{"id":"https://openalex.org/W7160355076","doi":"https://doi.org/10.48550/arxiv.2605.01324","title":"Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs","display_name":"Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs","publication_year":2026,"publication_date":"2026-05-02","ids":{"openalex":"https://openalex.org/W7160355076","doi":"https://doi.org/10.48550/arxiv.2605.01324"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01324","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":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.2605.01324","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135344806","display_name":"Jingze Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jingze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135386535","display_name":"Quan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Quan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028402689","display_name":"Hongfei Suo","orcid":"https://orcid.org/0000-0001-8643-1685"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suo, Hongfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064887672","display_name":"Zeqiang Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zeqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135414972","display_name":"Hongbo Chen","orcid":"https://orcid.org/0009-0002-1924-1210"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongbo","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6306999921798706,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6306999921798706,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.08030000329017639,"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":0.037700001150369644,"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/debiasing","display_name":"Debiasing","score":0.989300012588501},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7753000259399414},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.6829000115394592},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.45840001106262207},{"id":"https://openalex.org/keywords/belief-revision","display_name":"Belief revision","score":0.44369998574256897},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.37860000133514404},{"id":"https://openalex.org/keywords/causal-reasoning","display_name":"Causal reasoning","score":0.3727000057697296},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.37040001153945923}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.989300012588501},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7753000259399414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311000227928162},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.6829000115394592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5220999717712402},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C128913409","wikidata":"https://www.wikidata.org/wiki/Q3566063","display_name":"Belief revision","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.3727000057697296},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.34040001034736633},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3077000081539154},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.28220000863075256},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01324","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":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.2605.01324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01324","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":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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"reinforcement":[1],"learning":[2],"(RL)":[3],"has":[4],"significantly":[5],"advanced":[6],"reasoning":[7,64,79,149],"capabilities":[8],"in":[9,80,147],"large":[10],"multimodal":[11],"language":[12],"models":[13,21,50,82],"(MLLMs),":[14],"its":[15],"efficacy":[16],"remains":[17],"limited":[18],"for":[19,23,167],"lightweight":[20,49,81],"essential":[22],"edge":[24],"deployments.":[25],"To":[26],"address":[27],"this":[28,68],"issue,":[29],"we":[30,70],"leverage":[31],"causal":[32],"analysis":[33],"and":[34,159,180,204],"experiment":[35],"to":[36,51,99,121],"reveal":[37],"the":[38,89,105,113,127,164,191],"underlying":[39],"phenomenon":[40],"of":[41,163],"perceptual":[42,54],"bias,":[43],"demonstrating":[44],"that":[45,76],"RL-based":[46],"fine-tuning":[47],"compels":[48],"preferentially":[52],"adopt":[53],"shortcuts":[55],"induced":[56],"by":[57,67],"data":[58,166],"biases,":[59],"rather":[60],"than":[61],"developing":[62],"genuine":[63],"abilities.":[65],"Motivated":[66],"insight,":[69],"propose":[71],"VideoThinker,":[72],"a":[73,84,95,144,173,181,199,205],"causal-inspired":[74],"framework":[75],"cultivates":[77],"robust":[78],"through":[83],"two-stage":[85],"debiasing":[86],"process.":[87],"First,":[88],"Bias":[90],"Aware":[91],"Training":[92],"stage":[93],"forges":[94],"dedicated":[96],"\"bias":[97],"model\"":[98],"embody":[100],"these":[101],"shortcut":[102],"behaviors.":[103],"Then,":[104],"Causal":[106],"Debiasing":[107],"Policy":[108],"Optimization":[109],"(CDPO)":[110],"algorithm":[111],"fine-tunes":[112],"primary":[114],"model,":[115,141],"employing":[116],"an":[117],"innovative":[118],"repulsive":[119],"objective":[120],"actively":[122],"push":[123],"it":[124,135,169,189],"away":[125],"from":[126],"bias":[128],"model's":[129],"flawed":[130],"logic":[131],"while":[132],"simultaneously":[133],"pulling":[134],"toward":[136],"correct,":[137],"generalizable":[138],"solutions.":[139],"Our":[140],"VideoThinker-R1,":[142],"establishes":[143],"new":[145],"state-of-the-art":[146],"video":[148],"efficiency.":[150],"For":[151,186],"same-scale":[152],"comparison,":[153,188],"requiring":[154],"no":[155],"Supervised":[156],"Fine-Tuning":[157],"(SFT)":[158],"using":[160],"only":[161],"1":[162],"training":[165],"RL,":[168],"surpasses":[170],"VideoRFT-3B":[171],"with":[172],"3.2%":[174],"average":[175],"gain":[176,201,207],"on":[177,184,195,202,208],"widely-used":[178],"benchmarks":[179],"7%":[182],"lead":[183],"VideoMME.":[185],"cross-scale":[187],"outperforms":[190],"larger":[192],"Video-UTR-7B":[193],"model":[194],"multiple":[196],"benchmarks,":[197],"including":[198],"2.1%":[200],"MVBench":[203],"3.8%":[206],"TempCompass.":[209],"Code":[210],"is":[211],"available":[212],"at":[213],"https://github.com/falonss703/VideoThinker.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
