{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RXMKZEHT6EQIHUW3MWEIKHRR7A","short_pith_number":"pith:RXMKZEHT","canonical_record":{"source":{"id":"2607.06553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-07T17:54:28Z","cross_cats_sorted":[],"title_canon_sha256":"91856d4c09282a067996d28b3c6bfc24b58579178f066f50b222c589ac697281","abstract_canon_sha256":"c2a178e89929a1a6c57fe5ffc1fe5d1e11011f6ee4d26f968ba94e83d29e030f"},"schema_version":"1.0"},"canonical_sha256":"8dd8ac90f3f12083d2db6588851e31f81488b640ee3cabd39d78e1227e7c2303","source":{"kind":"arxiv","id":"2607.06553","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.06553","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"arxiv_version","alias_value":"2607.06553v1","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.06553","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_12","alias_value":"RXMKZEHT6EQI","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_16","alias_value":"RXMKZEHT6EQIHUW3","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_8","alias_value":"RXMKZEHT","created_at":"2026-07-08T01:19:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RXMKZEHT6EQIHUW3MWEIKHRR7A","target":"record","payload":{"canonical_record":{"source":{"id":"2607.06553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-07T17:54:28Z","cross_cats_sorted":[],"title_canon_sha256":"91856d4c09282a067996d28b3c6bfc24b58579178f066f50b222c589ac697281","abstract_canon_sha256":"c2a178e89929a1a6c57fe5ffc1fe5d1e11011f6ee4d26f968ba94e83d29e030f"},"schema_version":"1.0"},"canonical_sha256":"8dd8ac90f3f12083d2db6588851e31f81488b640ee3cabd39d78e1227e7c2303","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:19:29.756766Z","signature_b64":"4V+ke6meurVtEj3lF9gOEXya5u1OGm5yZhpkaz2exCJyL9Y0y56wennMxM2+nPVhBPWQ7078E/MNfUKX64GVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dd8ac90f3f12083d2db6588851e31f81488b640ee3cabd39d78e1227e7c2303","last_reissued_at":"2026-07-08T01:19:29.756325Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:19:29.756325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.06553","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":"2026-07-08T01:19:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"splDA4jJvY8WsGUvCl5jsb+Lv5hGDt3J+o+OBcMzc+evMUBI4Gch0CVNFbAPaLaarn9+sJsriUu0+h/W5f03Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:16:10.897099Z"},"content_sha256":"23b7fbc2632ede629bc3b38eea9e768706080f016edc4ddc7a159a92a5879f86","schema_version":"1.0","event_id":"sha256:23b7fbc2632ede629bc3b38eea9e768706080f016edc4ddc7a159a92a5879f86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RXMKZEHT6EQIHUW3MWEIKHRR7A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dengyang Jiang, Haodong Li, Pengtao Xie, Xin Lin, Yijiang Li, Zanyi Wang","submitted_at":"2026-07-07T17:54:28Z","abstract_excerpt":"Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets. We argue this inherits more of the generative output interface than dense prediction requires: unlike RGB synthesis, dense prediction asks for pixel-correct, t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.06553","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/2607.06553/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":"2026-07-08T01:19:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g2CVIF5p8Pt49pkGKJOo/xR/VsbFKeibFqK1KfERxIVLLabMj8cGbpUSAKcjuigstEpjIdDuTgXDN2r/v3tJDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:16:10.897444Z"},"content_sha256":"f6674654a67bfd25a20b3facc68999cbb5dde2dcf32f73176ffbe702e79e034a","schema_version":"1.0","event_id":"sha256:f6674654a67bfd25a20b3facc68999cbb5dde2dcf32f73176ffbe702e79e034a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/bundle.json","state_url":"https://pith.science/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/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":"2026-08-19T10:16:10Z","links":{"resolver":"https://pith.science/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A","bundle":"https://pith.science/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/bundle.json","state":"https://pith.science/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RXMKZEHT6EQIHUW3MWEIKHRR7A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RXMKZEHT6EQIHUW3MWEIKHRR7A","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":"c2a178e89929a1a6c57fe5ffc1fe5d1e11011f6ee4d26f968ba94e83d29e030f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-07T17:54:28Z","title_canon_sha256":"91856d4c09282a067996d28b3c6bfc24b58579178f066f50b222c589ac697281"},"schema_version":"1.0","source":{"id":"2607.06553","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.06553","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"arxiv_version","alias_value":"2607.06553v1","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.06553","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_12","alias_value":"RXMKZEHT6EQI","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_16","alias_value":"RXMKZEHT6EQIHUW3","created_at":"2026-07-08T01:19:29Z"},{"alias_kind":"pith_short_8","alias_value":"RXMKZEHT","created_at":"2026-07-08T01:19:29Z"}],"graph_snapshots":[{"event_id":"sha256:f6674654a67bfd25a20b3facc68999cbb5dde2dcf32f73176ffbe702e79e034a","target":"graph","created_at":"2026-07-08T01:19:29Z","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/2607.06553/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets. We argue this inherits more of the generative output interface than dense prediction requires: unlike RGB synthesis, dense prediction asks for pixel-correct, t","authors_text":"Dengyang Jiang, Haodong Li, Pengtao Xie, Xin Lin, Yijiang Li, Zanyi Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-07T17:54:28Z","title":"From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.06553","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":"sha256:23b7fbc2632ede629bc3b38eea9e768706080f016edc4ddc7a159a92a5879f86","target":"record","created_at":"2026-07-08T01:19:29Z","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":"c2a178e89929a1a6c57fe5ffc1fe5d1e11011f6ee4d26f968ba94e83d29e030f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-07T17:54:28Z","title_canon_sha256":"91856d4c09282a067996d28b3c6bfc24b58579178f066f50b222c589ac697281"},"schema_version":"1.0","source":{"id":"2607.06553","kind":"arxiv","version":1}},"canonical_sha256":"8dd8ac90f3f12083d2db6588851e31f81488b640ee3cabd39d78e1227e7c2303","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8dd8ac90f3f12083d2db6588851e31f81488b640ee3cabd39d78e1227e7c2303","first_computed_at":"2026-07-08T01:19:29.756325Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-08T01:19:29.756325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4V+ke6meurVtEj3lF9gOEXya5u1OGm5yZhpkaz2exCJyL9Y0y56wennMxM2+nPVhBPWQ7078E/MNfUKX64GVBQ==","signature_status":"signed_v1","signed_at":"2026-07-08T01:19:29.756766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.06553","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:23b7fbc2632ede629bc3b38eea9e768706080f016edc4ddc7a159a92a5879f86","sha256:f6674654a67bfd25a20b3facc68999cbb5dde2dcf32f73176ffbe702e79e034a"],"state_sha256":"8d8370e551e1417f608a6bb66c4986712c88a04d1118cee97bb2da4c0c6e5dc9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9l01/zENIPgRHCv4jgvs9jT+j+vqfM7NV8sqimXiIZhDzoiN92kLrxPMfXllLC6hXYtmLtEjrKFvTFEjCP+ZAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T10:16:10.900625Z","bundle_sha256":"712d14f3e87ccdb3e61f47647aa264ad49865c67c026887a3d086963df43de22"}}