{"id":"https://openalex.org/W7161818961","doi":"https://doi.org/10.48550/arxiv.2605.18800","title":"Theory-optimal Quantization Based on Flatness","display_name":"Theory-optimal Quantization Based on Flatness","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7161818961","doi":"https://doi.org/10.48550/arxiv.2605.18800"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18800","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.2605.18800","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038441327","display_name":"Xiusheng Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xiusheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136514393","display_name":"Zhe Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083479069","display_name":"Xuanwu Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Xuanwu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136555844","display_name":"Lu Wang","orcid":"https://orcid.org/0009-0008-9869-1255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029640741","display_name":"Yequan Wang","orcid":"https://orcid.org/0000-0001-7530-6125"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yequan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136583053","display_name":"Dong Li","orcid":"https://orcid.org/0000-0002-8680-0468"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136552689","display_name":"Emad Barsoum","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barsoum, Emad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136537975","display_name":"Kang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Kang","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/T10036","display_name":"Advanced Neural Network Applications","score":0.28299999237060547,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.28299999237060547,"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":0.23649999499320984,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.049400001764297485,"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/quantization","display_name":"Quantization (signal processing)","score":0.8029000163078308},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7069000005722046},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.5177000164985657},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3785000145435333},{"id":"https://openalex.org/keywords/flatness","display_name":"Flatness (cosmology)","score":0.36230000853538513},{"id":"https://openalex.org/keywords/diagonally-dominant-matrix","display_name":"Diagonally dominant matrix","score":0.31220000982284546},{"id":"https://openalex.org/keywords/diagonal-matrix","display_name":"Diagonal matrix","score":0.2922999858856201}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8029000163078308},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7069000005722046},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5698999762535095},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.5177000164985657},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4523000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4336000084877014},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C2778530986","wikidata":"https://www.wikidata.org/wiki/Q5457948","display_name":"Flatness (cosmology)","level":3,"score":0.36230000853538513},{"id":"https://openalex.org/C84507854","wikidata":"https://www.wikidata.org/wiki/Q952222","display_name":"Diagonally dominant matrix","level":3,"score":0.31220000982284546},{"id":"https://openalex.org/C113313756","wikidata":"https://www.wikidata.org/wiki/Q332791","display_name":"Diagonal matrix","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C5317259","wikidata":"https://www.wikidata.org/wiki/Q4462361","display_name":"Trellis quantization","level":5,"score":0.2696000039577484},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C19444555","wikidata":"https://www.wikidata.org/wiki/Q212750","display_name":"Flattening","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C2986395286","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy inference","level":5,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18800","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.2605.18800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18800","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":{"Post-training":[0],"quantization":[1,25,81,124,155,166],"has":[2],"emerged":[3],"as":[4],"a":[5,88,121,153],"widely":[6],"adopted":[7],"technique":[8],"for":[9],"compressing":[10],"and":[11,60,83,85],"accelerating":[12],"the":[13,57,77,94,103,168,172,183,189],"inference":[14],"of":[15,96],"Large":[16],"Language":[17],"Models":[18],"(LLMs).":[19],"The":[20],"primary":[21],"challenges":[22],"in":[23,164],"LLMs":[24],"stem":[26],"from":[27],"activation":[28],"outliers,":[29,84],"which":[30],"significantly":[31],"degrade":[32],"model":[33,76],"performance":[34,184],"especially":[35],"at":[36],"lower":[37],"bit":[38],"precision.":[39],"While":[40],"recent":[41],"approaches":[42],"attempt":[43],"to":[44,92,109,178],"mitigate":[45],"outliers":[46],"through":[47,131],"linear":[48],"transformations":[49],"across":[50,140],"feature":[51],"dimensions,":[52],"our":[53],"analysis":[54],"reveals":[55],"that":[56,126,150],"transformed":[58],"weights":[59],"activations":[61],"still":[62],"exhibit":[63],"persistent":[64],"outlier":[65,129,138],"patterns":[66,130],"with":[67,107],"concentrated":[68],"magnitude":[69],"distributions.":[70],"In":[71,171],"this":[72],"paper,":[73],"we":[74,101,115],"first":[75],"mathematical":[78],"relationship":[79],"between":[80],"error":[82],"then":[86],"introduce":[87],"new":[89,154],"metric":[90],"Flatness":[91],"quantify":[93],"distribution":[95],"outliers.":[97],"Based":[98],"on":[99,112,167,188],"this,":[100],"derive":[102],"theoretical":[104],"optimal":[105],"solution":[106],"respect":[108],"Flatness.":[110],"Building":[111],"these":[113],"insights,":[114],"propose":[116],"Bidirectional":[117],"Diagonal":[118],"Quantization":[119],"(BDQ),":[120],"novel":[122],"post-training":[123],"framework":[125],"effectively":[127],"disperses":[128],"optimized":[132],"matrix":[133,141],"transformations.":[134],"BDQ":[135,151,181],"strategically":[136],"distributes":[137],"magnitudes":[139],"dimensions":[142],"via":[143],"learned":[144],"diagonal":[145],"operations.":[146],"Extensive":[147],"experiments":[148],"demonstrate":[149],"establishes":[152],"benchmark.":[156],"It":[157],"achieves":[158],"less":[159],"than":[160],"1\\%":[161],"accuracy":[162],"drop":[163],"W4A4":[165],"LLaMA-3-8B":[169],"model.":[170,191],"more":[173],"challenging":[174],"W2A4KV16":[175],"experiment,":[176],"compared":[177],"state-of-the-art":[179],"approaches,":[180],"reduces":[182],"gap":[185],"by":[186],"39.1\\%":[187],"DeepSeek-R1-Distill-LLaMA-70B":[190]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-21T00:00:00"}