{"id":"https://openalex.org/W7138889528","doi":"https://doi.org/10.48550/arxiv.2603.15653","title":"Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context","display_name":"Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context","publication_year":2026,"publication_date":"2026-03-07","ids":{"openalex":"https://openalex.org/W7138889528","doi":"https://doi.org/10.48550/arxiv.2603.15653"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15653","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.2603.15653","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030482460","display_name":"Keivan Alizadeh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alizadeh, Keivan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130040901","display_name":"Parshin Shojaee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shojaee, Parshin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129806174","display_name":"Minsik Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Minsik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129887151","display_name":"Mehrdad Farajtabar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farajtabar, Mehrdad","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":0.376800000667572,"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":0.376800000667572,"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":0.22110000252723694,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0478999987244606,"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/recursion","display_name":"Recursion (computer science)","score":0.8464999794960022},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7314000129699707},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7156000137329102},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6384999752044678},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44279998540878296},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.42879998683929443},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.4162999987602234}],"concepts":[{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.8464999794960022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7699999809265137},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7314000129699707},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7156000137329102},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6384999752044678},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45829999446868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4514000117778778},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C137345334","wikidata":"https://www.wikidata.org/wiki/Q7303350","display_name":"Recursive partitioning","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.31189998984336853},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.31139999628067017},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C76188268","wikidata":"https://www.wikidata.org/wiki/Q1783165","display_name":"Context effect","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15653","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.2603.15653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15653","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":{"Long-context":[0],"handling":[1],"remains":[2],"a":[3,85,113,175,255],"core":[4],"challenge":[5,39],"for":[6,195],"language":[7],"models:":[8],"even":[9],"with":[10,92,203,234],"extended":[11],"context":[12,90,135,196],"windows,":[13],"models":[14],"often":[15,205],"fail":[16],"to":[17,122,149,209],"reliably":[18],"extract,":[19],"reason":[20],"over,":[21],"and":[22,82,104,117,124,137,174,221,244],"use":[23],"the":[24,57,118,155,167,199,210],"information":[25],"across":[26,131,218],"long":[27,45,222],"contexts.":[28,223],"Recent":[29],"works":[30],"like":[31],"Recursive":[32],"Language":[33],"Models":[34],"(RLM)":[35],"have":[36],"approached":[37],"this":[38,76,80],"by":[40],"agentic":[41],"way":[42],"of":[43,59,112,170],"decomposing":[44],"contexts":[46],"into":[47],"recursive":[48],"sub-calls":[49],"through":[50],"programmatic":[51,89],"interaction":[52,91],"at":[53],"inference.":[54],"While":[55],"promising,":[56],"success":[58],"RLM":[60,153,184,228],"critically":[61],"depends":[62],"on":[63],"how":[64],"these":[65,263],"context-interaction":[66,127],"programs":[67],"are":[68],"selected,":[69],"which":[70],"has":[71],"remained":[72],"largely":[73],"unexplored.":[74],"In":[75],"paper,":[77],"we":[78],"study":[79],"problem":[81],"introduce":[83],"SRLM,":[84],"framework":[86],"that":[87,141,162,194,227,258],"augments":[88],"uncertainty-aware":[93],"Self-Reflection.":[94],"SRLM":[95,142,214,253],"leverages":[96],"three":[97],"intrinsic":[98],"signals:":[99],"self":[100],"consistency,":[101],"reasoning":[102,261],"length,":[103],"verbalized":[105],"confidence.":[106],"These":[107],"serve":[108],"as":[109],"complementary":[110],"indicators":[111],"model's":[114,200],"internal":[115],"uncertainty,":[116],"model":[119],"uses":[120],"them":[121],"evaluate":[123],"compare":[125],"candidate":[126],"programs.":[128],"Extensive":[129],"experiments":[130],"diverse":[132],"benchmark":[133],"datasets,":[134],"lengths,":[136],"backbone":[138],"models,":[139],"show":[140,161],"consistently":[143],"outperforms":[144],"state-of-the-art":[145],"baselines,":[146],"yielding":[147],"up":[148],"22%":[150],"improvement":[151],"over":[152],"under":[154],"same":[156],"time":[157],"budget.":[158],"Our":[159],"findings":[160],"recursion":[163,190,204],"itself":[164],"is":[165,229,242,248],"not":[166],"primary":[168],"driver":[169],"performance":[171,207],"in":[172,232,252,262],"RLM,":[173],"simple":[176],"self-reflective":[177],"program":[178,240],"search":[179,241],"can":[180],"match":[181],"or":[182,188],"surpass":[183],"without":[185],"requiring":[186],"self-query":[187],"explicit":[189],"mechanisms.":[191],"We":[192,224],"find":[193,226],"lengths":[197],"within":[198],"window,":[201],"RLMs":[202],"degrade":[206],"relative":[208],"base":[211],"model,":[212],"whereas":[213],"yields":[215],"consistent":[216],"gains":[217],"both":[219],"short":[220],"also":[225],"less":[230],"effective":[231],"tasks":[233],"semantically":[235],"intensive":[236],"nature,":[237],"where":[238],"heuristic":[239],"insufficient":[243],"broader":[245],"contextual":[246],"understanding":[247],"required,":[249],"while":[250],"self-reflection":[251],"provides":[254],"semantic":[256],"signal":[257],"better":[259],"steers":[260],"scenarios.":[264]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-20T00:00:00"}