{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2FFOU4XYMUB2XX5QJF5DH53SNH","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":"86ee0b215716e55972a2d6ef393cd45a0e2bfd50db866e85cf441cc37a38b768","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T06:41:09Z","title_canon_sha256":"050e68e7aa56f24ccd4bbd6500288302261571369e34285f6e604d6c49e642d7"},"schema_version":"1.0","source":{"id":"2506.02510","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.02510","created_at":"2026-07-05T11:55:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.02510v1","created_at":"2026-07-05T11:55:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02510","created_at":"2026-07-05T11:55:19Z"},{"alias_kind":"pith_short_12","alias_value":"2FFOU4XYMUB2","created_at":"2026-07-05T11:55:19Z"},{"alias_kind":"pith_short_16","alias_value":"2FFOU4XYMUB2XX5Q","created_at":"2026-07-05T11:55:19Z"},{"alias_kind":"pith_short_8","alias_value":"2FFOU4XY","created_at":"2026-07-05T11:55:19Z"}],"graph_snapshots":[{"event_id":"sha256:9cd7a0e4a2da63b17c348e79c0236a7472998787136131eaa056a082cb02580b","target":"graph","created_at":"2026-07-05T11:55:19Z","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/2506.02510/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financial benchmarks often rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. To address this gap, we propose a novel benchmark called $\\texttt{M$^3$FinMeeting}$, which is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding. First, $\\texttt{M$^3$FinMeeting}$ supports English, Chinese, and Ja","authors_text":"Feng Chen, Jie Zhu, Junhui Li, Lifan Guo, Xiandong Li, Yalong Wen","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T06:41:09Z","title":"M$^3$FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02510","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":"sha256:0520b19f126686c224ee2587bad485eb4792a1e66c1c1431290dfb05c9fd4796","target":"record","created_at":"2026-07-05T11:55:19Z","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":"86ee0b215716e55972a2d6ef393cd45a0e2bfd50db866e85cf441cc37a38b768","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T06:41:09Z","title_canon_sha256":"050e68e7aa56f24ccd4bbd6500288302261571369e34285f6e604d6c49e642d7"},"schema_version":"1.0","source":{"id":"2506.02510","kind":"arxiv","version":1}},"canonical_sha256":"d14aea72f86503abdfb0497a33f77269d80400ef7f9346f037cc70670d74408e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d14aea72f86503abdfb0497a33f77269d80400ef7f9346f037cc70670d74408e","first_computed_at":"2026-07-05T11:55:19.766522Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:55:19.766522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Odorx2ORVCGGH/5FFzbwNHLhCYoUJl8QnozZ5fmYuTokQVp8QAVWMLaLQV/x7f5afgRqaK9FGzag6Lb/gNtcDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:55:19.766941Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.02510","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0520b19f126686c224ee2587bad485eb4792a1e66c1c1431290dfb05c9fd4796","sha256:9cd7a0e4a2da63b17c348e79c0236a7472998787136131eaa056a082cb02580b"],"state_sha256":"04adf40e7d6850091e10fc8b6043914e4d0514d77b6b66493f22eedc388ca49b"}