{"id":"https://openalex.org/W7131861627","doi":"https://doi.org/10.48550/arxiv.2602.22271","title":"Support Tokens, Stability Margins, and a New Foundation for Robust LLMs","display_name":"Support Tokens, Stability Margins, and a New Foundation for Robust LLMs","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131861627","doi":"https://doi.org/10.48550/arxiv.2602.22271"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.22271","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101736089","display_name":"Deepak Agarwal","orcid":"https://orcid.org/0000-0003-2590-2206"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Agarwal, Deepak","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120732369","display_name":"Dhyey Dharmendrakumar Mavani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mavani, Dhyey Dharmendrakumar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127326753","display_name":"Suyash Gupta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Suyash","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127087348","display_name":"Karthik Sethuraman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sethuraman, Karthik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127386276","display_name":"Tejas Dharamsi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dharamsi, Tejas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101736089"],"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/T10028","display_name":"Topic Modeling","score":0.23720000684261322,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.23720000684261322,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.1071000024676323,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09950000047683716,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7807999849319458},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.7114999890327454},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6341000199317932},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5720000267028809},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5421000123023987},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.519599974155426},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.46939998865127563},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.447299987077713}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7807999849319458},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.7114999890327454},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6341000199317932},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5306000113487244},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.519599974155426},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.46939998865127563},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.44119998812675476},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3797999918460846},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37880000472068787},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33820000290870667},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.31470000743865967},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.267300009727478},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26440000534057617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2639999985694885},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2596000134944916}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.22271","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.22271","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.22271","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.22271","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Self-attention":[0],"is":[1,37],"usually":[2],"described":[3],"as":[4,34],"a":[5,11,30,45,55,68,79,109,118,131,139,147],"flexible,":[6],"content-adaptive":[7],"way":[8],"to":[9,39,83,96,142,151,163],"mix":[10],"token":[12,115],"with":[13],"information":[14],"from":[15],"its":[16],"past.":[17],"We":[18,102],"reinterpret":[19],"causal":[20,106],"self-attention":[21],"transformers,":[22],"the":[23,50,60,72,84,97,152,157,168,172],"backbone":[24],"of":[25,49,53,62,99,171],"modern":[26],"foundation":[27,121],"models,":[28],"within":[29],"probabilistic":[31,40,120],"framework,":[32],"much":[33],"classical":[35],"PCA":[36],"extended":[38],"PCA.":[41],"This":[42],"reformulation":[43],"reveals":[44],"key":[46],"structural":[47],"consequence":[48],"underlying":[51],"change":[52],"variables:":[54],"barrier":[56],"constraint":[57],"emerges":[58],"on":[59,126],"parameters":[61],"self-attention.":[63],"The":[64],"resulting":[65,158],"geometry":[66,170],"exposes":[67],"degeneracy":[69],"boundary":[70],"where":[71],"attention-induced":[73],"mapping":[74],"becomes":[75],"locally":[76],"ill-conditioned,":[77],"yielding":[78],"stability-margin":[80],"interpretation":[81],"analogous":[82],"margin":[85,169],"in":[86,91],"support":[87,100],"vector":[88],"machines.":[89],"This,":[90],"turn,":[92],"naturally":[93],"gives":[94],"rise":[95],"concept":[98],"tokens.":[101],"further":[103],"show":[104],"that":[105,136],"transformers":[107],"define":[108],"consistent":[110],"stochastic":[111],"process":[112],"over":[113],"infinite":[114],"sequences,":[116],"providing":[117],"rigorous":[119],"for":[122],"sequence":[123],"modeling.":[124],"Building":[125],"this":[127],"view,":[128],"we":[129],"derive":[130],"Bayesian":[132],"MAP":[133],"training":[134,159],"objective":[135,160],"requires":[137],"only":[138],"minimal":[140],"modification":[141],"standard":[143],"LLM":[144],"training:":[145],"adding":[146],"smooth":[148],"log-barrier":[149],"penalty":[150],"usual":[153],"cross-entropy":[154],"loss.":[155],"Empirically,":[156],"improves":[161],"robustness":[162],"input":[164],"perturbations":[165],"and":[166],"sharpens":[167],"learned":[173],"representations":[174],"without":[175],"sacrificing":[176],"out-of-sample":[177],"accuracy.":[178]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-28T00:00:00"}
