{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MBVH4NYNAR5MDLTY7RTCS32F3W","short_pith_number":"pith:MBVH4NYN","canonical_record":{"source":{"id":"2502.11903","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T15:24:49Z","cross_cats_sorted":[],"title_canon_sha256":"cefdc65ac988bf40a32e8c9f24dd5430f981942f01659b89bd50d9f41ce2e0c3","abstract_canon_sha256":"35dd7d3886c642aba26a713285a257129e2ab261475b8f71ebc49e1b98b05e3e"},"schema_version":"1.0"},"canonical_sha256":"606a7e370d047ac1ae78fc66296f45ddb5f42c7c7502c1fe6eb1375795962a54","source":{"kind":"arxiv","id":"2502.11903","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11903","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11903v2","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11903","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_12","alias_value":"MBVH4NYNAR5M","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_16","alias_value":"MBVH4NYNAR5MDLTY","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_8","alias_value":"MBVH4NYN","created_at":"2026-07-05T10:26:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MBVH4NYNAR5MDLTY7RTCS32F3W","target":"record","payload":{"canonical_record":{"source":{"id":"2502.11903","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T15:24:49Z","cross_cats_sorted":[],"title_canon_sha256":"cefdc65ac988bf40a32e8c9f24dd5430f981942f01659b89bd50d9f41ce2e0c3","abstract_canon_sha256":"35dd7d3886c642aba26a713285a257129e2ab261475b8f71ebc49e1b98b05e3e"},"schema_version":"1.0"},"canonical_sha256":"606a7e370d047ac1ae78fc66296f45ddb5f42c7c7502c1fe6eb1375795962a54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:43.790355Z","signature_b64":"pxlHzLCjlM3cpcDvsyWn1rvCGVIQqPBHSHY+XyeO7v2z6H5qScXLBWY8Xu7gvZPOOWqMEjHkFmSZ6dMHwWvrBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"606a7e370d047ac1ae78fc66296f45ddb5f42c7c7502c1fe6eb1375795962a54","last_reissued_at":"2026-07-05T10:26:43.789432Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:43.789432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.11903","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":"2026-07-05T10:26:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0Cq8Y4k2Jbor+j10lEutINOTpxDlziIpNucfvzjJPFvoyM3qzotJF/roHCE/LofEM1gWXNksyg17/0Jzv/ABA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:46:20.572426Z"},"content_sha256":"d579985ec506169df3a9417cf2f419f6939264772e6090e52aa2ff7f11ee9ba1","schema_version":"1.0","event_id":"sha256:d579985ec506169df3a9417cf2f419f6939264772e6090e52aa2ff7f11ee9ba1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MBVH4NYNAR5MDLTY7RTCS32F3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengzhi Liu, Chong Zhang, Chun-Mei Feng, Feilong Tang, Haochen Xue, Imran Razzak, Jionglong Su, Junjun He, Ming Hu, Qidong Huang, Yexin Liu, Yulong Li, Yu Qiao, Yutong Xie, Zhongxing Xu, Zongyuan Ge","submitted_at":"2025-02-17T15:24:49Z","abstract_excerpt":"Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, their abilities to memorize, recall, and reason in sustained interactions within real-world scenarios remain underexplored. This paper introduces MMRC, a Multi-Modal Real-world Conversation benchmark for evaluating six core open-ended abilities of MLLMs: information extraction, multi-turn reasoning, information update, image management, memory recall, and answer refusal. With data collected from real-world scenarios, M"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11903","kind":"arxiv","version":2},"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/2502.11903/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":"2026-07-05T10:26:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jgsvo4uvc89/RExFcqRKGD5P+DpWne2RaJT+BrFtLbu1bVYpFsW29BHG63zTfLEZ5PCgI2HN3Kj2qApwhCObAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:46:20.573463Z"},"content_sha256":"4fbaa11f1dc1d0ec0ba09e4d7326da802441fce2d0cc8f6a0775e9e66e2bb372","schema_version":"1.0","event_id":"sha256:4fbaa11f1dc1d0ec0ba09e4d7326da802441fce2d0cc8f6a0775e9e66e2bb372"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/bundle.json","state_url