{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PSJTDXBL62BRHFFYP3JULGFOKI","short_pith_number":"pith:PSJTDXBL","canonical_record":{"source":{"id":"2506.16504","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T17:57:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1d0ad5db2cdf2c5f15ebf3e86bb570edd12dc79496c497c0b7d45ae92d0cc16","abstract_canon_sha256":"a036961bbc5a261a6d63f66754636d81fd0471fc7629908fce79db501a100c60"},"schema_version":"1.0"},"canonical_sha256":"7c9331dc2bf6831394b87ed34598ae521092ba2577eaba6848d6bea5bf04fba7","source":{"kind":"arxiv","id":"2506.16504","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16504","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16504v1","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16504","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"PSJTDXBL62BR","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PSJTDXBL62BRHFFY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PSJTDXBL","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PSJTDXBL62BRHFFYP3JULGFOKI","target":"record","payload":{"canonical_record":{"source":{"id":"2506.16504","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T17:57:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1d0ad5db2cdf2c5f15ebf3e86bb570edd12dc79496c497c0b7d45ae92d0cc16","abstract_canon_sha256":"a036961bbc5a261a6d63f66754636d81fd0471fc7629908fce79db501a100c60"},"schema_version":"1.0"},"canonical_sha256":"7c9331dc2bf6831394b87ed34598ae521092ba2577eaba6848d6bea5bf04fba7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:47.650469Z","signature_b64":"VPd0nRtdpftg5Oe5yh4W5Bn4rHncH4O5k/QpVGv4CaypOacczQNHsp9TQg7ym501lkJEYftVPJgZiZdSP8LUDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c9331dc2bf6831394b87ed34598ae521092ba2577eaba6848d6bea5bf04fba7","last_reissued_at":"2026-05-17T23:38:47.649934Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:47.649934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.16504","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":"2026-05-17T23:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DP1v8dPz3ahp2kdBpC/r09UwfWmj+r/+HDdRB7b+JTVCJuC8j/9r0VHxmMmHHT7VNo8adDMB+9A1v8wvEDOmAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T12:08:43.552411Z"},"content_sha256":"d3b9dd54f303e5d667603be5f8b87eea04c96e0c312182d900dfc0fc570be03e","schema_version":"1.0","event_id":"sha256:d3b9dd54f303e5d667603be5f8b87eea04c96e0c312182d900dfc0fc570be03e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PSJTDXBL62BRHFFYP3JULGFOKI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images.","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chunchao Guo, Di Luo, Fang Yang, Fan Yang, Haolin Liu, Huiwen Shi, Jie Jiang, Jingwei Huang, Lifu Wang, Linus, Mingxin Yang, Qingxiang Lin, Sheng Zhang, Shuhui Yang, Sicong Liu, Tian Liu, Xianghui Yang, Xin Huang, Yifei Feng, Yixuan Tang, Yuhong Liu, Yulin Cai, Yunfei Zhao, Zebin He, Zeqiang Lai, Zibo Zhao","submitted_at":"2025-06-19T17:57:40Z","abstract_excerpt":"In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That scaling model size, datasets, and compute directly produces the claimed improvements in shape fidelity and texture quality without overfitting or evaluation biases that favor the new system.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Hunyuan3D 2.5's LATTICE model with 10B parameters generates detailed 3D shapes from images and uses multi-view PBR for textures, outperforming prior methods in fidelity and mesh quality.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"11e8677feff247c3a3d507ec673db2ac8a67d308b2a32d123f93dc961ab01ef9"},"source":{"id":"2506.16504","kind":"arxiv","version":1},"verdict":{"id":"b3d6e355-478f-46a1-a50c-bfc45c147d74","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T14:05:48.164751Z","strongest_claim":"Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes.","one_line_summary":"Hunyuan3D 2.5's LATTICE model with 10B parameters generates detailed 3D shapes from images and uses multi-view PBR for textures, outperforming prior methods in fidelity and mesh quality.