{"id":"https://openalex.org/W7163209605","doi":"https://doi.org/10.48550/arxiv.2606.01182","title":"CA-BED: Conversation-Aware Bayesian Experimental Design","display_name":"CA-BED: Conversation-Aware Bayesian Experimental Design","publication_year":2026,"publication_date":"2026-05-31","ids":{"openalex":"https://openalex.org/W7163209605","doi":"https://doi.org/10.48550/arxiv.2606.01182"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01182","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.2606.01182","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137618495","display_name":"Daniel Arnould","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arnould, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137667202","display_name":"Rashad Aziz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aziz, Rashad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137712067","display_name":"Zixuan Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137637198","display_name":"Tanav Changal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Changal, Tanav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137646122","display_name":"Kevin Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Kevin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137659993","display_name":"Sunishchal Dev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dev, Sunishchal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082051197","display_name":"Gabriel Grand","orcid":"https://orcid.org/0000-0003-1920-0021"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grand, Gabriel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070375308","display_name":"Shreyas Sunil Kulkarni","orcid":"https://orcid.org/0000-0002-0427-3837"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kulkarni, Shreyas Sunil","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/T10028","display_name":"Topic Modeling","score":0.6912000179290771,"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":0.6912000179290771,"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/T12031","display_name":"Speech and dialogue systems","score":0.12559999525547028,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.02280000038444996,"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":0.5810999870300293},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5648999810218811},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5626999735832214},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.5508000254631042},{"id":"https://openalex.org/keywords/dialog-box","display_name":"Dialog box","score":0.5378999710083008},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4616999924182892},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4593000113964081},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.44519999623298645},{"id":"https://openalex.org/keywords/bayesian-experimental-design","display_name":"Bayesian experimental design","score":0.384799987077713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739799976348877},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5810999870300293},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5648999810218811},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5626999735832214},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.5508000254631042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455999970436096},{"id":"https://openalex.org/C173853756","wikidata":"https://www.wikidata.org/wiki/Q86915","display_name":"Dialog box","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4702000021934509},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44519999623298645},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.384799987077713},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.32179999351501465},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.32030001282691956},{"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/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C2983203078","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Information gain","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C75455068","wikidata":"https://www.wikidata.org/wiki/Q1455566","display_name":"Frequentist probability","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C2983703474","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Probability estimation","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C205706631","wikidata":"https://www.wikidata.org/wiki/Q2319304","display_name":"Expected utility hypothesis","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01182","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.2606.01182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01182","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":[{"id":"https://metadata.un.org/sdg/4","score":0.43719804286956787,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"excel":[4],"at":[5],"static":[6],"reasoning":[7],"tasks,":[8],"yet":[9],"their":[10],"performance":[11],"often":[12],"degrades":[13],"in":[14,29,110],"interactive":[15],"scenarios":[16],"where":[17],"information":[18,92],"must":[19],"be":[20,40],"actively":[21],"acquired":[22],"through":[23,94],"questioning.":[24],"A":[25],"key":[26],"challenge":[27],"lies":[28],"selecting":[30],"questions":[31],"that":[32,38,62],"reduce":[33],"uncertainty":[34],"while":[35],"incorporating":[36],"responses":[37],"may":[39],"ambiguous":[41],"or":[42],"only":[43,133],"partially":[44],"informative.":[45],"To":[46],"address":[47],"this,":[48],"we":[49],"propose":[50],"Conversation-Aware":[51],"Bayesian":[52,64],"Experimental":[53,65],"Design":[54,66],"(CA-BED),":[55],"an":[56,106,129],"inference-time":[57],"probabilistic":[58],"dialog":[59],"planning":[60],"framework":[61],"integrates":[63],"with":[67,116,128],"LLM-based":[68],"likelihood":[69],"estimation":[70],"to":[71,120,138],"optimize":[72],"question":[73],"selection":[74],"over":[75,84,113],"multiple":[76],"conversational":[77,135],"turns.":[78],"CA-BED":[79,104],"maintains":[80],"a":[81,95],"belief":[82],"distribution":[83],"hypotheses,":[85],"anticipates":[86],"possible":[87],"answers,":[88],"and":[89],"propagates":[90],"expected":[91],"gain":[93],"simulated":[96],"conversation":[97],"tree.":[98],"Across":[99],"two":[100],"structured":[101],"entity-deduction":[102],"benchmarks,":[103],"yields":[105],"average":[107,130],"21.8%":[108],"improvement":[109],"success":[111],"rates":[112],"direct":[114,139],"prompting,":[115],"comparable":[117],"gains":[118,127],"relative":[119],"alternative":[121],"information-seeking":[122],"methods.":[123],"It":[124],"achieves":[125],"these":[126],"increase":[131],"of":[132],"1.8":[134],"turns":[136],"compared":[137],"prompting.":[140]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
