{"id":"https://openalex.org/W7155013761","doi":"https://doi.org/10.48550/arxiv.2604.15495","title":"GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology","display_name":"GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7155013761","doi":"https://doi.org/10.48550/arxiv.2604.15495"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.15495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15495","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.2604.15495","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086680795","display_name":"Shivendra Agrawal","orcid":"https://orcid.org/0000-0003-1425-9892"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Shivendra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134048688","display_name":"Bradley Hayes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hayes, Bradley","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":0.6176999807357788,"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":0.6176999807357788,"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/T11904","display_name":"Spatial Cognition and Navigation","score":0.12809999287128448,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.07050000131130219,"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/floor-plan","display_name":"Floor plan","score":0.6154999732971191},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5001000165939331},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.4779999852180481},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.45809999108314514},{"id":"https://openalex.org/keywords/semantic-mapping","display_name":"Semantic mapping","score":0.44110000133514404},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.36739999055862427},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.36079999804496765},{"id":"https://openalex.org/keywords/semantic-network","display_name":"Semantic network","score":0.3490999937057495},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.34880000352859497},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.3456000089645386}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8027999997138977},{"id":"https://openalex.org/C61056293","wikidata":"https://www.wikidata.org/wiki/Q18965","display_name":"Floor plan","level":2,"score":0.6154999732971191},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.45809999108314514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4458000063896179},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.34880000352859497},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.34540000557899475},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.33559998869895935},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.30090001225471497},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3000999987125397},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C136085584","wikidata":"https://www.wikidata.org/wiki/Q910289","display_name":"Overlay","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.26489999890327454},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.26010000705718994},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C103692084","wikidata":"https://www.wikidata.org/wiki/Q1765824","display_name":"Semantic grid","level":3,"score":0.259799987077713},{"id":"https://openalex.org/C74072328","wikidata":"https://www.wikidata.org/wiki/Q1142726","display_name":"Intelligent agent","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25440001487731934},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.15495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15495","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.2604.15495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15495","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Navigating":[0],"complex,":[1],"densely":[2],"packed":[3],"environments":[4],"like":[5],"retail":[6],"stores,":[7],"warehouses,":[8],"and":[9,19,37,103,112,141,175],"hospitals":[10],"poses":[11],"a":[12,71,78,84,95,105,148,153,161,177],"significant":[13],"spatial":[14,59,122],"grounding":[15,60],"challenge":[16,41],"for":[17,222],"humans":[18],"embodied":[20],"AI.":[21],"In":[22,191],"these":[23],"spaces,":[24,54],"dense":[25],"visual":[26],"features":[27],"quickly":[28],"become":[29],"stale":[30],"given":[31],"the":[32,92,117,167,219],"quasi-static":[33],"nature":[34],"of":[35,119],"items,":[36],"long-tail":[38],"semantic":[39,107,113,173],"distributions":[40],"traditional":[42],"computer":[43],"vision.":[44],"While":[45],"Vision-Language":[46],"Models":[47],"(VLMs)":[48],"help":[49],"assistive":[50],"systems":[51],"navigate":[52],"semantically-rich":[53],"they":[55],"still":[56],"struggle":[57],"with":[58],"in":[61],"cluttered":[62],"environments.":[63],"We":[64,115],"present":[65],"GIST":[66,195],"(Grounded":[67],"Intelligent":[68],"Semantic":[69,133,150],"Topology),":[70],"multimodal":[72],"knowledge":[73,123],"extraction":[74],"pipeline":[75],"that":[76,136,165,181],"transforms":[77],"consumer-grade":[79],"mobile":[80],"point":[81],"cloud":[82],"into":[83,94,171,185],"semantically":[85],"annotated":[86],"navigation":[87,210],"topology.":[88],"Our":[89],"architecture":[90],"distills":[91],"scene":[93],"2D":[96],"occupancy":[97],"map,":[98],"extracts":[99],"its":[100],"topological":[101],"layout,":[102],"overlays":[104],"lightweight":[106],"layer":[108],"via":[109],"intelligent":[110],"keyframe":[111],"selection.":[114],"demonstrate":[116],"versatility":[118],"this":[120],"structured":[121],"through":[124],"critical":[125],"downstream":[126],"Human-AI":[127],"interaction":[128],"tasks:":[129],"(1)":[130],"an":[131,202,208],"intent-driven":[132],"Search":[134],"engine":[135],"actively":[137],"infers":[138],"categorical":[139],"alternatives":[140],"zones":[142],"when":[143],"exact":[144],"matches":[145],"fail;":[146],"(2)":[147],"one-shot":[149],"Localizer":[151],"achieving":[152],"1.04":[154],"m":[155],"top-5":[156],"mean":[157],"translation":[158],"error;":[159],"(3)":[160],"Zone":[162],"Classification":[163],"module":[164],"segments":[166],"walkable":[168],"floor":[169],"plan":[170],"high-level":[172],"regions;":[174],"(4)":[176],"Visually-Grounded":[178],"Instruction":[179],"Generator":[180],"synthesizes":[182],"optimal":[183],"paths":[184],"egocentric,":[186],"landmark-rich":[187],"natural":[188],"language":[189],"routing.":[190],"multi-criteria":[192],"LLM":[193],"evaluations,":[194],"outperforms":[196],"sequence-based":[197],"instruction":[198],"generation":[199],"baselines.":[200],"Finally,":[201],"in-situ":[203],"formative":[204],"evaluation":[205],"(N=5)":[206],"yields":[207],"80%":[209],"success":[211],"rate":[212],"relying":[213],"solely":[214],"on":[215],"verbal":[216],"cues,":[217],"validating":[218],"system's":[220],"capacity":[221],"universal":[223],"design.":[224]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-21T00:00:00"}