{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XZZK36CDVHTVWUH3CHMI3BJPGP","short_pith_number":"pith:XZZK36CD","canonical_record":{"source":{"id":"2607.19064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T12:55:34Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","eess.IV"],"title_canon_sha256":"6bb283f4a45ddbc7d01073df94252ae595dbc081ec00cadd235a3652fdd6c5c6","abstract_canon_sha256":"6265ae04f1d7cdac5bbf74cd02095a5252a061888b91d784566aece12dbbb488"},"schema_version":"1.0"},"canonical_sha256":"be72adf843a9e75b50fb11d88d852f33fa3455a24fddbda006544d2099d1ba93","source":{"kind":"arxiv","id":"2607.19064","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.19064","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"arxiv_version","alias_value":"2607.19064v1","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19064","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_12","alias_value":"XZZK36CDVHTV","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_16","alias_value":"XZZK36CDVHTVWUH3","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_8","alias_value":"XZZK36CD","created_at":"2026-07-22T01:24:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XZZK36CDVHTVWUH3CHMI3BJPGP","target":"record","payload":{"canonical_record":{"source":{"id":"2607.19064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T12:55:34Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","eess.IV"],"title_canon_sha256":"6bb283f4a45ddbc7d01073df94252ae595dbc081ec00cadd235a3652fdd6c5c6","abstract_canon_sha256":"6265ae04f1d7cdac5bbf74cd02095a5252a061888b91d784566aece12dbbb488"},"schema_version":"1.0"},"canonical_sha256":"be72adf843a9e75b50fb11d88d852f33fa3455a24fddbda006544d2099d1ba93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T01:24:06.917254Z","signature_b64":"VBw00aAJbIw02lchcIPDvrsxv16DXnENU0Kqs8/9Ix6bE6u+w3j2SuqMWx2hfwR2v3TAzEB4J2VD8N9C8x2aAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be72adf843a9e75b50fb11d88d852f33fa3455a24fddbda006544d2099d1ba93","last_reissued_at":"2026-07-22T01:24:06.916416Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T01:24:06.916416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.19064","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":"2026-07-22T01:24:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gyf3ehAEmguoOHBYQjh9QPzby6RUvOz3XKsimV0ukG9TdDI8W8D2YK4BMv2f005iOYjFpg+fLpThUiDlcFXDDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:52:43.580690Z"},"content_sha256":"32e7a8ddeaef0ba7e65d570c781ea7fe4b84a883bd4fef53c996ab6d075dbfe7","schema_version":"1.0","event_id":"sha256:32e7a8ddeaef0ba7e65d570c781ea7fe4b84a883bd4fef53c996ab6d075dbfe7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XZZK36CDVHTVWUH3CHMI3BJPGP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM","eess.IV"],"primary_cat":"cs.CV","authors_text":"Bin Li, Dongnan Gui, Fanyi Pu, Jiahao Li, Jinghao Guo, Jinglu Wang, Kaichen Zhang, Peng Zhang, Senqiao Yang, Shicheng Zheng, Tianci Bi, Wenxuan Xie, Xiao Li, Xiaoyi Zhang, Xinjie Zhang, Xun Guo, Yan Lu, Yifei Shen, Yuxuan Luo, Zhaoyang Jia, Zhening Liu, Zihan Zheng, Zimo Wen, Zongyu Guo","submitted_at":"2026-07-21T12:55:34Z","abstract_excerpt":"Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19064","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/2607.19064/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":"2026-07-22T01:24:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q2BjftyIQj6ydJ3ny9NnPicM5BzMyJESsWi8bEmOZ5ccvoHJp3Te+9oKyprimsFBP7p9r17ThtWr6NgVX/8tAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:52:43.581021Z"},"content_sha256":"1b7ec1afc43130bafeef2b8a05bf305e47799fa4f5ab7b2056e8089a675aa716","schema_version":"1.0","event_id":"sha256:1b7ec1afc43130bafeef2b8a05bf305e47799fa4f5ab7b2056e8089a675aa716"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/bundle.json","state