{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OFXBCK4U3STZAA7KQFBVV37YY6","short_pith_number":"pith:OFXBCK4U","schema_version":"1.0","canonical_sha256":"716e112b94dca79003ea81435aeff8c79a3d303d2b9e41dab117ff56dfb76219","source":{"kind":"arxiv","id":"2507.09482","version":1},"attestation_state":"computed","paper":{"title":"ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Changli Wang, Fang Yin, Rui Wu","submitted_at":"2025-07-13T04:03:05Z","abstract_excerpt":"Human emotions are complex, with sarcasm being a subtle and distinctive form. Despite progress in sarcasm research, sarcasm generation remains underexplored, primarily due to the overreliance on textual modalities and the neglect of visual cues, as well as the mismatch between image content and sarcastic intent in existing datasets. In this paper, we introduce M2SaG, a multimodal sarcasm generation dataset with 4,970 samples, each containing an image, a sarcastic text, and a sarcasm target. To benchmark M2SaG, we propose ViSP, a generation framework that integrates Proximal Policy Optimization"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.09482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:03:05Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"72cf1bbdb6c137e1d3c9add35585545020de28819a5d7b2083472b59d7afba7a","abstract_canon_sha256":"2c9756dac7e25eb5cc4f57744b364c9f6b8d793f0c11763dfdf02f0a2c7dcf76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:09.779617Z","signature_b64":"Ue+h5xHUkd2JixkNd6fH2dwwf11KQltamXMG6U+ddOuy7tY7wUKWTxoo/RSkBNolSiqwWo/5LqdXozW/UI9SAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"716e112b94dca79003ea81435aeff8c79a3d303d2b9e41dab117ff56dfb76219","last_reissued_at":"2026-07-05T11:36:09.779213Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:09.779213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Changli Wang, Fang Yin, Rui Wu","submitted_at":"2025-07-13T04:03:05Z","abstract_excerpt":"Human emotions are complex, with sarcasm being a subtle and distinctive form. Despite progress in sarcasm research, sarcasm generation remains underexplored, primarily due to the overreliance on textual modalities and the neglect of visual cues, as well as the mismatch between image content and sarcastic intent in existing datasets. In this paper, we introduce M2SaG, a multimodal sarcasm generation dataset with 4,970 samples, each containing an image, a sarcastic text, and a sarcasm target. To benchmark M2SaG, we propose ViSP, a generation framework that integrates Proximal Policy Optimization"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09482","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/2507.09482/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.09482","created_at":"2026-07-05T11:36:09.779268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.09482v1","created_at":"2026-07-05T11:36:09.779268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09482","created_at":"2026-07-05T11:36:09.779268+00:00"},{"alias_kind":"pith_short_12","alias_value":"OFXBCK4U3STZ","created_at":"2026-07-05T11:36:09.779268+00:00"},{"alias_kind":"pith_short_16","alias_value":"OFXBCK4U3STZAA7K","created_at":"2026-07-05T11:36:09.779268+00:00"},{"alias_kind":"pith_short_8","alias_value":"OFXBCK4U","created_at":"2026-07-05T11:36:09.779268+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6","json":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6.json","graph_json":"https://pith.science/api/pith-number/OFXBCK4U3STZAA7KQFBVV37YY6/graph.json","events_json":"https://pith.science/api/pith-number/OFXBCK4U3STZAA7KQFBVV37YY6/events.json","paper":"https://pith.science/paper/OFXBCK4U"},"agent_actions":{"view_html":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6","download_json":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6.json","view_paper":"https://pith.science/paper/OFXBCK4U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.09482&json=true","fetch_graph":"https://pith.science/api/pith-number/OFXBCK4U3STZAA7KQFBVV37YY6/graph.json","fetch_events":"https://pith.science/api/pith-number/OFXBCK4U3STZAA7KQFBVV37YY6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6/action/storage_attestation","attest_author":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6/action/author_attestation","sign_citation":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6/action/citation_signature","submit_replication":"https://pith.science/pith/OFXBCK4U3STZAA7KQFBVV37YY6/action/replication_record"}},"created_at":"2026-07-05T11:36:09.779268+00:00","updated_at":"2026-07-05T11:36:09.779268+00:00"}