{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MPNCYOT66WIXD2DDA2RXIFRRUO","short_pith_number":"pith:MPNCYOT6","canonical_record":{"source":{"id":"2607.04884","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T10:06:05Z","cross_cats_sorted":[],"title_canon_sha256":"e139edee96d0643eec3aee32a730649906ccbc69978f6509523a9c62a8b2e2c4","abstract_canon_sha256":"7fb0fafc98ebe76b328bd956a894b4b07c868d6dd7cd590a34f33d0810b7c901"},"schema_version":"1.0"},"canonical_sha256":"63da2c3a7ef59171e86306a3741631a3a5dfe14dd0a052405a2ef073d65f32e8","source":{"kind":"arxiv","id":"2607.04884","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.04884","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"arxiv_version","alias_value":"2607.04884v1","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04884","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_12","alias_value":"MPNCYOT66WIX","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_16","alias_value":"MPNCYOT66WIXD2DD","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_8","alias_value":"MPNCYOT6","created_at":"2026-07-07T02:20:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MPNCYOT66WIXD2DDA2RXIFRRUO","target":"record","payload":{"canonical_record":{"source":{"id":"2607.04884","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T10:06:05Z","cross_cats_sorted":[],"title_canon_sha256":"e139edee96d0643eec3aee32a730649906ccbc69978f6509523a9c62a8b2e2c4","abstract_canon_sha256":"7fb0fafc98ebe76b328bd956a894b4b07c868d6dd7cd590a34f33d0810b7c901"},"schema_version":"1.0"},"canonical_sha256":"63da2c3a7ef59171e86306a3741631a3a5dfe14dd0a052405a2ef073d65f32e8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:20:09.411574Z","signature_b64":"OnDJQMxxCoiHIy6i6TfHVBOE67q+g0LcpRN8M9yw6bVNxWY5eLxqWoZtKN82biWw8vJL7qoNLHuWLl7DriKKAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63da2c3a7ef59171e86306a3741631a3a5dfe14dd0a052405a2ef073d65f32e8","last_reissued_at":"2026-07-07T02:20:09.410842Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:20:09.410842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.04884","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":"2026-07-07T02:20:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tdaRVDrvD7hthdt1DvE6AvhOVR+R26Lr7Tbj7xaxAr05TNLcNfolRhWeQYCGlBba7BKDZbT7iivTRVEpKMa8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T04:48:20.038536Z"},"content_sha256":"6cce73826d280813e20c75f80bdebefaad33eb462bf1a7eaa444f9d825c657d8","schema_version":"1.0","event_id":"sha256:6cce73826d280813e20c75f80bdebefaad33eb462bf1a7eaa444f9d825c657d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MPNCYOT66WIXD2DDA2RXIFRRUO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binghong Wu, Bochao Wang, Can Ma, Chengquan Zhang, Gengluo Li, Guanghua Yu, Han Hu, Hao Feng, Hongbing Wen, Hong Liu, Huawen Shen, Jieneng Yang, Liang Wu, Pengyuan Lyu, Shangpin Peng, Shijing Hu, Weinong Wang, Xingyu Wan, Yongkun Du, Yu Zhou, Zheng Ruan, Zhiqiong Lu, Zibin Lin","submitted_at":"2026-07-06T10:06:05Z","abstract_excerpt":"We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preser"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04884","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/2607.04884/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":"2026-07-07T02:20:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cASvxYbGY2YBmDLZ+3ZKIujPAHKvjq8u4PKJ6EkNRIsvY4Wbkm4g5/sLQTR1cR8DTptqKqE2jk4RzaVAINedBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T04:48:20.038914Z"},"content_sha256":"c870da545fe20e13f638951c7d0dd2eeab4f04ffd3bdff6655e84d6ba3165afd","schema_version":"1.0","event_id":"sha256:c870da545fe20e13f638951c7d0dd2eeab4f04ffd3bdff6655e84d6ba3165afd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/bundle.json","state_url":"https