{"id":"https://openalex.org/W4414858668","doi":"https://doi.org/10.48550/arxiv.2505.24784","title":"AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models","display_name":"AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models","publication_year":2025,"publication_date":"2025-05-30","ids":{"openalex":"https://openalex.org/W4414858668","doi":"https://doi.org/10.48550/arxiv.2505.24784"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.24784","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.24784","pdf_url":"https://arxiv.org/pdf/2505.24784","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.24784","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026661010","display_name":"Conor Heins","orcid":"https://orcid.org/0000-0002-5884-7728"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Heins, Conor","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062379167","display_name":"Toon Van de Maele","orcid":"https://orcid.org/0000-0001-7583-3291"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van de Maele, Toon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027042078","display_name":"Alexander Tschantz","orcid":"https://orcid.org/0000-0002-9751-5929"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tschantz, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019660414","display_name":"Hampus Linander","orcid":"https://orcid.org/0000-0002-7865-8414"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Linander, Hampus","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076881344","display_name":"Dimitrije Markovi\u0107","orcid":"https://orcid.org/0000-0002-4185-696X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Markovic, Dimitrije","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031081461","display_name":"Tommaso Salvatori","orcid":"https://orcid.org/0000-0002-7254-9882"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salvatori, Tommaso","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040457648","display_name":"Corrado Pezzato","orcid":"https://orcid.org/0000-0002-1835-9578"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pezzato, Corrado","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024997865","display_name":"Ozan \u00c7atal","orcid":"https://orcid.org/0000-0002-0216-7918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Catal, Ozan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068525508","display_name":"Ran Wei","orcid":"https://orcid.org/0000-0002-7320-5571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Ran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052383262","display_name":"Magnus Koudahl","orcid":"https://orcid.org/0000-0003-3411-5036"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koudahl, Magnus","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061876914","display_name":"Marco Perin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perin, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086852785","display_name":"Karl Friston","orcid":"https://orcid.org/0000-0001-7984-8909"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Friston, Karl","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071221406","display_name":"Tim Verbelen","orcid":"https://orcid.org/0000-0003-2731-7262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Verbelen, Tim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026149273","display_name":"Christopher L. Buckley","orcid":"https://orcid.org/0000-0002-8551-9121"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Buckley, Christopher","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":14,"corresponding_author_ids":["https://openalex.org/A5026661010"],"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/T11574","display_name":"Artificial Intelligence in Games","score":0.9563999772071838,"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":0.9563999772071838,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9397000074386597,"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/interpretability","display_name":"Interpretability","score":0.6865000128746033},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6161999702453613},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.565500020980835},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5249000191688538},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5037000179290771},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5033000111579895},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4828000068664551},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45500001311302185},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.42320001125335693}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6865000128746033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6829000115394592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6348000168800354},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6161999702453613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.58160001039505},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5033000111579895},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.42320001125335693},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C44210515","wikidata":"https://www.wikidata.org/wiki/Q16968978","display_name":"Bespoke","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C2777723229","wikidata":"https://www.wikidata.org/wiki/Q4367921","display_name":"Learnability","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C167729594","wikidata":"https://www.wikidata.org/wiki/Q17736","display_name":"Axiom","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3125},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.24784","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.24784","pdf_url":"https://arxiv.org/pdf/2505.24784","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.24784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.24784","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:oai:arXiv.org:2505.24784","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.24784","pdf_url":"https://arxiv.org/pdf/2505.24784","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Current":[0],"deep":[1],"reinforcement":[2],"learning":[3,108,169],"(DRL)":[4],"approaches":[5,128],"achieve":[6],"state-of-the-art":[7],"performance":[8],"in":[9,109],"various":[10,187],"domains,":[11],"but":[12],"struggle":[13],"with":[14,40,69,129,135,194],"data":[15,122],"efficiency":[16,123],"compared":[17,201],"to":[18,43,106,182,202],"human":[19],"learning,":[20],"which":[21,115],"leverages":[22],"core":[23,99],"priors":[24,100],"about":[25,101],"objects":[26],"and":[27,48,56,104,124,168,175,204],"their":[28],"interactions.":[29,156],"Active":[30],"inference":[31,60],"offers":[32],"a":[33,45,66,88,93,196],"principled":[34],"framework":[35],"for":[36,65],"integrating":[37],"sensory":[38],"information":[39],"prior":[41],"knowledge":[42],"learn":[44],"world":[46],"model":[47,162,180],"quantify":[49],"the":[50,75,120,130,160,206],"uncertainty":[51],"of":[52,79,98,126,142,159,199,209],"its":[53],"own":[54],"beliefs":[55],"predictions.":[57],"However,":[58],"active":[59],"models":[61,171],"are":[62,146],"usually":[63,133],"crafted":[64],"single":[67,173],"task":[68],"bespoke":[70],"knowledge,":[71],"so":[72],"they":[73],"lack":[74],"domain":[76],"flexibility":[77],"typical":[78],"DRL":[80],"approaches.":[81],"To":[82],"bridge":[83],"this":[84],"gap,":[85],"we":[86,116],"propose":[87],"novel":[89],"architecture":[90],"that":[91,152],"integrates":[92],"minimal":[94],"yet":[95],"expressive":[96],"set":[97],"object-centric":[102],"dynamics":[103,145],"interactions":[105],"accelerate":[107],"low-data":[110],"regimes.":[111],"The":[112,157],"resulting":[113],"approach,":[114],"call":[117],"AXIOM,":[118],"combines":[119],"usual":[121],"interpretability":[125],"Bayesian":[127,179],"across-task":[131],"generalization":[132],"associated":[134],"DRL.":[136],"AXIOM":[137,185],"represents":[138],"scenes":[139],"as":[140,148],"compositions":[141],"objects,":[143],"whose":[144],"modeled":[147],"piecewise":[149],"linear":[150],"trajectories":[151],"capture":[153],"sparse":[154],"object-object":[155],"structure":[158],"generative":[161],"is":[163],"expanded":[164],"online":[165],"by":[166],"growing":[167],"mixture":[170],"from":[172],"events":[174],"periodically":[176],"refined":[177],"through":[178],"reduction":[181],"induce":[183],"generalization.":[184],"masters":[186],"games":[188],"within":[189],"only":[190],"10,000":[191],"interaction":[192],"steps,":[193],"both":[195],"small":[197],"number":[198],"parameters":[200],"DRL,":[203],"without":[205],"computational":[207],"expense":[208],"gradient-based":[210],"optimization.":[211]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
