{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:JFHY3ZBGIOIOKRWDPH7J3OZJQX","short_pith_number":"pith:JFHY3ZBG","canonical_record":{"source":{"id":"2312.16886","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-28T08:21:24Z","cross_cats_sorted":[],"title_canon_sha256":"8ae89b1d0264365c810ef9738b215df1238931e0636e31f10912255e38dfd833","abstract_canon_sha256":"e68f7b6321bbf4d6b6ef76e8c563800fceecbdd6394c343e11af2aa461d8d6b7"},"schema_version":"1.0"},"canonical_sha256":"494f8de4264390e546c379fe9dbb2985cd457a040f21d5d52b70996854c08fd9","source":{"kind":"arxiv","id":"2312.16886","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.16886","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2312.16886v2","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16886","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"JFHY3ZBGIOIO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"JFHY3ZBGIOIOKRWD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"JFHY3ZBG","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:JFHY3ZBGIOIOKRWDPH7J3OZJQX","target":"record","payload":{"canonical_record":{"source":{"id":"2312.16886","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-28T08:21:24Z","cross_cats_sorted":[],"title_canon_sha256":"8ae89b1d0264365c810ef9738b215df1238931e0636e31f10912255e38dfd833","abstract_canon_sha256":"e68f7b6321bbf4d6b6ef76e8c563800fceecbdd6394c343e11af2aa461d8d6b7"},"schema_version":"1.0"},"canonical_sha256":"494f8de4264390e546c379fe9dbb2985cd457a040f21d5d52b70996854c08fd9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:47.293895Z","signature_b64":"SHxGbshw0iQmbCO6pluwz+kUBbckBg2RyLZ256eoouLAn5bdxtF/aqzyr4/0yl9ot7X93tK8OKxbi+wZweITCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"494f8de4264390e546c379fe9dbb2985cd457a040f21d5d52b70996854c08fd9","last_reissued_at":"2026-05-17T23:38:47.293101Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:47.293101Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.16886","source_version":2,"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":"ywUN/WnmxuSTDJ85DVcuvT5eTUu81pMG5P5M5v9oKBgSOHeRpB/YgOepSS4HKet/n78LI+L2FsOgBhqPdCCXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T02:49:52.230222Z"},"content_sha256":"53dc285614a8a60393d9aebe109a4bf51d6f1d9d11da8a51f5e224496c7f5aa1","schema_version":"1.0","event_id":"sha256:53dc285614a8a60393d9aebe109a4bf51d6f1d9d11da8a51f5e224496c7f5aa1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:JFHY3ZBGIOIOKRWDPH7J3OZJQX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Zhang, Chunhua Shen, Fei Wei, Limeng Qiao, Shuang Xu, Xiangxiang Chu, Xiaolin Wei, Xinyang Lin, Xinyu Zhang, Yang Yang, Yiming Hu","submitted_at":"2023-12-28T08:21:24Z","abstract_excerpt":"We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we mea"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our models demonstrate on par performance compared with a few much larger models. ... we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the specific combination of scratch-trained small LMs, CLIP vision pretraining, and efficient projector will generalize to competitive benchmark scores without the full training details, data composition, or exact comparison baselines being verifiable from the abstract alone.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MobileVLM achieves on-par performance with much larger vision-language models on standard benchmarks while delivering state-of-the-art inference speeds of 21.5 tokens per second on Snapdragon 888 CPU and 65.3 on Jetson Orin GPU.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"783e84a0e244ccd12de1f120e169082cf6345d199ce958cf87b6cbb1ff2035b1"},"source":{"id":"2312.16886","kind":"arxiv","version":2},"verdict":{"id":"acb3a2e8-a582-4393-aed8-42ad48945efc","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T16:29:49.950995Z","strongest_claim":"Our models demonstrate on par performance compared with a few much larger models. ... we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively.","one_line_summary":"MobileVLM achieves on-par performance with much larger vision-language models on standard benchmarks while delivering state-of-the-art inference speeds of 21.5 tokens per second on Snapdragon 888 CPU and 65.3 on Jetson Orin GPU.