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That scaling model size, datasets, and compute directly produces the claimed improvements in shape fidelity and texture quality without overfitting or evaluation biases that favor the new system.","pith_extraction_headline":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images."},"references":{"count":24,"sample":[{"doi":"","year":null,"title":"Matatlas: Text-driven consistent geometry texturing and material assignment","work_id":"65bf8f44-1808-4d4c-9ab9-4c3ca62be762","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Make- it-real: Unleashing large multimodal model for painting 3d objects with realistic materials","work_id":"0b0024e9-70a4-4709-92bc-b4475970f013","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Chenjian Gao, Boyan Jiang, Xinghui Li, Yingpeng Zhang, and Qian Yu","work_id":"4877e3d1-9338-431b-a493-783315777cb8","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Romero, Tsung-Yi Lin, and Ming-Yu Liu","work_id":"63b64d9f-62b0-4618-ab46-24daf2cdd6af","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"LRM: Large Reconstruction Model for Single Image to 3D","work_id":"0662dc2c-cc1c-4358-99bd-2a5f34795738","ref_index":5,"cited_arxiv_id":"2311.04400","is_internal_anchor":true}],"resolved_work":24,"snapshot_sha256":"3f7ea1cfc722d9f0393f1aa554d7128b7041b23079338221f29bb98651a85806","internal_anchors":5},"formal_canon":{"evidence_count":1,"snapshot_sha256":"f2d882bde26ce0f9e875f20832dcfd5fb0fc27ab94802edbd56f418f2c19ce53"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"b3d6e355-478f-46a1-a50c-bfc45c147d74"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bZxXmD9u4cTPyk8CTZONSVuX67VF6x8H0Nb9DGClnqZPzf3BMxslN9TFq+Ry+9CW3ohWvI0405aDxTEJmdNYCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T12:08:43.553503Z"},"content_sha256":"c466970d7745ae7a15d672e88ecbd1995eb6c384ec7aa1b55910b37ca67f6bfd","schema_version":"1.0","event_id":"sha256:c466970d7745ae7a15d672e88ecbd1995eb6c384ec7aa1b55910b37ca67f6bfd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PSJTDXBL62BRHFFYP3JULGFOKI/bundle.json","state_url":"https://pith.science/pith/PSJTDXBL62BRHFFYP3JULGFOKI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PSJTDXBL62BRHFFYP3JULGFOKI/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":"2026-06-10T12:08:43Z","links":{"resolver":"https://pith.science/pith/PSJTDXBL62BRHFFYP3JULGFOKI","bundle":"https://pith.science/pith/PSJTDXBL62BRHFFYP3JULGFOKI/bundle.json","state":"https://pith.science/pith/PSJTDXBL62BRHFFYP3JULGFOKI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PSJTDXBL62BRHFFYP3JULGFOKI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PSJTDXBL62BRHFFYP3JULGFOKI","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":"a036961bbc5a261a6d63f66754636d81fd0471fc7629908fce79db501a100c60","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T17:57:40Z","title_canon_sha256":"a1d0ad5db2cdf2c5f15ebf3e86bb570edd12dc79496c497c0b7d45ae92d0cc16"},"schema_version":"1.0","source":{"id":"2506.16504","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16504","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16504v1","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16504","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"PSJTDXBL62BR","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PSJTDXBL62BRHFFY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PSJTDXBL","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:c466970d7745ae7a15d672e88ecbd1995eb6c384ec7aa1b55910b37ca67f6bfd","target":"graph","created_at":"2026-05-17T23:38:47Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That scaling model size, datasets, and compute directly produces the claimed improvements in shape fidelity and texture quality without overfitting or evaluation biases that favor the new system."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Hunyuan3D 2.5's LATTICE model with 10B parameters generates detailed 3D shapes from images and uses multi-view PBR for textures, outperforming prior methods in fidelity and mesh quality."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images."