{"id":"https://openalex.org/W7163045816","doi":"https://doi.org/10.48550/arxiv.2605.31035","title":"MixFP4: Enhancing NVFP4 with Adaptive FP4/INT4 Block Representations","display_name":"MixFP4: Enhancing NVFP4 with Adaptive FP4/INT4 Block Representations","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163045816","doi":"https://doi.org/10.48550/arxiv.2605.31035"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31035","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.2605.31035","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016822729","display_name":"Jiaxiang Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Jiaxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102028283","display_name":"Yonghao Chen","orcid":"https://orcid.org/0009-0000-5744-5930"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yonghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137516358","display_name":"Ruilong Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Ruilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137599842","display_name":"Xinyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinyu","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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.35420000553131104,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.35420000553131104,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.22669999301433563,"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/T10028","display_name":"Topic Modeling","score":0.05079999938607216,"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/robustness","display_name":"Robustness (evolution)","score":0.5885999798774719},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.49239999055862427},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.4846999943256378},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.48339998722076416},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/unification","display_name":"Unification","score":0.3587999939918518},{"id":"https://openalex.org/keywords/datapath","display_name":"Datapath","score":0.3578999936580658},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.3416000008583069}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7968000173568726},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5885999798774719},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.4846999943256378},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4318000078201294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4059000015258789},{"id":"https://openalex.org/C96146094","wikidata":"https://www.wikidata.org/wiki/Q609057","display_name":"Unification","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C2781198647","wikidata":"https://www.wikidata.org/wiki/Q1633673","display_name":"Datapath","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2515999972820282},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C2778100165","wikidata":"https://www.wikidata.org/wiki/Q1589327","display_name":"Memory hierarchy","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31035","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.2605.31035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31035","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"large":[1],"language":[2],"models":[3],"continue":[4],"to":[5,53],"scale,":[6],"fine-grained":[7],"block-scaled":[8,42],"low-precision":[9],"formats":[10],"such":[11],"as":[12],"NVFP4":[13,54,119],"are":[14],"increasingly":[15],"adopted":[16],"for":[17],"their":[18],"substantial":[19],"throughput":[20],"and":[21,63,72,116],"memory":[22],"benefits.":[23],"However,":[24],"a":[25,49,97],"single":[26],"FP4":[27,60,113],"micro-format":[28,51],"often":[29],"mismatches":[30],"heterogeneous":[31],"block-level":[32],"tensor":[33],"statistics.":[34],"To":[35],"address":[36],"this":[37],"without":[38],"changing":[39],"the":[40,74,83,87],"standard":[41],"MMA/GEMM":[43],"execution":[44],"path,":[45],"we":[46],"propose":[47],"MixFP4,":[48],"mixed":[50],"extension":[52],"that":[55],"selects":[56],"between":[57],"two":[58],"stored":[59],"micro-formats":[61,95],"(E2M1":[62],"E1M2)":[64],"per":[65],"block.":[66],"MixFP4":[67,103,111],"reuses":[68],"NVFP4's":[69],"scale":[70],"hierarchy":[71],"encodes":[73],"format":[75],"choice":[76],"with":[77,121],"zero":[78],"additional":[79],"metadata":[80],"by":[81],"repurposing":[82],"sign":[84],"bit":[85],"of":[86],"FP8":[88],"E4M3":[89],"block":[90],"scale.":[91],"By":[92],"decoding":[93],"both":[94],"into":[96],"unified":[98],"internal":[99],"E2M2":[100],"compute":[101],"representation,":[102],"avoids":[104],"datapath":[105],"duplication.":[106],"Across":[107],"representative":[108],"LLM":[109],"families,":[110],"improves":[112],"quantization":[114],"robustness":[115],"accuracy":[117],"over":[118],"baselines":[120],"modest":[122],"tensor-core":[123],"overhead":[124],"(3.1\\%":[125],"area,":[126],"1.5\\%":[127],"power).":[128]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