":"https://pith.science/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/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":"2026-08-07T16:46:20Z","links":{"resolver":"https://pith.science/pith/MBVH4NYNAR5MDLTY7RTCS32F3W","bundle":"https://pith.science/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/bundle.json","state":"https://pith.science/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MBVH4NYNAR5MDLTY7RTCS32F3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MBVH4NYNAR5MDLTY7RTCS32F3W","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":"35dd7d3886c642aba26a713285a257129e2ab261475b8f71ebc49e1b98b05e3e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T15:24:49Z","title_canon_sha256":"cefdc65ac988bf40a32e8c9f24dd5430f981942f01659b89bd50d9f41ce2e0c3"},"schema_version":"1.0","source":{"id":"2502.11903","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11903","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11903v2","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11903","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_12","alias_value":"MBVH4NYNAR5M","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_16","alias_value":"MBVH4NYNAR5MDLTY","created_at":"2026-07-05T10:26:43Z"},{"alias_kind":"pith_short_8","alias_value":"MBVH4NYN","created_at":"2026-07-05T10:26:43Z"}],"graph_snapshots":[{"event_id":"sha256:4fbaa11f1dc1d0ec0ba09e4d7326da802441fce2d0cc8f6a0775e9e66e2bb372","target":"graph","created_at":"2026-07-05T10:26:43Z","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/2502.11903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, their abilities to memorize, recall, and reason in sustained interactions within real-world scenarios remain underexplored. This paper introduces MMRC, a Multi-Modal Real-world Conversation benchmark for evaluating six core open-ended abilities of MLLMs: information extraction, multi-turn reasoning, information update, image management, memory recall, and answer refusal. With data collected from real-world scenarios, M","authors_text":"Chengzhi Liu, Chong Zhang, Chun-Mei Feng, Feilong Tang, Haochen Xue, Imran Razzak, Jionglong Su, Junjun He, Ming Hu, Qidong Huang, Yexin Liu, Yulong Li, Yu Qiao, Yutong Xie, Zhongxing Xu, Zongyuan Ge","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T15:24:49Z","title":"MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11903","kind":"arxiv","version":2},"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:d579985ec506169df3a9417cf2f419f6939264772e6090e52aa2ff7f11ee9ba1","target":"record","created_at":"2026-07-05T10:26:43Z","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":"35dd7d3886c642aba26a713285a257129e2ab261475b8f71ebc49e1b98b05e3e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T15:24:49Z","title_canon_sha256":"cefdc65ac988bf40a32e8c9f24dd5430f981942f01659b89bd50d9f41ce2e0c3"},"schema_version":"1.0","source":{"id":"2502.11903","kind":"arxiv","version":2}},"canonical_sha256":"606a7e370d047ac1ae78fc66296f45ddb5f42c7c7502c1fe6eb1375795962a54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"606a7e370d047ac1ae78fc66296f45ddb5f42c7c7502c1fe6eb1375795962a54","first_computed_at":"2026-07-05T10:26:43.789432Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:26:43.789432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pxlHzLCjlM3cpcDvsyWn1rvCGVIQqPBHSHY+XyeO7v2z6H5qScXLBWY8Xu7gvZPOOWqMEjHkFmSZ6dMHwWvrBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:26:43.790355Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11903","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d579985ec506169df3a9417cf2f419f6939264772e6090e52aa2ff7f11ee9ba1","sha256:4fbaa11f1dc1d0ec0ba09e4d7326da802441fce2d0cc8f6a0775e9e66e2bb372"],"state_sha256":"a8b31eef3302bbfd4c2002ede1c7b18ba1b018e5a8330bad94c509dfc2066647"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9+FLcIf7BX7TtwfXpBiR752fw+Uq7uugqLsrlrGw0tkDAX/Y2QWvslJQV5z+I3uYqPn8jhlUtSc2nl+I7pcCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:46:20.580492Z","bundle_sha256":"34afc221a620641444a463f047d5371c5df98962c7bc67c80553dbe69d921a6c"}}