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the specific combination of scratch-trained small LMs, CLIP vision pretraining, and efficient projector will generalize to competitive benchmark scores without the full training details, data composition, or exact comparison baselines being verifiable from the abstract alone.","pith_extraction_headline":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs."},"references":{"count":133,"sample":[{"doi":"","year":2023,"title":"An In-depth Look at Gemini’s Language Abilities","work_id":"0a08125e-c6a1-40ab-99aa-8357f934a3d4","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Flamingo: a visual language model for few-shot learn- ing","work_id":"3eb5d477-e371-477c-9314-420a3b529fbf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Anas Awadalla, Irena Gao, Joshua Gardner, Jack Hes- sel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Jenia Jitsev, Simon Korn- blith, Pang Wei Koh, Gabriel Ilharco, Mitche","work_id":"8da6e5be-e048-46de-b0fb-94f732778270","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Qwen Technical Report","work_id":"bb1fd52f-6b2f-437c-9516-37bdf6eb9be8","ref_index":4,"cited_arxiv_id":"2309.16609","is_internal_anchor":true},{"doi":"","year":2023,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","ref_index":5,"cited_arxiv_id":"2308.12966","is_internal_anchor":true}],"resolved_work":133,"snapshot_sha256":"b701461e562fc0d5e453f9094bec0f0d1229f2833026f20f73e61aeea27da1ca","internal_anchors":42},"formal_canon":{"evidence_count":1,"snapshot_sha256":"ff1c136a28d1cc09693a950eac8555a993a91ac73e8b5d0799ba56c1a71ea2a5"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"acb3a2e8-a582-4393-aed8-42ad48945efc"},"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":"OD7ANX2EEcj6fIr0MUdaUH8R2GlzIdbIdI2ybnKTetdIm1mAW6nVOBxq9l3JGH0oAH2ylQGIthSqeG/V54UuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T02:49:52.231400Z"},"content_sha256":"f050b0edea3ad940c078ad4c29335e3a72e984e188861c0572aed6193117fc5e","schema_version":"1.0","event_id":"sha256:f050b0edea3ad940c078ad4c29335e3a72e984e188861c0572aed6193117fc5e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/bundle.json","state_url":"https://pith.science/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/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":"2026-06-05T02:49:52Z","links":{"resolver":"https://pith.science/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX","bundle":"https://pith.science/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/bundle.json","state":"https://pith.science/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JFHY3ZBGIOIOKRWDPH7J3OZJQX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:JFHY3ZBGIOIOKRWDPH7J3OZJQX","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":"e68f7b6321bbf4d6b6ef76e8c563800fceecbdd6394c343e11af2aa461d8d6b7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-28T08:21:24Z","title_canon_sha256":"8ae89b1d0264365c810ef9738b215df1238931e0636e31f10912255e38dfd833"},"schema_version":"1.0","source":{"id":"2312.16886","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.16886","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2312.16886v2","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16886","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"JFHY3ZBGIOIO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"JFHY3ZBGIOIOKRWD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"JFHY3ZBG","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f050b0edea3ad940c078ad4c29335e3a72e984e188861c0572aed6193117fc5e","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 models demonstrate on par performance compared with a few much larger models. ... we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the specific combination of scratch-trained small LMs, CLIP vision pretraining, and efficient projector will generalize to competitive benchmark scores without the full training details, data composition, or exact comparison baselines being verifiable from the abstract alone."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MobileVLM achieves on-par performance with much larger vision-language models on standard benchmarks while delivering state-of-the-art inference speeds of 21.5 tokens per second on Snapdragon 888 CPU and 65.3 on Jetson Orin GPU."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs."