{"id":"https://openalex.org/W7167618004","doi":"https://doi.org/10.48550/arxiv.2607.04293","title":"CausalGame: Benchmarking Causal Thinking of LLM Agents in Games","display_name":"CausalGame: Benchmarking Causal Thinking of LLM Agents in Games","publication_year":2026,"publication_date":"2026-07-05","ids":{"openalex":"https://openalex.org/W7167618004","doi":"https://doi.org/10.48550/arxiv.2607.04293"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.04293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2607.04293","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140214812","display_name":"Zhenhao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140172239","display_name":"Yongqiang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yongqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140207181","display_name":"Chenxi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chenxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060684352","display_name":"Junchi Yu","orcid":"https://orcid.org/0000-0003-4118-3248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Junchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140175134","display_name":"Xiangchen Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Xiangchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140186923","display_name":"Zijian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140164229","display_name":"Jialin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jialin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140209074","display_name":"Philip Torr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torr, Philip","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071638276","display_name":"Bo Han","orcid":"https://orcid.org/0000-0003-0983-9382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065782115","display_name":"K Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kun","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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.26489999890327454,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.26489999890327454,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.20569999516010284,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0925000011920929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/causation","display_name":"Causation","score":0.6047000288963318},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5957000255584717},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.546999990940094},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.5130000114440918},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5084999799728394},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.4973999857902527},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4341000020503998}],"concepts":[{"id":"https://openalex.org/C166151441","wikidata":"https://www.wikidata.org/wiki/Q4923601","display_name":"Causation","level":2,"score":0.6047000288963318},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5957000255584717},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5623000264167786},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.546999990940094},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.4318000078201294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42829999327659607},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.42669999599456787},{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30160000920295715},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C116860108","wikidata":"https://www.wikidata.org/wiki/Q5054575","display_name":"Causal theory of reference","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.04293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.04293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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":{"Building":[0],"AI":[1,51,177],"Scientist":[2,178],"agents":[3,88,95],"with":[4,109],"Large":[5],"Language":[6],"Models":[7],"(LLMs)":[8],"has":[9],"recently":[10],"attracted":[11],"growing":[12],"attention.":[13],"Since":[14],"scientific":[15,70,116],"discovery":[16,117],"fundamentally":[17],"relies":[18],"on":[19,160],"uncovering":[20],"causal":[21,28,83,139,174],"relationships":[22],"from":[23,33,57],"observations,":[24],"the":[25,82,141,161,173],"capability":[26],"of":[27,47,86,151,156,176],"thinking,":[29],"i.e.,":[30],"distinguishing":[31],"causation":[32],"correlation":[34],"and":[35,62,104,129,153,168],"recognizing":[36],"hidden":[37,63,130],"biases,":[38],"is":[39],"essential":[40],"to":[41,96],"LLM":[42,87,94,134],"agents.":[43,179],"Although":[44],"a":[45,78,106,166],"number":[46],"benchmarks":[48],"exist":[49,67],"for":[50,171],"Scientists,":[52],"none":[53,136],"explicitly":[54],"incorporate":[55,124],"challenges":[56],"selection":[58,125],"bias,":[59,126],"measurement":[60,127],"error,":[61,128],"confounders":[64],"that":[65,80,123],"widely":[66],"in":[68],"real-world":[69],"discovery.":[71],"To":[72,113],"this":[73],"end,":[74],"we":[75,119],"present":[76],"CausalGame,":[77],"benchmark":[79],"evaluates":[81],"thinking":[84,175],"capabilities":[85],"through":[89],"interactive":[90],"games.":[91],"CausalGame":[92,164],"asks":[93],"actively":[97],"design":[98,120],"experimental":[99],"protocols,":[100],"collect":[101],"observation":[102],"data,":[103],"derive":[105],"final":[107],"solution":[108],"an":[110],"explanation":[111],"report.":[112],"emulate":[114],"realistic":[115],"challenges,":[118],"14":[121],"scenarios":[122],"confounders.":[131],"Across":[132],"30":[133],"agents,":[135],"demonstrates":[137],"reliable":[138],"thinking:":[140],"best":[142],"model":[143],"reaches":[144],"only":[145],"68.0%":[146],"survival":[147],"against":[148],"analytical":[149],"optima":[150],"78-85%,":[152],"merely":[154],"5-7%":[155],"sessions":[157],"receive":[158],"credits":[159],"causal-reasoning":[162],"rubrics.":[163],"provides":[165],"scalable":[167],"controlled":[169],"testbed":[170],"evaluating":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}
