{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VBWV4FD2LDTZ56KTKZ2QGVVN7S","short_pith_number":"pith:VBWV4FD2","schema_version":"1.0","canonical_sha256":"a86d5e147a58e79ef95356750356adfc9bafef433ca29600b612e0465b910f9b","source":{"kind":"arxiv","id":"2606.26872","version":1},"attestation_state":"computed","paper":{"title":"SpatialFlow-GRPO: Where Spatial Credit Drives Image Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Wen, Fan Yang, Han Li, Hongyang Wei, Shuo Yang, Tingting Gao, Wei Chen, Xingyu Lu, Yancheng Long, Yankai Yang","submitted_at":"2026-06-25T10:58:25Z","abstract_excerpt":"Recent online reinforcement learning has substantially improved image editing quality. However, existing Flow-GRPO-style methods usually rely on a single whole-image reward, which makes fine-grained editing optimization difficult. We observe that a key obstacle in image editing is this spatial uniformity assumption: a whole-image reward cannot distinguish how different spatial regions contribute to image quality. To address this issue, we propose SpatialFlow-GRPO, a training framework that introduces spatially fine-grained reward feedback. The framework converts region-aware rewards into seman"},"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":"2606.26872","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T10:58:25Z","cross_cats_sorted":[],"title_canon_sha256":"09705745251b724ddf862f1950d0010c77e9c89c4c6f9eac41acafd65cb8afea","abstract_canon_sha256":"80131f42d4b1eae06372a6034aa5e977d3fce804f8d9b31cfbb0a919f8ea03fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:02.460225Z","signature_b64":"wk8Co4X7wnVUxjxlMk2xcZ2PIMDY495TsoBRIOi1LqCjiJlnD+q0OKYUwPB4wbzELu8Q1DT8guiH6mihj4JyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a86d5e147a58e79ef95356750356adfc9bafef433ca29600b612e0465b910f9b","last_reissued_at":"2026-06-26T01:16:02.459785Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:02.459785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpatialFlow-GRPO: Where Spatial Credit Drives Image Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Wen, Fan Yang, Han Li, Hongyang Wei, Shuo Yang, Tingting Gao, Wei Chen, Xingyu Lu, Yancheng Long, Yankai Yang","submitted_at":"2026-06-25T10:58:25Z","abstract_excerpt":"Recent online reinforcement learning has substantially improved image editing quality. However, existing Flow-GRPO-style methods usually rely on a single whole-image reward, which makes fine-grained editing optimization difficult. We observe that a key obstacle in image editing is this spatial uniformity assumption: a whole-image reward cannot distinguish how different spatial regions contribute to image quality. To address this issue, we propose SpatialFlow-GRPO, a training framework that introduces spatially fine-grained reward feedback. The framework converts region-aware rewards into seman"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26872","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/2606.26872/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":"2606.26872","created_at":"2026-06-26T01:16:02.459850+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26872v1","created_at":"2026-06-26T01:16:02.459850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26872","created_at":"2026-06-26T01:16:02.459850+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBWV4FD2LDTZ","created_at":"2026-06-26T01:16:02.459850+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBWV4FD2LDTZ56KT","created_at":"2026-06-26T01:16:02.459850+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBWV4FD2","created_at":"2026-06-26T01:16:02.459850+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S","json":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S.json","graph_json":"https://pith.science/api/pith-number/VBWV4FD2LDTZ56KTKZ2QGVVN7S/graph.json","events_json":"https://pith.science/api/pith-number/VBWV4FD2LDTZ56KTKZ2QGVVN7S/events.json","paper":"https://pith.science/paper/VBWV4FD2"},"agent_actions":{"view_html":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S","download_json":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S.json","view_paper":"https://pith.science/paper/VBWV4FD2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26872&json=true","fetch_graph":"https://pith.science/api/pith-number/VBWV4FD2LDTZ56KTKZ2QGVVN7S/graph.json","fetch_events":"https://pith.science/api/pith-number/VBWV4FD2LDTZ56KTKZ2QGVVN7S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S/action/storage_attestation","attest_author":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S/action/author_attestation","sign_citation":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S/action/citation_signature","submit_replication":"https://pith.science/pith/VBWV4FD2LDTZ56KTKZ2QGVVN7S/action/replication_record"}},"created_at":"2026-06-26T01:16:02.459850+00:00","updated_at":"2026-06-26T01:16:02.459850+00:00"}