{"id":"https://openalex.org/W7134073896","doi":"https://doi.org/10.48550/arxiv.2603.05275","title":"SarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning","display_name":"SarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134073896","doi":"https://doi.org/10.48550/arxiv.2603.05275"},"language":"en","primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05275","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128238731","display_name":"Zhu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Zhu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Yongjian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yongjian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124380056","display_name":"Huiyuan Lai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lai, Huiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101560145","display_name":"Xiyuan Gao","orcid":"https://orcid.org/0000-0003-0870-6721"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Xiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001110193","display_name":"Shekhar Nayak","orcid":"https://orcid.org/0000-0002-4277-4851"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nayak, Shekhar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090819693","display_name":"Matt Coler","orcid":"https://orcid.org/0000-0002-7631-5063"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Coler, Matt","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5128238731"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3465999960899353,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3465999960899353,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.15379999577999115,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.039500001817941666,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sarcasm","display_name":"Sarcasm","score":0.9417999982833862},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5856999754905701},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5135999917984009},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3982999920845032},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.32249999046325684},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.2944999933242798}],"concepts":[{"id":"https://openalex.org/C2776207355","wikidata":"https://www.wikidata.org/wiki/Q191035","display_name":"Sarcasm","level":3,"score":0.9417999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.666100025177002},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5856999754905701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5544999837875366},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40389999747276306},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4009000062942505},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3328999876976013},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32249999046325684},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.31619998812675476},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C2780400661","wikidata":"https://www.wikidata.org/wiki/Q8154361","display_name":"Wishful thinking","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05275","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:pure.rug.nl:openaire_cris_publications/608e3c28-9d8b-490d-972d-6cbc8e57607f","is_oa":true,"landing_page_url":"https://hdl.handle.net/11370/608e3c28-9d8b-490d-972d-6cbc8e57607f","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Li, Z, Chen, Y, Lai, H, Gao, X, Nayak, S & Coler, M 2026 'SarcasmMiner : A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning' arXiv. https://doi.org/10.48550/arXiv.2603.05275","raw_type":"info:eu-repo/semantics/preprint"},{"id":"pmh:oai:pure.rug.nl:publications/608e3c28-9d8b-490d-972d-6cbc8e57607f","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/608e3c28-9d8b-490d-972d-6cbc8e57607f","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Li, Z, Chen, Y, Lai, H, Gao, X, Nayak, S & Coler, M 2026 'SarcasmMiner : A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning' arXiv. https://doi.org/10.48550/arXiv.2603.05275","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2603.05275","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05275","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.05275","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.6624485850334167,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"sarcasm":[1,19,41],"detection":[2,42],"requires":[3],"resolving":[4],"pragmatic":[5],"incongruity":[6],"across":[7],"textual,":[8],"acoustic,":[9],"and":[10,46,90,116],"visual":[11],"cues":[12],"through":[13],"cross-modal":[14],"reasoning.":[15,38],"To":[16],"enable":[17],"robust":[18],"reasoning":[20,45,73,91],"with":[21,79],"foundation":[22],"models,":[23],"we":[24],"propose":[25],"SarcasmMiner,":[26],"a":[27,48,66],"reinforcement":[28],"learning":[29],"based":[30],"post-training":[31],"framework":[32],"that":[33,109],"resists":[34],"hallucination":[35],"in":[36],"multimodal":[37,117],"We":[39],"reformulate":[40],"as":[43],"structured":[44],"adopt":[47],"dual-track":[49],"distillation":[50],"strategy:":[51],"high-quality":[52],"teacher":[53],"trajectories":[54,64],"initialize":[55],"the":[56,60],"student":[57,76],"model,":[58],"while":[59],"full":[61],"set":[62],"of":[63],"trains":[65],"generative":[67],"reward":[68,111],"model":[69],"(GenRM)":[70],"to":[71,104],"evaluate":[72],"quality.":[74,92],"The":[75],"is":[77],"optimized":[78],"group":[80],"relative":[81],"policy":[82],"optimization":[83],"(GRPO)":[84],"using":[85],"decoupled":[86],"rewards":[87],"for":[88],"accuracy":[89],"On":[93],"MUStARD++,":[94],"SarcasmMiner":[95],"increases":[96],"F1":[97],"from":[98],"59.83%":[99],"(zero-shot),":[100],"68.23%":[101],"(supervised":[102],"finetuning)":[103],"70.22%.":[105],"These":[106],"findings":[107],"suggest":[108],"reasoning-aware":[110],"modeling":[112],"enhances":[113],"both":[114],"performance":[115],"grounding.":[118]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-07T00:00:00"}
