{"id":"https://openalex.org/W7163710790","doi":"https://doi.org/10.48550/arxiv.2606.05429","title":"Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models","display_name":"Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163710790","doi":"https://doi.org/10.48550/arxiv.2606.05429"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05429","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05429","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.2606.05429","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089529142","display_name":"Rayyan Abdalla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdalla, Rayyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014592910","display_name":"Amir Hussein","orcid":"https://orcid.org/0000-0002-0820-4062"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hussein, Amir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137920558","display_name":"Min Wu (41570)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137941133","display_name":"Dinesh Manocha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manocha, Dinesh","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":0.4300999939441681,"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":0.4300999939441681,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11110000312328339,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.10980000346899033,"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":0.7638000249862671},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.7005000114440918},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.66839998960495},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5806000232696533},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5142999887466431},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.478300005197525},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.32760000228881836}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7638000249862671},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.7005000114440918},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.66839998960495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347000002861023},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5806000232696533},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5142999887466431},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36550000309944153},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2761000096797943},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.26269999146461487},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05429","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05429","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.2606.05429","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05429","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":[1,41],"(PTQ)":[2],"is":[3],"critical":[4],"for":[5,43,100,155],"the":[6,70,117],"efficient":[7],"deployment":[8],"of":[9,73,162],"large":[10],"language":[11],"models":[12,60],"(LLMs).":[13],"Recent":[14],"ultra-low-bit":[15,40],"PTQ":[16],"methods":[17,139],"rely":[18],"on":[19,135,173],"rigid":[20],"weight-saliency":[21],"assumptions":[22],"or":[23],"position":[24],"heuristics,":[25],"introducing":[26],"substantial":[27],"hidden":[28,47],"scaling":[29,48,93,131],"overhead.":[30],"We":[31],"propose":[32],"SAGE-PTQ":[33,50,77,95,110,123,147,168],"(Saliency-Aware":[34],"Graph-guided":[35],"Efficient":[36],"PTQ),":[37],"a":[38,65],"novel":[39],"framework":[42],"LLMs":[44],"that":[45],"minimizes":[46],"cost.":[49],"separates":[51],"salient":[52,85,101],"and":[53,87,103,128,143],"unsalient":[54,62,89,107],"weights":[55,63,86,102],"using":[56,158],"distributional":[57],"statistics,":[58],"then":[59],"subsampled":[61],"as":[64,141],"sparse":[66],"graph":[67],"to":[68,84,115,153],"estimate":[69],"optimal":[71,118],"number":[72],"groups":[74],"per":[75,106,121,133],"layer.":[76],"applies":[78],"dual-mode":[79],"quantization,":[80],"assigning":[81],"multi-bit":[82],"precision":[83],"binarizing":[88],"weights.":[90],"To":[91],"reduce":[92],"overhead,":[94],"uses":[96],"one":[97,104,174],"per-channel":[98],"scale":[99],"scalar":[105],"group.":[108],"Finally,":[109],"implements":[111],"adaptive":[112],"saliency":[113,119],"thresholding":[114],"select":[116],"ratio":[120],"matrix.":[122],"achieves":[124,148],"1.03":[125],"weight":[126],"bits":[127,132],"only":[129],"0.004":[130],"matrix":[134],"average,":[136],"outperforming":[137],"state-of-the-art":[138],"such":[140],"BiLLM":[142],"PB-LLM.":[144],"On":[145,166],"LLaMA-3-8B,":[146],"6.74":[149],"WikiText2":[150],"perplexity,":[151],"compared":[152],"55.8":[154],"BiLLM,":[156],"while":[157],"less":[159],"than":[160],"50%":[161],"BiLLM's":[163],"GPU":[164],"memory.":[165],"LLaMA-2-70B,":[167],"provides":[169],"1.5x":[170],"faster":[171],"decoding":[172],"NVIDIA":[175],"L40":[176],"GPU,":[177],"demonstrating":[178],"practical":[179],"inference":[180],"efficiency.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}