{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WQLLX4OMUVD4PAVZ72W5IY65L3","short_pith_number":"pith:WQLLX4OM","canonical_record":{"source":{"id":"2311.16502","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T17:33:21Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"c676d155268c4b0c7a75a3b5e40ee86f50174544ced223da0e78878e44a7ea68","abstract_canon_sha256":"de0ecfa23bacf26dab6973c29b09c6078f8e05cd01f66e073e06de1205925749"},"schema_version":"1.0"},"canonical_sha256":"b416bbf1cca547c782b9feadd463dd5ee863fd5f73a381dd348d67f0b449ab90","source":{"kind":"arxiv","id":"2311.16502","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16502","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16502v4","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16502","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"WQLLX4OMUVD4","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"WQLLX4OMUVD4PAVZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"WQLLX4OM","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WQLLX4OMUVD4PAVZ72W5IY65L3","target":"record","payload":{"canonical_record":{"source":{"id":"2311.16502","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T17:33:21Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"c676d155268c4b0c7a75a3b5e40ee86f50174544ced223da0e78878e44a7ea68","abstract_canon_sha256":"de0ecfa23bacf26dab6973c29b09c6078f8e05cd01f66e073e06de1205925749"},"schema_version":"1.0"},"canonical_sha256":"b416bbf1cca547c782b9feadd463dd5ee863fd5f73a381dd348d67f0b449ab90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:53.375690Z","signature_b64":"9RGGfUz1OyYMsQwkdLOCdbgvGZQEpnFDf4p1t8ndwZ+5SeOMnrXJ0H1sLt/Ww7BNhB2a2ovgNoUJs6lwADA8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b416bbf1cca547c782b9feadd463dd5ee863fd5f73a381dd348d67f0b449ab90","last_reissued_at":"2026-05-17T23:38:53.375011Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:53.375011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.16502","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9n46P/5wr9JQagEKEWb/6lowcYIiBpF1WCC97nZgCdVeAWM8npX3dZ6sF4BXJEOL+vwrKdKnoQZYCNlj/GNtCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T23:16:47.122152Z"},"content_sha256":"1eb10f83ee152b19f1a5f9e290d2f3917e333c3d74f2e51813c55d5d9f2f02f4","schema_version":"1.0","event_id":"sha256:1eb10f83ee152b19f1a5f9e290d2f3917e333c3d74f2e51813c55d5d9f2f02f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WQLLX4OMUVD4PAVZ72W5IY65L3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions.","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CL","authors_text":"Botao Yu, Boyuan Zheng, Cong Wei, Dongfu Jiang, Ge Zhang, Huan Sun, Kai Zhang, Ming Yin, Renliang Sun, Ruibin Yuan, Ruoqi Liu, Samuel Stevens, Tianyu Zheng, Weiming Ren, Wenhao Huang, Wenhu Chen, Xiang Yue, Yibo Liu, Yuansheng Ni, Yu Su, Yuxuan Sun, Zhenzhu Yang","submitted_at":"2023-11-27T17:33:21Z","abstract_excerpt":"We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. U"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The collected questions and images accurately represent the perception and reasoning demands of college-level expertise across the six disciplines.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MMMU provides 11.5K heterogeneous college-level multimodal questions that current models solve at 56-59% accuracy, establishing a new standard for expert multimodal evaluation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"db82426af49414413a3d226e5a137afc0db3f808f6d3fbc011059136fbc29bde"},"source":{"id":"2311.16502","kind":"arxiv","version":4},"verdict":{"id":"58753d9a-79c9-4414-b8ba-70dd7202ea1f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:32:50.610184Z","strongest_claim":"Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement.","one_line_summary":"MMMU provides 11.5K heterogeneous college-level multimodal questions that current models solve at 56-59% accuracy, establishing a new standard for expert multimodal evaluation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The collected questions and images accurately represent the perception and reasoning demands of college-level expertise across the six disciplines.","pith_extraction_headline":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions."},"references":{"count":97,"sample":[{"doi":"","year":2023,"title":"Artificial general intelligence is already here","work_id":"ee91164c-c115-42cf-b76e-7bfc03f754de","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Flamingo: a visual language model for few-shot learning","work_id":"482765e0-05ff-4ec7-b839-d279de7d5d65","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Lawrence Zitnick, and Devi Parikh","work_id":"4ed9a186-58bd-448e-8d3e-f388ddffa45d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models","work_id":"87bfa84a-e663-4165-806f-93ef439d88d0","ref_index":4,"cited_arxiv_id":"2308.01390","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":97,"snapshot_sha256":"69e920abf0bf85b9da808524ccac4492e07a91e2b06c5897ee526bbd97ace56b","internal_anchors":34},"formal_canon":{"evidence_count":3,"snapshot_sha256":"216551743014b356989930d42f52f907b6419d05685b64948fdbc37d5218c014"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"58753d9a-79c9-4414-b8ba-70dd7202ea1f"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CqwX4IGqPFy3NC/VByNLIxKP7inBeQMQyimz4ULr7UqiYJMFsiEPTGJt+NewWzx6b9HgNGBaRoiXyTTWUAm5AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T23:16:47.123023Z"},"content_sha256":"03491e1f1dc4918a4f1677b6499eddf223f2d350f3f6e4ebf4dc888bfb55b8b3","schema_version":"1.0","event_id":"sha256:03491e1f1dc4918a4f1677b6499eddf223f2d350f3f6e4ebf4dc888bfb55b8b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/bundle.json","state_url":"https://pith.science/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/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":"2026-05-28T23:16:47Z","links":{"resolver":"https://