{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GDS2XEROTGROTKNTNIPVQUAKHC","short_pith_number":"pith:GDS2XERO","canonical_record":{"source":{"id":"2506.07086","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-08T11:15:57Z","cross_cats_sorted":[],"title_canon_sha256":"4b85245421c20b8b394f61e32da7e79547ac01cbd4594918467ae7df225005d0","abstract_canon_sha256":"1188cbdd59956ef688efc884ae1ffc344b1f0d8954348262c6c41b87bcaee1f5"},"schema_version":"1.0"},"canonical_sha256":"30e5ab922e99a2e9a9b36a1f58500a388eef06b8e934807c2e26358440c2758f","source":{"kind":"arxiv","id":"2506.07086","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07086","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07086v1","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07086","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_12","alias_value":"GDS2XEROTGRO","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_16","alias_value":"GDS2XEROTGROTKNT","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_8","alias_value":"GDS2XERO","created_at":"2026-07-05T11:18:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GDS2XEROTGROTKNTNIPVQUAKHC","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07086","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-08T11:15:57Z","cross_cats_sorted":[],"title_canon_sha256":"4b85245421c20b8b394f61e32da7e79547ac01cbd4594918467ae7df225005d0","abstract_canon_sha256":"1188cbdd59956ef688efc884ae1ffc344b1f0d8954348262c6c41b87bcaee1f5"},"schema_version":"1.0"},"canonical_sha256":"30e5ab922e99a2e9a9b36a1f58500a388eef06b8e934807c2e26358440c2758f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:01.026289Z","signature_b64":"4XFgLnTVVPgsb3WpxQqL/MEinwAZZklCcBlDa8iUr34QPGQTnOdp5XceGkbo6qPBG8XOsrNlj6TAC0bMg0w1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30e5ab922e99a2e9a9b36a1f58500a388eef06b8e934807c2e26358440c2758f","last_reissued_at":"2026-07-05T11:18:01.025872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:01.025872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07086","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":"2026-07-05T11:18:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CLx/jsZkmfd7yeBzKVmrTlK/jPVLeu7N9rTFFIihfIrc6foGDKoFA+grrukeU/1yWZCI5TPa7rMZHNrIoh6JDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:54:15.056872Z"},"content_sha256":"d5190ab6c187e853998eb2606e885ceb11dd074b983883bf479acee543d0e3d3","schema_version":"1.0","event_id":"sha256:d5190ab6c187e853998eb2606e885ceb11dd074b983883bf479acee543d0e3d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GDS2XEROTGROTKNTNIPVQUAKHC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Guoqing Jin, Lei Zhang, Pengsen Cheng, Yan Song, Yuanhe Tian","submitted_at":"2025-06-08T11:15:57Z","abstract_excerpt":"Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text, thereby enhancing human-computer interaction and emotion understanding. Existing approaches typically rely on unimodal analysis or straightforward fusion of cross-modal information that fail to capture complex and conflicting evidence presented across different modalities. In this paper, we propose a novel LLM-based approach for affective computing that explicitly deconstructs visual and textual representations into shared (modality-invariant) and mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07086","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/2506.07086/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":"2026-07-05T11:18:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JcQEdgaOkFKo6bNGYI/5jzdyg24NiHj05Q45zMIaEQRQUh9ElGTrZ10dOEYIZ9I7PvVgqIFvUubk2ze5EB/mCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:54:15.057367Z"},"content_sha256":"eb25c3700559a847b94bd90ba1d4ed089830cf4e0806a6423f2a39c22a14376a","schema_version":"1.0","event_id":"sha256:eb25c3700559a847b94bd90ba1d4ed089830cf4e0806a6423f2a39c22a14376a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GDS2XEROTGROTKNTNIPVQUAKHC/bundle.json","state_url":"https://pith.science/pith/GDS2XEROTGROTKNTNIPVQUAKHC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GDS2XEROTGROTKNTNIPVQUAKHC/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":"2026-08-07T16:54:15Z","links":{"resolver":"https://pith.science/pith/GDS2XEROTGROTKNTNIPVQUAKHC","bundle":"https://pith.science/pith/GDS2XEROTGROTKNTNIPVQUAKHC/bundle.json","state":"https://pith.science/pith/GDS2XEROTGROTKNTNIPVQUAKHC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GDS2XEROTGROTKNTNIPVQUAKHC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GDS2XEROTGROTKNTNIPVQUAKHC","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":"1188cbdd59956ef688efc884ae1ffc344b1f0d8954348262c6c41b87bcaee1f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-08T11:15:57Z","title_canon_sha256":"4b85245421c20b8b394f61e32da7e79547ac01cbd4594918467ae7df225005d0"},"schema_version":"1.0","source":{"id":"2506.07086","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07086","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07086v1","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07086","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_12","alias_value":"GDS2XEROTGRO","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_16","alias_value":"GDS2XEROTGROTKNT","created_at":"2026-07-05T11:18:01Z"},{"alias_kind":"pith_short_8","alias_value":"GDS2XERO","created_at":"2026-07-05T11:18:01Z"}],"graph_snapshots":[{"event_id":"sha256:eb25c3700559a847b94bd90ba1d4ed089830cf4e0806a6423f2a39c22a14376a","target":"graph","created_at":"2026-07-05T11:18:01Z","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/2506.07086/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text, thereby enhancing human-computer interaction and emotion understanding. Existing approaches typically rely on unimodal analysis or straightforward fusion of cross-modal information that fail to capture complex and conflicting evidence presented across different modalities. In this paper, we propose a novel LLM-based approach for affective computing that explicitly deconstructs visual and textual representations into shared (modality-invariant) and mod","authors_text":"Guoqing Jin, Lei Zhang, Pengsen Cheng, Yan Song, Yuanhe Tian","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-08T11:15:57Z","title":"Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07086","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:d5190ab6c187e853998eb2606e885ceb11dd074b983883bf479acee543d0e3d3","target":"record","created_at":"2026-07-05T11:18:01Z","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":"1188cbdd59956ef688efc884ae1ffc344b1f0d8954348262c6c41b87bcaee1f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-08T11:15:57Z","title_canon_sha256":"4b85245421c20b8b394f61e32da7e79547ac01cbd4594918467ae7df225005d0"},"schema_version":"1.0","source":{"id":"2506.07086","kind":"arxiv","version":1}},"canonical_sha256":"30e5ab922e99a2e9a9b36a1f58500a388eef06b8e934807c2e26358440c2758f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30e5ab922e99a2e9a9b36a1f58500a388eef06b8e934807c2e26358440c2758f","first_computed_at":"2026-07-05T11:18:01.025872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:01.025872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4XFgLnTVVPgsb3WpxQqL/MEinwAZZklCcBlDa8iUr34QPGQTnOdp5XceGkbo6qPBG8XOsrNlj6TAC0bMg0w1Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:01.026289Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07086","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d5190ab6c187e853998eb2606e885ceb11dd074b983883bf479acee543d0e3d3","sha256:eb25c3700559a847b94bd90ba1d4ed089830cf4e0806a6423f2a39c22a14376a"],"state_sha256":"3dcafb2d8ddc86473c4d94ab9f45051ebc3a3a5050e5465ac6b2c30636a5e5de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o078kZkcAGFQVoFnkz08NDnaIcQ6j86KTUjBGyygSkoOXyCabgCdVJn1iPvboMPLIJts/78cFGczc72X90YxDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:54:15.060986Z","bundle_sha256":"23e94f724b6a9c3edb80a856e9134e9bf3852dc80741c3950c9df076ab55dcd7"}}