{"id":"https://openalex.org/W7169595566","doi":"https://doi.org/10.48550/arxiv.2607.15142","title":"Concept-Guided Spatial Regularization for World Models in Atari Pong","display_name":"Concept-Guided Spatial Regularization for World Models in Atari Pong","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169595566","doi":"https://doi.org/10.48550/arxiv.2607.15142"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.15142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15142","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.2607.15142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085698173","display_name":"Ye Lu","orcid":"https://orcid.org/0000-0002-2376-4519"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yukuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141106708","display_name":"Zaishuo Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Zaishuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123019438","display_name":"Weyl Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Weyl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141094536","display_name":"Yubei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yubei","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.661899983882904,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.661899983882904,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.06809999793767929,"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":0.047600001096725464,"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/regularization","display_name":"Regularization (linguistics)","score":0.6345999836921692},{"id":"https://openalex.org/keywords/ball","display_name":"Ball (mathematics)","score":0.5299000144004822},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.42800000309944153},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.42800000309944153},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3652999997138977}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6543999910354614},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6261000037193298},{"id":"https://openalex.org/C122041747","wikidata":"https://www.wikidata.org/wiki/Q838611","display_name":"Ball (mathematics)","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4968999922275543},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27889999747276306}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.15142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15142","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.2607.15142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15142","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":[{"id":"https://metadata.un.org/sdg/16","score":0.5219931602478027,"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":{"World":[0],"models":[1,16,55,238],"are":[2,18,83],"usually":[3],"evaluated":[4,134],"as":[5,188],"components":[6],"of":[7,177,184],"model-based":[8],"reinforcement":[9],"learning":[10],"(MBRL)":[11],"systems,":[12],"while":[13],"the":[14,46,52,69,79,94,136,143,151,173,189,236],"world":[15,54,130],"themselves":[17],"rarely":[19],"studied":[20],"in":[21,30,135,191,227],"isolation.":[22],"We":[23,179,198],"examine":[24],"five":[25,92,141],"representative":[26],"visual":[27,86,110],"world-model":[28,250],"agents":[29],"Atari":[31],"Pong:":[32],"DreamerV3,":[33,163,228],"DIAMOND,":[34,229],"TWISTER,":[35],"Simulus,":[36],"and":[37,44,56,78,87,105,132,223,230,239],"STORM.":[38],"After":[39],"reproducing":[40],"their":[41],"training":[42,155],"pipelines":[43],"matching":[45],"reported":[47],"agent":[48,72],"performance,":[49],"we":[50,112],"freeze":[51],"learned":[53],"evaluate":[57,114],"them":[58,115],"with":[59,74,116],"a":[60,64,121,128],"closed-loop":[61,221],"rollout":[62],"diagnostic:":[63],"policy":[65,123],"trained":[66,125],"separately":[67],"from":[68,168],"corresponding":[70,152],"MBRL":[71,154,226],"interacts":[73],"each":[75],"frozen":[76,129],"model,":[77],"generated":[80],"video":[81],"trajectories":[82],"inspected":[84],"for":[85,162],"dynamical":[88],"errors.":[89],"Across":[90,139],"all":[91,140,249],"models,":[93,142],"rollouts":[95,222],"contain":[96],"clear":[97],"failures,":[98],"including":[99],"ball":[100,103,190],"disappearance,":[101],"incorrect":[102],"motion,":[104],"invalid":[106],"ball-paddle":[107],"interactions.":[108],"Beyond":[109],"trajectories,":[111],"further":[113],"pixel-space":[117,224],"zero-shot":[118,225],"MBRL,":[119],"where":[120],"new":[122],"is":[124,159],"entirely":[126],"inside":[127],"model":[131],"then":[133],"real":[137],"environment.":[138],"resulting":[144],"policies":[145],"substantially":[146],"underperform":[147],"those":[148],"produced":[149],"by":[150],"original":[153],"pipelines.":[156],"The":[157],"gap":[158],"particularly":[160],"large":[161],"whose":[164],"mean":[165],"return":[166,176],"drops":[167],"-5.5":[169],"to":[170,195,211],"-20.9,":[171],"near":[172],"minimum":[174],"Pong":[175],"-21.":[178],"hypothesize":[180],"that":[181,217,243],"insufficient":[182],"modeling":[183],"task-critical":[185],"concepts,":[186],"such":[187],"Pong,":[192],"may":[193],"contribute":[194],"these":[196],"failures.":[197],"therefore":[199],"propose":[200],"Concept-Guided":[201],"Spatial":[202],"Regularization":[203],"(CGSReg),":[204],"an":[205],"auxiliary":[206],"pixel":[207],"reconstruction":[208],"loss":[209],"applied":[210],"segmented":[212],"concept":[213],"regions.":[214],"Experiments":[215],"show":[216],"CGSReg":[218,244],"improves":[219],"both":[220],"TWISTER.":[231],"Its":[232],"effects":[233],"vary":[234],"across":[235],"remaining":[237],"evaluation":[240],"metrics,":[241],"indicating":[242],"alone":[245],"does":[246],"not":[247],"address":[248],"bottlenecks.":[251]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-18T00:00:00"}
