{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BSGFVEPILCAVIZPNVOWYTZKP7T","short_pith_number":"pith:BSGFVEPI","canonical_record":{"source":{"id":"2311.17092","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T05:53:55Z","cross_cats_sorted":[],"title_canon_sha256":"adee2096f55493672ce997f6f783d569343d666048fc8974b580d256c6fb7127","abstract_canon_sha256":"a727dfa5a5045f2b0dbd862d1733d47b575d827febb8f022e133c309fc920b20"},"schema_version":"1.0"},"canonical_sha256":"0c8c5a91e858815465edabad89e54ffcfc9a526e6eada8e157eb4c87ce67bb27","source":{"kind":"arxiv","id":"2311.17092","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.17092","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.17092v1","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.17092","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_12","alias_value":"BSGFVEPILCAV","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_16","alias_value":"BSGFVEPILCAVIZPN","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_8","alias_value":"BSGFVEPI","created_at":"2026-07-05T07:17:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BSGFVEPILCAVIZPNVOWYTZKP7T","target":"record","payload":{"canonical_record":{"source":{"id":"2311.17092","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T05:53:55Z","cross_cats_sorted":[],"title_canon_sha256":"adee2096f55493672ce997f6f783d569343d666048fc8974b580d256c6fb7127","abstract_canon_sha256":"a727dfa5a5045f2b0dbd862d1733d47b575d827febb8f022e133c309fc920b20"},"schema_version":"1.0"},"canonical_sha256":"0c8c5a91e858815465edabad89e54ffcfc9a526e6eada8e157eb4c87ce67bb27","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:57.363091Z","signature_b64":"veuoPHN6JM8BRTmD0xa974nfrprjGpOIlRJYav6cBwL4Ho9L980BDVZ/Kn8+BIeqXOOZY1fC0YiRxBPDp70+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c8c5a91e858815465edabad89e54ffcfc9a526e6eada8e157eb4c87ce67bb27","last_reissued_at":"2026-07-05T07:17:57.362659Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:57.362659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.17092","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":"2026-07-05T07:17:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t6vUiL9wsXF2/kSPERiM7syUUOOim24vNKr1VuUfN1L4kQHlTEJ2v6rJ3xv99g5tTG3C7S++VFNsSg+070e4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:28:28.073450Z"},"content_sha256":"2064f9b95922fb62b2bac2a5e834c2b32ae3443544eca65cebdc7bcae8e270c3","schema_version":"1.0","event_id":"sha256:2064f9b95922fb62b2bac2a5e834c2b32ae3443544eca65cebdc7bcae8e270c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BSGFVEPILCAVIZPNVOWYTZKP7T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SEED-Bench-2: Benchmarking Multimodal Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohao Li, Guangzhi Wang, Ruimao Zhang, Rui Wang, Ying Shan, Yixiao Ge, Yuying Ge","submitted_at":"2023-11-28T05:53:55Z","abstract_excerpt":"Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3). However, existing MLLM benchmarks remain limited to assessing only models' comprehension ability of single image-text inputs, failing to keep up with the strides made in MLLMs. A comprehensive benchmark is imperative for investigating the progress and uncovering the limitations of current MLLMs. In th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.17092","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/2311.17092/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":"2026-07-05T07:17:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HbpLkfGYNVxajecolNbWegWdvDzeJzMiBGnbf7pVeR5HUjAXcwNHuBZCyVsKHWOqsKvVL3owcFzPwBY6ajlpDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:28:28.073847Z"},"content_sha256":"fb717fc9413dbc00825db5166a8e8ffc3dba120a9654bca6cf9e3271b230cb7f","schema_version":"1.0","event_id":"sha256:fb717fc9413dbc00825db5166a8e8ffc3dba120a9654bca6cf9e3271b230cb7f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/bundle.json","state_url":"https://pith.science/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/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":"2026-08-03T16:28:28Z","links":{"resolver":"https://pith.science/pith/BSGFVEPILCAVIZPNVOWYTZKP7T","bundle":"https://pith.science/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/bundle.json","state":"https://pith.science/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BSGFVEPILCAVIZPNVOWYTZKP7T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BSGFVEPILCAVIZPNVOWYTZKP7T","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":"a727dfa5a5045f2b0dbd862d1733d47b575d827febb8f022e133c309fc920b20","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T05:53:55Z","title_canon_sha256":"adee2096f55493672ce997f6f783d569343d666048fc8974b580d256c6fb7127"},"schema_version":"1.0","source":{"id":"2311.17092","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.17092","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.17092v1","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.17092","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_12","alias_value":"BSGFVEPILCAV","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_16","alias_value":"BSGFVEPILCAVIZPN","created_at":"2026-07-05T07:17:57Z"},{"alias_kind":"pith_short_8","alias_value":"BSGFVEPI","created_at":"2026-07-05T07:17:57Z"}],"graph_snapshots":[{"event_id":"sha256:fb717fc9413dbc00825db5166a8e8ffc3dba120a9654bca6cf9e3271b230cb7f","target":"graph","created_at":"2026-07-05T07:17:57Z","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/2311.17092/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3). However, existing MLLM benchmarks remain limited to assessing only models' comprehension ability of single image-text inputs, failing to keep up with the strides made in MLLMs. A comprehensive benchmark is imperative for investigating the progress and uncovering the limitations of current MLLMs. In th","authors_text":"Bohao Li, Guangzhi Wang, Ruimao Zhang, Rui Wang, Ying Shan, Yixiao Ge, Yuying Ge","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T05:53:55Z","title":"SEED-Bench-2: Benchmarking Multimodal Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.17092","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":"sha256:2064f9b95922fb62b2bac2a5e834c2b32ae3443544eca65cebdc7bcae8e270c3","target":"record","created_at":"2026-07-05T07:17:57Z","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":"a727dfa5a5045f2b0dbd862d1733d47b575d827febb8f022e133c309fc920b20","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T05:53:55Z","title_canon_sha256":"adee2096f55493672ce997f6f783d569343d666048fc8974b580d256c6fb7127"},"schema_version":"1.0","source":{"id":"2311.17092","kind":"arxiv","version":1}},"canonical_sha256":"0c8c5a91e858815465edabad89e54ffcfc9a526e6eada8e157eb4c87ce67bb27","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c8c5a91e858815465edabad89e54ffcfc9a526e6eada8e157eb4c87ce67bb27","first_computed_at":"2026-07-05T07:17:57.362659Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:17:57.362659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"veuoPHN6JM8BRTmD0xa974nfrprjGpOIlRJYav6cBwL4Ho9L980BDVZ/Kn8+BIeqXOOZY1fC0YiRxBPDp70+DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:17:57.363091Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.17092","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2064f9b95922fb62b2bac2a5e834c2b32ae3443544eca65cebdc7bcae8e270c3","sha256:fb717fc9413dbc00825db5166a8e8ffc3dba120a9654bca6cf9e3271b230cb7f"],"state_sha256":"0f4e6679a610f91e0e376594b0fb86aefab73c0b4a9860192ae94038b1cfe6d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xSEdSfOe29xh9fpkF/0/mBxAEf7VUF+cH3W0AtikV9AAxBeMW6444mK7iUYyR0j3OP/lZeaE/v/t84xB0t9EDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:28:28.076754Z","bundle_sha256":"d18887f3520c6e74c4a58fc5c7c4639275417331a217065d642cd9e0bd4b510f"}}