}],"snapshot_sha256":"11e8677feff247c3a3d507ec673db2ac8a67d308b2a32d123f93dc961ab01ef9"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"f2d882bde26ce0f9e875f20832dcfd5fb0fc27ab94802edbd56f418f2c19ce53"},"paper":{"abstract_excerpt":"In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping","authors_text":"Chunchao Guo, Di Luo, Fang Yang, Fan Yang, Haolin Liu, Huiwen Shi, Jie Jiang, Jingwei Huang, Lifu Wang, Linus, Mingxin Yang, Qingxiang Lin, Sheng Zhang, Shuhui Yang, Sicong Liu, Tian Liu, Xianghui Yang, Xin Huang, Yifei Feng, Yixuan Tang, Yuhong Liu, Yulin Cai, Yunfei Zhao, Zebin He, Zeqiang Lai, Zibo Zhao","cross_cats":["cs.AI"],"headline":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T17:57:40Z","title":"Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details"},"references":{"count":24,"internal_anchors":5,"resolved_work":24,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Matatlas: Text-driven consistent geometry texturing and material assignment","work_id":"65bf8f44-1808-4d4c-9ab9-4c3ca62be762","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Make- it-real: Unleashing large multimodal model for painting 3d objects with realistic materials","work_id":"0b0024e9-70a4-4709-92bc-b4475970f013","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Chenjian Gao, Boyan Jiang, Xinghui Li, Yingpeng Zhang, and Qian Yu","work_id":"4877e3d1-9338-431b-a493-783315777cb8","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Romero, Tsung-Yi Lin, and Ming-Yu Liu","work_id":"63b64d9f-62b0-4618-ab46-24daf2cdd6af","year":null},{"cited_arxiv_id":"2311.04400","doi":"","is_internal_anchor":true,"ref_index":5,"title":"LRM: Large Reconstruction Model for Single Image to 3D","work_id":"0662dc2c-cc1c-4358-99bd-2a5f34795738","year":null}],"snapshot_sha256":"3f7ea1cfc722d9f0393f1aa554d7128b7041b23079338221f29bb98651a85806"},"source":{"id":"2506.16504","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-16T14:05:48.164751Z","id":"b3d6e355-478f-46a1-a50c-bfc45c147d74","model_set":{"reader":"grok-4.3"},"one_line_summary":"Hunyuan3D 2.5's LATTICE model with 10B parameters generates detailed 3D shapes from images and uses multi-view PBR for textures, outperforming prior methods in fidelity and mesh quality.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Scaling a shape foundation model to 10 billion parameters yields sharp, detailed 3D meshes and PBR textures that closely match input images.","strongest_claim":"Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes.","weakest_assumption":"That scaling model size, datasets, and compute directly produces the claimed improvements in shape fidelity and texture quality without overfitting or evaluation biases that favor the new system."}},"verdict_id":"b3d6e355-478f-46a1-a50c-bfc45c147d74"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d3b9dd54f303e5d667603be5f8b87eea04c96e0c312182d900dfc0fc570be03e","target":"record","created_at":"2026-05-17T23:38:47Z","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":"a036961bbc5a261a6d63f66754636d81fd0471fc7629908fce79db501a100c60","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T17:57:40Z","title_canon_sha256":"a1d0ad5db2cdf2c5f15ebf3e86bb570edd12dc79496c497c0b7d45ae92d0cc16"},"schema_version":"1.0","source":{"id":"2506.16504","kind":"arxiv","version":1}},"canonical_sha256":"7c9331dc2bf6831394b87ed34598ae521092ba2577eaba6848d6bea5bf04fba7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c9331dc2bf6831394b87ed34598ae521092ba2577eaba6848d6bea5bf04fba7","first_computed_at":"2026-05-17T23:38:47.649934Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:47.649934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VPd0nRtdpftg5Oe5yh4W5Bn4rHncH4O5k/QpVGv4CaypOacczQNHsp9TQg7ym501lkJEYftVPJgZiZdSP8LUDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:47.650469Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.16504","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3b9dd54f303e5d667603be5f8b87eea04c96e0c312182d900dfc0fc570be03e","sha256:c466970d7745ae7a15d672e88ecbd1995eb6c384ec7aa1b55910b37ca67f6bfd"],"state_sha256":"8cc3f92a7a73c38621e0c74217b937e4134bc81b653d6eb5a16c0f8ee4b7c6b4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NfcOthY8s6+NvOopYueuu9wc0IBzZTABPNXE15zgcdQtJH30MeM1iQeg/wdSF6UkOVDeo3gxkprzeQJkca0zDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T12:08:43.558816Z","bundle_sha256":"76f31a871e353fd775f0e824bb5234975b15e1fa362016390d1f48c8c8779e1a"}}