://pith.science/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/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":"2026-07-25T04:48:20Z","links":{"resolver":"https://pith.science/pith/MPNCYOT66WIXD2DDA2RXIFRRUO","bundle":"https://pith.science/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/bundle.json","state":"https://pith.science/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MPNCYOT66WIXD2DDA2RXIFRRUO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MPNCYOT66WIXD2DDA2RXIFRRUO","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":"7fb0fafc98ebe76b328bd956a894b4b07c868d6dd7cd590a34f33d0810b7c901","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T10:06:05Z","title_canon_sha256":"e139edee96d0643eec3aee32a730649906ccbc69978f6509523a9c62a8b2e2c4"},"schema_version":"1.0","source":{"id":"2607.04884","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.04884","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"arxiv_version","alias_value":"2607.04884v1","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04884","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_12","alias_value":"MPNCYOT66WIX","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_16","alias_value":"MPNCYOT66WIXD2DD","created_at":"2026-07-07T02:20:09Z"},{"alias_kind":"pith_short_8","alias_value":"MPNCYOT6","created_at":"2026-07-07T02:20:09Z"}],"graph_snapshots":[{"event_id":"sha256:c870da545fe20e13f638951c7d0dd2eeab4f04ffd3bdff6655e84d6ba3165afd","target":"graph","created_at":"2026-07-07T02:20:09Z","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/2607.04884/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preser","authors_text":"Binghong Wu, Bochao Wang, Can Ma, Chengquan Zhang, Gengluo Li, Guanghua Yu, Han Hu, Hao Feng, Hongbing Wen, Hong Liu, Huawen Shen, Jieneng Yang, Liang Wu, Pengyuan Lyu, Shangpin Peng, Shijing Hu, Weinong Wang, Xingyu Wan, Yongkun Du, Yu Zhou, Zheng Ruan, Zhiqiong Lu, Zibin Lin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T10:06:05Z","title":"HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04884","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":"sha256:6cce73826d280813e20c75f80bdebefaad33eb462bf1a7eaa444f9d825c657d8","target":"record","created_at":"2026-07-07T02:20:09Z","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":"7fb0fafc98ebe76b328bd956a894b4b07c868d6dd7cd590a34f33d0810b7c901","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T10:06:05Z","title_canon_sha256":"e139edee96d0643eec3aee32a730649906ccbc69978f6509523a9c62a8b2e2c4"},"schema_version":"1.0","source":{"id":"2607.04884","kind":"arxiv","version":1}},"canonical_sha256":"63da2c3a7ef59171e86306a3741631a3a5dfe14dd0a052405a2ef073d65f32e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63da2c3a7ef59171e86306a3741631a3a5dfe14dd0a052405a2ef073d65f32e8","first_computed_at":"2026-07-07T02:20:09.410842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T02:20:09.410842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OnDJQMxxCoiHIy6i6TfHVBOE67q+g0LcpRN8M9yw6bVNxWY5eLxqWoZtKN82biWw8vJL7qoNLHuWLl7DriKKAA==","signature_status":"signed_v1","signed_at":"2026-07-07T02:20:09.411574Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.04884","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6cce73826d280813e20c75f80bdebefaad33eb462bf1a7eaa444f9d825c657d8","sha256:c870da545fe20e13f638951c7d0dd2eeab4f04ffd3bdff6655e84d6ba3165afd"],"state_sha256":"a4cbfb0299770f113422afa10568a062679004d402ffd07caffc210532bb8930"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"biOvXWUAPFV5h9s/Rr9d6XfiWjsF0Qr4O2bxZS7JV4WlvjlwFiTaMBQWmvohMONgOFtaC2pNpSZCJ+Fdf4YxCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T04:48:20.041141Z","bundle_sha256":"6d27e9262f5c82e01bcb65eda9729175a8da469074d195a424d9456e128b258e"}}