{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2UFB3O2DWSXNXHJG73F66MBCN3","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":"0b263741b88d5e59835c8b3c0c6c6ebc3eee29b6599ebad6cb4d20e1a5dad056","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-04T16:00:21Z","title_canon_sha256":"accc22af02147effb8f40d8232f1dc96e16b0bb2545b7ad82e0b7d44dc4594b3"},"schema_version":"1.0","source":{"id":"2409.02834","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02834","created_at":"2026-07-05T09:29:33Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02834v3","created_at":"2026-07-05T09:29:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02834","created_at":"2026-07-05T09:29:33Z"},{"alias_kind":"pith_short_12","alias_value":"2UFB3O2DWSXN","created_at":"2026-07-05T09:29:33Z"},{"alias_kind":"pith_short_16","alias_value":"2UFB3O2DWSXNXHJG","created_at":"2026-07-05T09:29:33Z"},{"alias_kind":"pith_short_8","alias_value":"2UFB3O2D","created_at":"2026-07-05T09:29:33Z"}],"graph_snapshots":[{"event_id":"sha256:a1b043bba378bbc9c92835da21a466636bb83630e9b6ec6ec9c597e64c09beef","target":"graph","created_at":"2026-07-05T09:29:33Z","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/2409.02834/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improving and measuring the performance of LLMs based on textual math reasoning datasets (e.g., MATH, GSM8K). Recently, a few researchers have released English multimodal math datasets (e.g., MATHVISTA and MATH-V) to evaluate the effectiveness of large multimodal models (LMMs). In this paper, we release a Chinese multimodal math (CMM-Math) dataset, including benchmark and training parts, to evaluate and enhance the mathemat","authors_text":"Aimin Zhou, Bo Jiang, Jie Zhou, Ji Wu, Liang He, Qianjun Pan, Qin Chen, Wentao Liu, Yi Zhang, Zhuo Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-04T16:00:21Z","title":"CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02834","kind":"arxiv","version":3},"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:c1444498b9d1f693c2b020e12bb22858d2e7799eace575fed1033ede3f91c710","target":"record","created_at":"2026-07-05T09:29:33Z","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":"0b263741b88d5e59835c8b3c0c6c6ebc3eee29b6599ebad6cb4d20e1a5dad056","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-04T16:00:21Z","title_canon_sha256":"accc22af02147effb8f40d8232f1dc96e16b0bb2545b7ad82e0b7d44dc4594b3"},"schema_version":"1.0","source":{"id":"2409.02834","kind":"arxiv","version":3}},"canonical_sha256":"d50a1dbb43b4aedb9d26fecbef30226efa7f788c1c5dbedcd42dfbe9f9e35782","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d50a1dbb43b4aedb9d26fecbef30226efa7f788c1c5dbedcd42dfbe9f9e35782","first_computed_at":"2026-07-05T09:29:33.011754Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:29:33.011754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JN+PRnqcEwkYUoMfjPYXgkfNKA9FbIHLb6YkXhfmOaOBPd7OmlJhOoHGEy2fRzemhRYORXYfeFfqVDpOV/IFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:29:33.012283Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.02834","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1444498b9d1f693c2b020e12bb22858d2e7799eace575fed1033ede3f91c710","sha256:a1b043bba378bbc9c92835da21a466636bb83630e9b6ec6ec9c597e64c09beef"],"state_sha256":"8a454e8f113a33a6c5960d7d591778dcf04512959cc33b20ace2e524ae279bb0"}