{"id":"https://openalex.org/W7164406690","doi":"https://doi.org/10.48550/arxiv.2606.11201","title":"To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending","display_name":"To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7164406690","doi":"https://doi.org/10.48550/arxiv.2606.11201"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11201","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":"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.2606.11201","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138426540","display_name":"Jin Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138409222","display_name":"Xin Li (51274)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138460035","display_name":"Jun Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jun","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/T13702","display_name":"Machine Learning in Healthcare","score":0.2540000081062317,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.2540000081062317,"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":0.15520000457763672,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12729999423027039,"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/software-deployment","display_name":"Software deployment","score":0.6575999855995178},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.642799973487854},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6247000098228455},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.508400022983551},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.44999998807907104},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3763999938964844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228999733924866},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6575999855995178},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.642799973487854},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6247000098228455},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.508400022983551},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.48660001158714294},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3578999936580658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32589998841285706},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11201","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":"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.2606.11201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11201","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":"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":{"The":[0],"wide":[1],"deployment":[2],"of":[3],"LLMs":[4],"has":[5],"made":[6],"model":[7,164],"alignment":[8,26,99,117,121],"necessary":[9],"to":[10,19,70,106,158],"make":[11,86],"newly":[12],"trained":[13],"models":[14,48],"safely":[15],"and":[16,73,90,122,148,156],"effectively":[17],"respond":[18],"user":[20],"instructions.":[21],"Among":[22],"different":[23],"methods,":[24],"inference-time":[25,98,116],"is":[27,168],"often":[28],"cheaper":[29],"as":[30],"it":[31,135],"intervenes":[32],"(i.e.,":[33],"offers":[34],"guidances)":[35],"only":[36],"during":[37],"output":[38],"generation.":[39],"Existing":[40],"proposals":[41],"apply":[42],"guidances":[43,68],"extracted":[44],"from":[45,103],"certain":[46],"aligned":[47],"without":[49],"properly":[50],"assessing":[51],"their":[52],"reliability.":[53,130],"Nonetheless,":[54],"our":[55],"systematic":[56],"evaluation":[57],"reveals":[58],"that":[59,101],"guidance":[60,138],"effectiveness":[61],"varies":[62],"drastically":[63],"across":[64],"models;":[65],"since":[66],"ineffective":[67],"lead":[69],"further":[71,75],"confusion":[72],"thus":[74,91],"interventions,":[76],"the":[77],"resulting":[78],"excessive":[79],"interventions":[80,87],"typically":[81],"indicate":[82],"poor":[83],"performance.":[84],"To":[85],"more":[88,92],"effective":[89],"efficient,":[93],"we":[94],"introduce":[95],"BlendIn,":[96],"an":[97],"framework":[100],"shifts":[102],"binary":[104],"decisions":[105],"creating":[107],"hybrid":[108],"distributions":[109],"integrating":[110],"both":[111,145],"models'":[112],"knowledge.":[113],"BlendIn":[114,143],"stabilizes":[115],"by":[118],"performing":[119],"quality-aware":[120],"proportionally":[123],"weighting":[124],"each":[125],"model's":[126],"contribution":[127],"based":[128],"on":[129,162],"Compared":[131],"with":[132],"existing":[133],"works,":[134],"preserves":[136],"beneficial":[137],"while":[139],"downweighting":[140],"unreliable":[141],"suggestions.":[142],"provides":[144],"diagnostic":[146],"signals":[147],"mitigation":[149],"strategies":[150],"for":[151],"misaligned":[152],"guidance,":[153],"achieving":[154],"consistent":[155],"up":[157],"50%":[159],"performance":[160],"improvement":[161],"challenging":[163],"pairs.":[165],"Our":[166],"code":[167],"available":[169],"at:":[170],"https://github.com/DecayingSeart/BlendIn.":[171]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-12T00:00:00"}
