{"id":"https://openalex.org/W7164884150","doi":"https://doi.org/10.48550/arxiv.2606.15532","title":"EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management","display_name":"EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7164884150","doi":"https://doi.org/10.48550/arxiv.2606.15532"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15532","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.15532","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137831751","display_name":"Rongzhi Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Rongzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074455169","display_name":"XJ Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138747090","display_name":"Yuchuan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yuchuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138704347","display_name":"Rui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138705141","display_name":"Zequn Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zequn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101796074","display_name":"Tao Ren","orcid":"https://orcid.org/0000-0001-8951-3926"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034618643","display_name":"Weiyao Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Weiyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104202404","display_name":"Bingxue Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Bingxue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012728371","display_name":"J P Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Jieping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138707917","display_name":"Yongbin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yongbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138690973","display_name":"Wei Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10667","display_name":"Emotion and Mood Recognition","score":0.5422999858856201,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.5422999858856201,"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":0.1396999955177307,"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"}},{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.1152999997138977,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7874000072479248},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.6040999889373779},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5343000292778015},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.3531999886035919},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.3463999927043915},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.33309999108314514},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3066999912261963}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7874000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7498000264167786},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.6040999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5385000109672546},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4311000108718872},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C174107131","wikidata":"https://www.wikidata.org/wiki/Q191591","display_name":"Emotional intelligence","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15532","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15532","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Emotional":[0],"intelligence":[1],"(EI)":[2],"in":[3],"Large":[4],"Language":[5],"Models":[6],"(LLMs)":[7],"is":[8,22],"often":[9],"evaluated":[10],"through":[11],"static":[12],"understanding":[13],"tasks":[14],"or":[15],"single-response":[16],"dialogue":[17],"generation.":[18],"However,":[19],"emotion":[20,54,244],"management":[21],"interactive:":[23],"a":[24,31,49,74,131,188],"good":[25],"model":[26],"should":[27],"not":[28],"only":[29],"recognize":[30],"user's":[32,38],"emotion,":[33],"but":[34,167],"also":[35,218],"improve":[36,176],"the":[37,104,115,134,138,142,177,193,204],"emotional":[39],"and":[40,64,81,94,113,130,155,164,217,226,240],"relational":[41],"state":[42,109,117,136,144,196],"over":[43],"several":[44],"turns.":[45],"We":[46,151],"introduce":[47],"EIBench,":[48],"simulator-based":[50],"benchmark":[51,129],"for":[52,62,66,149,242],"interactive":[53],"management.":[55,245],"EIBench":[56,125,216],"contains":[57],"2,222":[58],"scenarios,":[59],"with":[60,169],"2,009":[61],"training":[63,132,241],"213":[65],"held-out":[67],"testing.":[68],"The":[69],"scenarios":[70],"are":[71],"organized":[72],"by":[73],"2x2":[75],"taxonomy":[76],"covering":[77],"Support,":[78],"Defense,":[79],"Repair,":[80],"Charm,":[82],"which":[83],"together":[84],"capture":[85],"different":[86],"forms":[87],"of":[88,180],"support,":[89],"boundary":[90,170],"maintenance,":[91],"trust":[92],"repair,":[93],"rapport":[95],"building.":[96],"In":[97],"each":[98,111],"scenario,":[99],"an":[100,107,119,127],"LLM":[101],"simulator":[102],"plays":[103],"user,":[105],"updates":[106,145,197],"emotion-relation":[108],"after":[110],"turn,":[112],"maps":[114],"final":[116,135,205],"to":[118,213],"anchor-based":[120],"score.":[121],"This":[122],"design":[123],"makes":[124],"both":[126,238],"evaluation":[128,239],"environment:":[133],"gives":[137],"outcome":[139,206],"reward,":[140],"while":[141,202],"per-turn":[143,195],"provide":[146],"dense":[147,199],"feedback":[148,201],"RL.":[150],"evaluate":[152],"15":[153],"open-":[154],"closed-source":[156],"LLMs.":[157],"Current":[158],"models":[159],"perform":[160],"well":[161],"on":[162,215,220],"support":[163,237],"rapport-building":[165],"scenes,":[166],"struggle":[168],"maintenance":[171],"under":[172],"user":[173,234],"pressure.":[174],"To":[175],"EI":[178],"ability":[179],"LLMs,":[181],"we":[182],"propose":[183],"Centered":[184],"Turn-Credit":[185],"GRPO":[186,189],"(CTC-GRPO),":[187],"extension":[190],"that":[191,232],"reuses":[192],"simulator's":[194],"as":[198],"turn-level":[200],"preserving":[203],"reward.":[207],"CTC-GRPO":[208],"improves":[209,219],"Qwen3-8B":[210],"from":[211],"-22.4":[212],"+22.4":[214],"out-of-distribution":[221],"evaluations":[222],"including":[223],"SAGE":[224],"(+12.4)":[225],"EQBench3":[227],"(+20.9%).":[228],"Our":[229],"results":[230],"show":[231],"simulator-tracked":[233],"states":[235],"can":[236],"multi-turn":[243]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}