08-05T18:30:49Z","links":{"resolver":"https://pith.science/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF","bundle":"https://pith.science/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/bundle.json","state":"https://pith.science/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CHS4PVKDMP2FL7P2MYDAJVL7EF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CHS4PVKDMP2FL7P2MYDAJVL7EF","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":"4171d1b045266d31410a96306c8277775569a9287ba652cd690b9c22d05360b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T10:05:36Z","title_canon_sha256":"08a88985c010d73514e3016ced42d4e911be54f82f37892288297a0ddb505836"},"schema_version":"1.0","source":{"id":"2607.26715","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26715","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26715v1","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26715","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"CHS4PVKDMP2F","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_16","alias_value":"CHS4PVKDMP2FL7P2","created_at":"2026-07-30T01:22:01Z"},{"alias_kind":"pith_short_8","alias_value":"CHS4PVKD","created_at":"2026-07-30T01:22:01Z"}],"graph_snapshots":[{"event_id":"sha256:3f4aca9d05c79f81c6bd9f54f0ea3c631ac3d26279aca7e98cda8104a6df1289","target":"graph","created_at":"2026-07-30T01:22:01Z","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/2607.26715/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization. For illumination recovery, we propose an illumination transfer attention (ITA), which re-weights the self-attention m","authors_text":"Patrick Le Callet, Yan Huang, Yinan Wang, Yong Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T10:05:36Z","title":"FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26715","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:f3b254a340b45e6b8b6850fa17b9dd3f058f658d942d8a33d53e5ec718d9826a","target":"record","created_at":"2026-07-30T01:22:01Z","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":"4171d1b045266d31410a96306c8277775569a9287ba652cd690b9c22d05360b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T10:05:36Z","title_canon_sha256":"08a88985c010d73514e3016ced42d4e911be54f82f37892288297a0ddb505836"},"schema_version":"1.0","source":{"id":"2607.26715","kind":"arxiv","version":1}},"canonical_sha256":"11e5c7d54363f455fdfa660604d57f214fa52c2cf8456e8af63aa72ebe9cf854","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"11e5c7d54363f455fdfa660604d57f214fa52c2cf8456e8af63aa72ebe9cf854","first_computed_at":"2026-07-30T01:22:01.083357Z","kind":"pith_receipt","last_reissued_at":"2026-07-30T01:22:01.083357Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.26715","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3b254a340b45e6b8b6850fa17b9dd3f058f658d942d8a33d53e5ec718d9826a","sha256:3f4aca9d05c79f81c6bd9f54f0ea3c631ac3d26279aca7e98cda8104a6df1289"],"state_sha256":"37172e34beb84f2dd73f3903f2e9a974d99162ebd202020f40ec66a058782247"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gtelUYEGeoeCqDotcp2HnXrMmqMXXNM/95WfHR8rmXKseMrYtCpB9FIcajczcWu/xdsunSapAcP+FBGo/KJlBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T18:30:49.204773Z","bundle_sha256":"7d9973bf78ac31cedb03cdb269330c61a2b7326ac18aee6c3ec501a467d1c19e"}}