_url":"https://pith.science/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/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":"2026-08-04T15:52:43Z","links":{"resolver":"https://pith.science/pith/XZZK36CDVHTVWUH3CHMI3BJPGP","bundle":"https://pith.science/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/bundle.json","state":"https://pith.science/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XZZK36CDVHTVWUH3CHMI3BJPGP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XZZK36CDVHTVWUH3CHMI3BJPGP","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":"6265ae04f1d7cdac5bbf74cd02095a5252a061888b91d784566aece12dbbb488","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T12:55:34Z","title_canon_sha256":"6bb283f4a45ddbc7d01073df94252ae595dbc081ec00cadd235a3652fdd6c5c6"},"schema_version":"1.0","source":{"id":"2607.19064","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.19064","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"arxiv_version","alias_value":"2607.19064v1","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19064","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_12","alias_value":"XZZK36CDVHTV","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_16","alias_value":"XZZK36CDVHTVWUH3","created_at":"2026-07-22T01:24:06Z"},{"alias_kind":"pith_short_8","alias_value":"XZZK36CD","created_at":"2026-07-22T01:24:06Z"}],"graph_snapshots":[{"event_id":"sha256:1b7ec1afc43130bafeef2b8a05bf305e47799fa4f5ab7b2056e8089a675aa716","target":"graph","created_at":"2026-07-22T01:24:06Z","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/2607.19064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while ","authors_text":"Bin Li, Dongnan Gui, Fanyi Pu, Jiahao Li, Jinghao Guo, Jinglu Wang, Kaichen Zhang, Peng Zhang, Senqiao Yang, Shicheng Zheng, Tianci Bi, Wenxuan Xie, Xiao Li, Xiaoyi Zhang, Xinjie Zhang, Xun Guo, Yan Lu, Yifei Shen, Yuxuan Luo, Zhaoyang Jia, Zhening Liu, Zihan Zheng, Zimo Wen, Zongyu Guo","cross_cats":["cs.AI","cs.LG","cs.MM","eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T12:55:34Z","title":"Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19064","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":"sha256:32e7a8ddeaef0ba7e65d570c781ea7fe4b84a883bd4fef53c996ab6d075dbfe7","target":"record","created_at":"2026-07-22T01:24:06Z","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":"6265ae04f1d7cdac5bbf74cd02095a5252a061888b91d784566aece12dbbb488","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T12:55:34Z","title_canon_sha256":"6bb283f4a45ddbc7d01073df94252ae595dbc081ec00cadd235a3652fdd6c5c6"},"schema_version":"1.0","source":{"id":"2607.19064","kind":"arxiv","version":1}},"canonical_sha256":"be72adf843a9e75b50fb11d88d852f33fa3455a24fddbda006544d2099d1ba93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be72adf843a9e75b50fb11d88d852f33fa3455a24fddbda006544d2099d1ba93","first_computed_at":"2026-07-22T01:24:06.916416Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-22T01:24:06.916416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VBw00aAJbIw02lchcIPDvrsxv16DXnENU0Kqs8/9Ix6bE6u+w3j2SuqMWx2hfwR2v3TAzEB4J2VD8N9C8x2aAw==","signature_status":"signed_v1","signed_at":"2026-07-22T01:24:06.917254Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.19064","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32e7a8ddeaef0ba7e65d570c781ea7fe4b84a883bd4fef53c996ab6d075dbfe7","sha256:1b7ec1afc43130bafeef2b8a05bf305e47799fa4f5ab7b2056e8089a675aa716"],"state_sha256":"36abb38f1a74a2c307f1fc42e3126a522bfe249a42e9c40ce02664b372d4d349"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZKNMKdSVEg4vO6G0bOUnZOVaux8tZlKFr7ER5Q6/sJoe785bZHPVuwt4f4UuK0+Ib0/V9tIcxmSxRxnci/gDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:52:43.584632Z","bundle_sha256":"63cf9147d82bc3c32378fe8bfc0182e8148bb943f37704bd67365f40e8d1377e"}}