pith.science/pith/WQLLX4OMUVD4PAVZ72W5IY65L3","bundle":"https://pith.science/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/bundle.json","state":"https://pith.science/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WQLLX4OMUVD4PAVZ72W5IY65L3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WQLLX4OMUVD4PAVZ72W5IY65L3","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":"de0ecfa23bacf26dab6973c29b09c6078f8e05cd01f66e073e06de1205925749","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T17:33:21Z","title_canon_sha256":"c676d155268c4b0c7a75a3b5e40ee86f50174544ced223da0e78878e44a7ea68"},"schema_version":"1.0","source":{"id":"2311.16502","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16502","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16502v4","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16502","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"WQLLX4OMUVD4","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"WQLLX4OMUVD4PAVZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"WQLLX4OM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:03491e1f1dc4918a4f1677b6499eddf223f2d350f3f6e4ebf4dc888bfb55b8b3","target":"graph","created_at":"2026-05-17T23:38:53Z","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":"Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The collected questions and images accurately represent the perception and reasoning demands of college-level expertise across the six disciplines."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MMMU provides 11.5K heterogeneous college-level multimodal questions that current models solve at 56-59% accuracy, establishing a new standard for expert multimodal evaluation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions."}],"snapshot_sha256":"db82426af49414413a3d226e5a137afc0db3f808f6d3fbc011059136fbc29bde"},"formal_canon":{"evidence_count":3,"snapshot_sha256":"216551743014b356989930d42f52f907b6419d05685b64948fdbc37d5218c014"},"paper":{"abstract_excerpt":"We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. U","authors_text":"Botao Yu, Boyuan Zheng, Cong Wei, Dongfu Jiang, Ge Zhang, Huan Sun, Kai Zhang, Ming Yin, Renliang Sun, Ruibin Yuan, Ruoqi Liu, Samuel Stevens, Tianyu Zheng, Weiming Ren, Wenhao Huang, Wenhu Chen, Xiang Yue, Yibo Liu, Yuansheng Ni, Yu Su, Yuxuan Sun, Zhenzhu Yang","cross_cats":["cs.AI","cs.CV"],"headline":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T17:33:21Z","title":"MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"},"references":{"count":97,"internal_anchors":34,"resolved_work":97,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Artificial general intelligence is already here","work_id":"ee91164c-c115-42cf-b76e-7bfc03f754de","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Flamingo: a visual language model for few-shot learning","work_id":"482765e0-05ff-4ec7-b839-d279de7d5d65","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Lawrence Zitnick, and Devi Parikh","work_id":"4ed9a186-58bd-448e-8d3e-f388ddffa45d","year":2015},{"cited_arxiv_id":"2308.01390","doi":"","is_internal_anchor":true,"ref_index":4,"title":"OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models","work_id":"87bfa84a-e663-4165-806f-93ef439d88d0","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":"69e920abf0bf85b9da808524ccac4492e07a91e2b06c5897ee526bbd97ace56b"},"source":{"id":"2311.16502","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-15T05:32:50.610184Z","id":"58753d9a-79c9-4414-b8ba-70dd7202ea1f","model_set":{"reader":"grok-4.3"},"one_line_summary":"MMMU provides 11.5K heterogeneous college-level multimodal questions that current models solve at 56-59% accuracy, establishing a new standard for expert multimodal evaluation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Multimodal models like GPT-4V and Gemini Ultra reach only 56-59% accuracy on a new benchmark of 11,500 college-level expert questions.","strongest_claim":"Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement.","weakest_assumption":"The collected questions and images accurately represent the perception and reasoning demands of college-level expertise across the six disciplines."}},"verdict_id":"58753d9a-79c9-4414-b8ba-70dd7202ea1f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1eb10f83ee152b19f1a5f9e290d2f3917e333c3d74f2e51813c55d5d9f2f02f4","target":"record","created_at":"2026-05-17T23:38:53Z","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":"de0ecfa23bacf26dab6973c29b09c6078f8e05cd01f66e073e06de1205925749","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T17:33:21Z","title_canon_sha256":"c676d155268c4b0c7a75a3b5e40ee86f50174544ced223da0e78878e44a7ea68"},"schema_version":"1.0","source":{"id":"2311.16502","kind":"arxiv","version":4}},"canonical_sha256":"b416bbf1cca547c782b9feadd463dd5ee863fd5f73a381dd348d67f0b449ab90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b416bbf1cca547c782b9feadd463dd5ee863fd5f73a381dd348d67f0b449ab90","first_computed_at":"2026-05-17T23:38:53.375011Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:53.375011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9RGGfUz1OyYMsQwkdLOCdbgvGZQEpnFDf4p1t8ndwZ+5SeOMnrXJ0H1sLt/Ww7BNhB2a2ovgNoUJs6lwADA8DQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:53.375690Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.16502","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1eb10f83ee152b19f1a5f9e290d2f3917e333c3d74f2e51813c55d5d9f2f02f4","sha256:03491e1f1dc4918a4f1677b6499eddf223f2d350f3f6e4ebf4dc888bfb55b8b3"],"state_sha256":"d1f76876164f0697853062e438853537d2e2c581817f6fc9e9924a86b4696ea9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7t/hKGZsN319Q8frH9fzwJa6qNiblTCAOLYt790wR8eY9vu2Mz03HDWq8QzoRLtFIplH8fvzeeRJlKT3A6CIAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T23:16:47.126700Z","bundle_sha256":"e53d0602997daf05df703a28f47cfc8584d15b7f207fc51af3bc4db3dde72cc4"}}