}],"snapshot_sha256":"783e84a0e244ccd12de1f120e169082cf6345d199ce958cf87b6cbb1ff2035b1"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"ff1c136a28d1cc09693a950eac8555a993a91ac73e8b5d0799ba56c1a71ea2a5"},"paper":{"abstract_excerpt":"We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we mea","authors_text":"Bo Zhang, Chunhua Shen, Fei Wei, Limeng Qiao, Shuang Xu, Xiangxiang Chu, Xiaolin Wei, Xinyang Lin, Xinyu Zhang, Yang Yang, Yiming Hu","cross_cats":[],"headline":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-28T08:21:24Z","title":"MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices"},"references":{"count":133,"internal_anchors":42,"resolved_work":133,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"An In-depth Look at Gemini’s Language Abilities","work_id":"0a08125e-c6a1-40ab-99aa-8357f934a3d4","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Flamingo: a visual language model for few-shot learn- ing","work_id":"3eb5d477-e371-477c-9314-420a3b529fbf","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Anas Awadalla, Irena Gao, Joshua Gardner, Jack Hes- sel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Jenia Jitsev, Simon Korn- blith, Pang Wei Koh, Gabriel Ilharco, Mitche","work_id":"8da6e5be-e048-46de-b0fb-94f732778270","year":2023},{"cited_arxiv_id":"2309.16609","doi":"","is_internal_anchor":true,"ref_index":4,"title":"Qwen Technical Report","work_id":"bb1fd52f-6b2f-437c-9516-37bdf6eb9be8","year":2023},{"cited_arxiv_id":"2308.12966","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","year":2023}],"snapshot_sha256":"b701461e562fc0d5e453f9094bec0f0d1229f2833026f20f73e61aeea27da1ca"},"source":{"id":"2312.16886","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T16:29:49.950995Z","id":"acb3a2e8-a582-4393-aed8-42ad48945efc","model_set":{"reader":"grok-4.3"},"one_line_summary":"MobileVLM achieves on-par performance with much larger vision-language models on standard benchmarks while delivering state-of-the-art inference speeds of 21.5 tokens per second on Snapdragon 888 CPU and 65.3 on Jetson Orin GPU.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A vision-language model with 1.4B and 2.7B parameters matches larger models while running at 65 tokens per second on mobile GPUs.","strongest_claim":"Our models demonstrate on par performance compared with a few much larger models. ... we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively.","weakest_assumption":"That the specific combination of scratch-trained small LMs, CLIP vision pretraining, and efficient projector will generalize to competitive benchmark scores without the full training details, data composition, or exact comparison baselines being verifiable from the abstract alone."}},"verdict_id":"acb3a2e8-a582-4393-aed8-42ad48945efc"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53dc285614a8a60393d9aebe109a4bf51d6f1d9d11da8a51f5e224496c7f5aa1","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":"e68f7b6321bbf4d6b6ef76e8c563800fceecbdd6394c343e11af2aa461d8d6b7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-28T08:21:24Z","title_canon_sha256":"8ae89b1d0264365c810ef9738b215df1238931e0636e31f10912255e38dfd833"},"schema_version":"1.0","source":{"id":"2312.16886","kind":"arxiv","version":2}},"canonical_sha256":"494f8de4264390e546c379fe9dbb2985cd457a040f21d5d52b70996854c08fd9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"494f8de4264390e546c379fe9dbb2985cd457a040f21d5d52b70996854c08fd9","first_computed_at":"2026-05-17T23:38:47.293101Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:47.293101Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SHxGbshw0iQmbCO6pluwz+kUBbckBg2RyLZ256eoouLAn5bdxtF/aqzyr4/0yl9ot7X93tK8OKxbi+wZweITCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:47.293895Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.16886","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53dc285614a8a60393d9aebe109a4bf51d6f1d9d11da8a51f5e224496c7f5aa1","sha256:f050b0edea3ad940c078ad4c29335e3a72e984e188861c0572aed6193117fc5e"],"state_sha256":"06be9c33d2966e3b7cc118f54b71010be302f0c6a92ca867f838e69e259f137c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jMQEi5vJ1Scx4tDselxDkZtWcAiNo0tUrgQq9BiIpHGJ8qtwM3Mj3wTOXeO7PsK4mJfT1xit5Nzl4EzCijptBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T02:49:52.235938Z","bundle_sha256":"f4da4a58afc363b0e9475c795773270ef3683f5310f4b38c9b7adb85d2cc6a78"}}