{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NCATGAUSIYOKK6IATCXCZWZGSI","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":"a34e5f7625ffa53d743b9a37fe7c1dff87c6eaf15d64dcc0b2778aa597f1c288","cross_cats_sorted":["cs.CL","cs.CV","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-09T15:43:07Z","title_canon_sha256":"b1f6357a87e328c7061b11b1a14e2d5f924a6118e572f90b0bbeb45753e4e9f2"},"schema_version":"1.0","source":{"id":"2310.05804","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.05804","created_at":"2026-07-05T07:24:01Z"},{"alias_kind":"arxiv_version","alias_value":"2310.05804v2","created_at":"2026-07-05T07:24:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.05804","created_at":"2026-07-05T07:24:01Z"},{"alias_kind":"pith_short_12","alias_value":"NCATGAUSIYOK","created_at":"2026-07-05T07:24:01Z"},{"alias_kind":"pith_short_16","alias_value":"NCATGAUSIYOKK6IA","created_at":"2026-07-05T07:24:01Z"},{"alias_kind":"pith_short_8","alias_value":"NCATGAUS","created_at":"2026-07-05T07:24:01Z"}],"graph_snapshots":[{"event_id":"sha256:93ebed2f6b8bccb288f2f4cc776e62599ab4d4e6bb5d14227ac2d12cbcc90424","target":"graph","created_at":"2026-07-05T07:24:01Z","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/2310.05804/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e.g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved. To alleviate this, we present Adaptive Language-guided Multimodal Transformer (ALMT), which incorporates an Adaptive Hyper-modality Learning (AHL) module to learn an irrelevance/conflict-suppressing representation from visual and audio features under the guidance of language features at different scales. With the ","authors_text":"Guanghao Yin, Haoyu Zhang, Kejun Liu, Tianshu Yu, Yuanyuan Liu, Yu Wang","cross_cats":["cs.CL","cs.CV","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-09T15:43:07Z","title":"Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.05804","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":"sha256:0d52fa3703bf07cfcc7ca1af15f42efb23dc61604dbe0a9ebbdf0ff59bd27976","target":"record","created_at":"2026-07-05T07:24:01Z","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":"a34e5f7625ffa53d743b9a37fe7c1dff87c6eaf15d64dcc0b2778aa597f1c288","cross_cats_sorted":["cs.CL","cs.CV","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-09T15:43:07Z","title_canon_sha256":"b1f6357a87e328c7061b11b1a14e2d5f924a6118e572f90b0bbeb45753e4e9f2"},"schema_version":"1.0","source":{"id":"2310.05804","kind":"arxiv","version":2}},"canonical_sha256":"6881330292461ca5790098ae2cdb26923a9d2808c826548706e8e91c5bb570d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6881330292461ca5790098ae2cdb26923a9d2808c826548706e8e91c5bb570d3","first_computed_at":"2026-07-05T07:24:01.097493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:24:01.097493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sesShiJOM/UEobuv/Xj3HzGOppepBrN2W2u8Hwv1p3DbwuZMesIWi+B0Jv2sEc/1GSON1QwawyCeThj8qq5/Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:24:01.097924Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.05804","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d52fa3703bf07cfcc7ca1af15f42efb23dc61604dbe0a9ebbdf0ff59bd27976","sha256:93ebed2f6b8bccb288f2f4cc776e62599ab4d4e6bb5d14227ac2d12cbcc90424"],"state_sha256":"a1694cddd03be0cee4978d5cc7354c8e2dc186bc1d0d13b751587ce62d3caece"}