{"id":"https://openalex.org/W7162643785","doi":"https://doi.org/10.48550/arxiv.2605.27767","title":"UniMaia: Steering Chess Policies with Language for Human-like Play","display_name":"UniMaia: Steering Chess Policies with Language for Human-like Play","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162643785","doi":"https://doi.org/10.48550/arxiv.2605.27767"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27767","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":"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.2605.27767","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050031218","display_name":"Sherman Siu","orcid":"https://orcid.org/0000-0002-4489-6430"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Siu, Sherman","raw_affiliation_strings":["University of Waterloo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085902284","display_name":"Lesley Istead","orcid":"https://orcid.org/0000-0003-0063-8154"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Istead, Lesley","raw_affiliation_strings":["University of Waterloo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"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/T11574","display_name":"Artificial Intelligence in Games","score":0.4918999969959259,"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"}},"topics":[{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.4918999969959259,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.1843000054359436,"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":0.06030000001192093,"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/construct","display_name":"Construct (python library)","score":0.6126999855041504},{"id":"https://openalex.org/keywords/controllability","display_name":"Controllability","score":0.5536999702453613},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5264999866485596},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.49790000915527344},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4903999865055084},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.462799996137619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878000140190125},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6126999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557699978351593},{"id":"https://openalex.org/C48209547","wikidata":"https://www.wikidata.org/wiki/Q1331104","display_name":"Controllability","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4903999865055084},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.462799996137619},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3682999908924103},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3199000060558319},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25459998846054077},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27767","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":"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.2605.27767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27767","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":"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":[{"score":0.7344480156898499,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,191],"large":[3],"language":[4,9,53],"models":[5,54],"have":[6],"enabled":[7],"natural":[8],"to":[10],"serve":[11],"as":[12,39],"a":[13,67,75,82,87,128,134],"flexible":[14,57],"interface":[15],"for":[16,69],"controlling":[17],"complex":[18],"systems,":[19],"but":[20,47],"often":[21],"at":[22],"the":[23,105],"cost":[24],"of":[25,201],"large-scale":[26,129],"multimodal":[27,209],"training":[28],"or":[29],"weakened":[30],"domain-specific":[31,202],"inductive":[32],"biases.":[33],"In":[34],"structured":[35],"decision-making":[36],"domains":[37],"such":[38],"chess,":[40],"specialized":[41],"policy":[42,71,79,107,203],"networks":[43,204],"achieve":[44],"strong":[45],"performance":[46],"lack":[48],"semantic":[49,93],"controllability,":[50],"while":[51,103,164,211],"prompt-conditioned":[52,70,143,154,199],"are":[55],"more":[56],"yet":[58],"typically":[59],"exhibit":[60],"weaker":[61],"domain":[62],"grounding.":[63],"We":[64,109],"propose":[65],"$\\textbf{UniMaia}$,":[66],"framework":[68],"modulation":[72],"that":[73,198],"adapts":[74],"frozen":[76],"Lc0-based":[77],"chess":[78],"network":[80],"using":[81],"parameter-efficient":[83],"text":[84],"encoder":[85],"and":[86,100,118,138,144,156,181,216],"ControlNet-style":[88],"conditioning":[89,117],"mechanism.":[90],"UniMaia":[91,147],"enables":[92],"control":[94,200],"over":[95],"gameplay,":[96],"including":[97],"opening":[98],"selection":[99],"player":[101],"strength,":[102],"preserving":[104],"pretrained":[106],"representations.":[108],"further":[110,177],"introduce":[111,139],"$\\textbf{UniMaia-Aux}$,":[112],"which":[113],"incorporates":[114],"auxiliary":[115],"temporal":[116],"behavioral":[119,182],"prediction":[120,174],"objectives.":[121],"To":[122],"support":[123],"this":[124],"work,":[125],"we":[126],"construct":[127],"metadata-augmented":[130],"Lichess":[131],"dataset,":[132],"develop":[133],"semi-automated":[135],"prompt-generation":[136],"pipeline,":[137],"benchmarks":[140,155],"spanning":[141],"both":[142],"metadata-conditioned":[145,169],"settings.":[146],"achieves":[148],"state-of-the-art":[149],"expected":[150,179],"accuracy":[151,159,180],"on":[152,160,171],"several":[153,185],"competitive":[157,166],"top-move":[158,192],"general":[161],"instruction-following":[162],"tasks,":[163],"remaining":[165],"with":[167,188],"dedicated":[168],"approaches":[170],"human":[172],"move":[173],"benchmarks.":[175],"UniMaia-Aux":[176],"improves":[178],"modeling":[183],"across":[184],"evaluation":[186],"settings,":[187],"modest":[189],"trade-offs":[190,213],"accuracy.":[193],"Overall,":[194],"our":[195],"results":[196],"demonstrate":[197],"is":[205],"feasible":[206],"without":[207],"end-to-end":[208],"training,":[210],"highlighting":[212],"between":[214],"controllability":[215],"predictive":[217],"performance.":[218]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-29T00:00:00"}
