{"id":"https://openalex.org/W7160943422","doi":"https://doi.org/10.48550/arxiv.2605.09719","title":"Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT","display_name":"Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160943422","doi":"https://doi.org/10.48550/arxiv.2605.09719"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09719","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":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.2605.09719","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031435979","display_name":"Alaa Asfour","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Asfour, Alaa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092513671","display_name":"Christopher Indris","orcid":"https://orcid.org/0009-0003-3327-0218"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Indris, Christopher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052213006","display_name":"Leihan Chen","orcid":"https://orcid.org/0000-0003-0887-7811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Leihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135977087","display_name":"Tejas Vyas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vyas, Tejas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135920399","display_name":"Guanghui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guanghui","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":0.9569000005722046,"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":0.9569000005722046,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.012000000104308128,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.008700000122189522,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spatial-intelligence","display_name":"Spatial intelligence","score":0.6991000175476074},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6474999785423279},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6309999823570251},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4966000020503998},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48809999227523804},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.3276999890804291},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.32679998874664307},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.3156999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975999712944031},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.6991000175476074},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6474999785423279},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6309999823570251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5159000158309937},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4966000020503998},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2500999867916107},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09719","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.09719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09719","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":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":{"Large-scale":[0],"3D":[1,112,147],"vision-language":[2],"models":[3],"(VLMs)":[4],"like":[5],"LLaVA-3D":[6],"offer":[7],"strong":[8,132],"spatial":[9,29,119,133],"reasoning":[10,30,80,109],"but":[11],"are":[12],"difficult":[13],"to":[14,17,35],"deploy":[15],"due":[16],"high":[18],"computational":[19],"costs.":[20],"We":[21],"propose":[22],"a":[23,32,36,48,70],"knowledge":[24],"distillation":[25,72],"framework":[26,62,144],"that":[27,92],"transfers":[28],"from":[31],"7B":[33],"teacher":[34],"2.29B":[37],"student":[38,115],"model.":[39],"Our":[40,143],"approach":[41],"achieves":[42],"8.7x":[43],"lower":[44],"inference":[45],"latency":[46],"and":[47,69,123,129,140],"3x":[49],"reduction":[50],"in":[51,110],"model":[52,116],"size":[53],"while":[54],"retaining":[55],"54-72%":[56],"of":[57,106],"the":[58,66,103],"teacher's":[59],"performance.":[60],"The":[61,114],"utilizes":[63],"VGGT":[64],"as":[65,94],"vision":[67],"encoder":[68],"multi-task":[71],"pipeline":[73],"with":[74],"uncertainty-aware":[75],"loss":[76],"weighting.":[77],"To":[78],"improve":[79],"without":[81],"chain-of-thought":[82],"(CoT)":[83],"data,":[84],"we":[85],"introduce":[86],"\"Hidden":[87],"CoT\":":[88],"learnable":[89],"latent":[90,107],"tokens":[91],"serve":[93],"an":[95],"internal":[96],"scratchpad":[97,108],"before":[98],"answer":[99],"generation.":[100],"This":[101],"is":[102],"first":[104],"use":[105],"distilled":[111],"VLMs.":[113],"jointly":[117],"performs":[118],"description,":[120],"depth":[121],"estimation,":[122],"object":[124],"detection.":[125],"Experiments":[126],"on":[127,138,150],"ScanNet":[128],"3D-FRONT":[130],"show":[131],"understanding,":[134],"reaching":[135],"68-72%":[136],"accuracy":[137],"proximity":[139],"contact":[141],"tasks.":[142],"enables":[145],"efficient":[146],"scene":[148],"QA":[149],"resource-constrained":[151],"platforms.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}