{"id":"https://openalex.org/W7168276596","doi":"https://doi.org/10.48550/arxiv.2607.10640","title":"Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models","display_name":"Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models","publication_year":2026,"publication_date":"2026-07-12","ids":{"openalex":"https://openalex.org/W7168276596","doi":"https://doi.org/10.48550/arxiv.2607.10640"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10640","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.2607.10640","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140719324","display_name":"Zhaoyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140653349","display_name":"Yanjun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yanjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140601693","display_name":"Wangkai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wangkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140631097","display_name":"Yujia Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yujia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140648723","display_name":"Tianzhu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tianzhu","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":0.9271000027656555,"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":0.9271000027656555,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.02370000071823597,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.010200000368058681,"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/pruning","display_name":"Pruning","score":0.775600016117096},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5383999943733215},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4927999973297119},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46790000796318054},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45840001106262207},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4194999933242798},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.38830000162124634}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.775600016117096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6173999905586243},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5383999943733215},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.510200023651123},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45840001106262207},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C2985596519","wikidata":"https://www.wikidata.org/wiki/Q179635","display_name":"Heat flow","level":3,"score":0.3181999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2985999882221222},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2851000130176544},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27379998564720154},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10640","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.2607.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10640","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":[{"score":0.5419038534164429,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language":[0],"Models":[1],"(VLMs)":[2],"are":[3],"costly":[4],"at":[5,158],"inference":[6],"time":[7],"because":[8],"they":[9],"must":[10],"process":[11],"long":[12],"sequences":[13],"of":[14,141,147],"visual":[15,63,148],"tokens.":[16],"Existing":[17],"token":[18],"pruning":[19,48,133,145],"methods":[20,127],"often":[21],"degrade":[22],"under":[23],"high":[24],"compression":[25],"by":[26],"blindly":[27],"discarding":[28],"information,":[29],"breaking":[30],"spatial":[31,54,91],"structure":[32],"or":[33],"collapsing":[34],"diversity.":[35],"We":[36],"propose":[37],"SpecFlow,":[38],"a":[39,68,74],"training-free":[40],"framework":[41],"that":[42,122],"shifts":[43],"the":[44],"paradigm":[45],"from":[46],"destructive":[47],"to":[49,59,82,93,104],"conservative":[50],"condensation,":[51],"strictly":[52],"enforcing":[53],"coverage":[55],"and":[56,96,115,132],"statistical":[57,106],"conservation":[58],"ensure":[60],"stability.":[61],"Treating":[62],"tokens":[64],"as":[65],"nodes":[66],"in":[67],"$k$NN":[69],"graph,":[70],"SpecFlow":[71,124,138],"(i)":[72],"computes":[73],"stable":[75],"importance":[76],"field":[77],"via":[78,89],"spectral":[79],"heat":[80],"flow":[81],"preserve":[83],"structural":[84],"coherence,":[85],"(ii)":[86],"allocates":[87],"budgets":[88],"adaptive":[90],"partitioning":[92],"guarantee":[94],"coverage,":[95],"(iii)":[97],"aggregates":[98],"discarded":[99],"information":[100],"into":[101],"coreset":[102],"sinks":[103],"maintain":[105],"conservation.":[107],"The":[108],"method":[109],"is":[110,116,156],"plug-and-play,":[111],"requires":[112],"no":[113],"fine-tuning,":[114],"compatible":[117],"with":[118,137],"FlashAttention.":[119],"Experiments":[120],"confirm":[121],"our":[123],"outperforms":[125],"SOTA":[126],"across":[128],"tasks,":[129],"VLM":[130],"architectures,":[131],"ratios.":[134],"Notably,":[135],"LLaVA-1.5":[136],"retains":[139],"95.6%":[140],"original":[142],"performance":[143],"despite":[144],"88.9%":[146],"tokens,":[149],"offering":[150],"an":[151],"exceptional":[152],"efficiency-accuracy":[153],"balance.":[154],"Code":[155],"available":[157],"https://github.com/Lzy-dot/SpecFlow":[159]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}
