{"id":"https://openalex.org/W7143381394","doi":"https://doi.org/10.48550/arxiv.2603.25841","title":"GazeQwen: Lightweight Gaze-Conditioned LLM Modulation for Streaming Video Understanding","display_name":"GazeQwen: Lightweight Gaze-Conditioned LLM Modulation for Streaming Video Understanding","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7143381394","doi":"https://doi.org/10.48550/arxiv.2603.25841"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25841","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.2603.25841","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104277382","display_name":"Trong Thang Pham","orcid":"https://orcid.org/0000-0003-3170-4142"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pham, Trong Thang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130984767","display_name":"Hien Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Hien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5131000295","display_name":"Ngan Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Ngan","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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.7692000269889832,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.7692000269889832,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.07150000333786011,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.06780000030994415,"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/gaze","display_name":"Gaze","score":0.839900016784668},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5157999992370605},{"id":"https://openalex.org/keywords/overlay","display_name":"Overlay","score":0.4763000011444092},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.41100001335144043},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4016000032424927},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.3889999985694885},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3822000026702881}],"concepts":[{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.839900016784668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652999758720398},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48660001158714294},{"id":"https://openalex.org/C136085584","wikidata":"https://www.wikidata.org/wiki/Q910289","display_name":"Overlay","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42399999499320984},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4223000109195709},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.35530000925064087},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.31529998779296875},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25841","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.2603.25841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25841","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":{"Current":[0],"multimodal":[1],"large":[2],"language":[3],"models":[4,135],"(MLLMs)":[5],"cannot":[6],"effectively":[7],"utilize":[8],"eye-gaze":[9],"information":[10],"for":[11,92],"video":[12,60],"understanding,":[13],"even":[14],"when":[15],"gaze":[16,39,50,118,145],"cues":[17],"are":[18,164],"supplied":[19],"via":[20,77],"visual":[21,120],"overlays":[22],"or":[23,156],"text":[24],"descriptions.":[25],"We":[26],"introduce":[27],"GazeQwen,":[28],"a":[29,48,108],"parameter":[30],"efficient":[31],"approach":[32],"that":[33,56,140],"equips":[34],"an":[35,147],"open-source":[36,132],"MLLM":[37],"with":[38,63,117],"awareness":[40],"through":[41],"hidden-state":[42],"modulation.":[43],"At":[44],"its":[45],"core":[46],"is":[47,149],"compact":[49],"resampler":[51],"(~1-5":[52],"M":[53],"trainable":[54],"parameters)":[55],"encodes":[57],"V-JEPA":[58],"2.1":[59],"features":[61],"together":[62],"fixation-derived":[64],"positional":[65],"encodings":[66],"and":[67,122,133,162],"produces":[68],"additive":[69],"residuals":[70],"injected":[71],"into":[72],"selected":[73],"LLM":[74,91,148],"decoder":[75],"layers":[76],"forward":[78],"hooks.":[79],"An":[80],"optional":[81],"second":[82],"training":[83],"stage":[84],"adds":[85],"low-rank":[86],"adapters":[87],"(LoRA)":[88],"to":[89,143],"the":[90,101,113,127],"tighter":[93],"integration.":[94],"Evaluated":[95],"on":[96],"all":[97,131],"10":[98],"tasks":[99],"of":[100],"StreamGaze":[102],"benchmark,":[103],"GazeQwen":[104],"reaches":[105],"63.9%":[106],"accuracy,":[107],"+16.1":[109],"point":[110],"gain":[111],"over":[112,125],"same":[114],"Qwen2.5-VL-7B":[115],"backbone":[116],"as":[119],"prompts":[121],"+10.5":[123],"points":[124],"GPT-4o,":[126],"highest":[128],"score":[129],"among":[130],"proprietary":[134],"tested.":[136],"These":[137],"results":[138],"suggest":[139],"learning":[141],"where":[142],"inject":[144],"within":[146],"more":[150],"effective":[151],"than":[152],"scaling":[153],"model":[154],"size":[155],"engineering":[157],"better":[158],"prompts.":[159],"All":[160],"code":[161],"checkpoints":[163],"available":[165],"at":[166],"https://github.com/phamtrongthang123/gazeqwen":[167],".":[168]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-31T00:00:00"}
