{"id":"https://openalex.org/W7162781541","doi":"https://doi.org/10.48550/arxiv.2605.30002","title":"KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning","display_name":"KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162781541","doi":"https://doi.org/10.48550/arxiv.2605.30002"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30002","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":null,"license_id":null,"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.2605.30002","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137335065","display_name":"Kun Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137366593","display_name":"Ziwei Shan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Ziwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137322529","display_name":"Yuchen Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yuchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137373030","display_name":"Yiyang Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Yiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137329701","display_name":"Sihan Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Sihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128275892","display_name":"Shuqi Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Shuqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137346082","display_name":"Lintao Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lintao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030485564","display_name":"Xingyu Lu","orcid":"https://orcid.org/0000-0002-8393-0582"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137357737","display_name":"Kan Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Kan","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/T11326","display_name":"Stock Market Forecasting Methods","score":0.3497999906539917,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.3497999906539917,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.15469999611377716,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.1137000024318695,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7741000056266785},{"id":"https://openalex.org/keywords/semantic-reasoner","display_name":"Semantic reasoner","score":0.6929000020027161},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.454800009727478},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.4456999897956848},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42579999566078186},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3693999946117401},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.3549000024795532},{"id":"https://openalex.org/keywords/case-based-reasoning","display_name":"Case-based reasoning","score":0.3398999869823456}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7741000056266785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513999938964844},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.6929000020027161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.623199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.506600022315979},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40400001406669617},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C103057564","wikidata":"https://www.wikidata.org/wiki/Q4751139","display_name":"Analytic reasoning","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C2985727698","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Semantic annotation","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30002","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30002","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30002","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":null,"license_id":null,"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":{"Cross-domain":[0],"multimodal":[1,21,78],"time":[2,79,183],"series":[3,80,184],"forecasting":[4,96,148,164],"is":[5,189],"a":[6,73,87,137,144,176],"challenging":[7],"task,":[8],"requiring":[9],"models":[10],"to":[11,51,102],"integrate":[12],"precise":[13],"numerical":[14,60,95,105],"comprehension,":[15],"cross-domain":[16],"semantic":[17,45,54,108],"understanding,":[18],"and":[19,47,56,62,86,94,107,126,153,173,181],"effective":[20],"fusion.":[22],"Existing":[23],"approaches":[24],"either":[25],"build":[26],"Time":[27],"Series":[28],"Foundation":[29],"Models":[30,39],"(TSFMs)":[31],"from":[32,147],"scratch":[33],"or":[34],"leverage":[35],"pretrained":[36,171],"Large":[37],"Language":[38],"(LLMs).":[40],"However,":[41],"TSFMs":[42],"often":[43],"overlook":[44],"understanding":[46,106],"lack":[48],"the":[49,104,120,133,168],"ability":[50],"perform":[52],"future-oriented":[53],"reasoning,":[55,134],"LLMs":[57,172],"struggle":[58],"with":[59,150],"comprehension":[61],"accurate":[63,125],"quantitative":[64],"forecasting.":[65],"To":[66,130],"overcome":[67],"these":[68],"limitations,":[69],"we":[70,135],"propose":[71],"KairosAgent,":[72],"novel":[74],"agentic":[75],"framework":[76],"for":[77,179],"forecasting,":[81],"including":[82],"an":[83],"LLM-based":[84],"reasoner":[85],"TSFM-based":[88],"forecaster.":[89],"KairosAgent":[90,160],"unifies":[91],"textual":[92],"reasoning":[93,109,114],"by":[97],"dynamically":[98],"invoking":[99],"analytical":[100],"tools":[101],"enhance":[103],"capabilities":[110],"of":[111,140,170],"LLMs.":[112],"The":[113,186],"results":[115],"are":[116],"subsequently":[117],"fused":[118],"into":[119],"TSFM":[121],"pipeline,":[122],"enabling":[123],"more":[124],"reliable":[127],"future":[128],"predictions.":[129],"further":[131],"improve":[132],"curate":[136],"large-scale":[138],"corpus":[139],"high-quality":[141],"trajectories,":[142],"alongside":[143],"reinforcement":[145],"learning":[146],"paradigm":[149],"multi-turn":[151],"refinement":[152],"turn-level":[154],"credit":[155],"assignment.":[156],"Experiments":[157],"demonstrate":[158],"that":[159],"achieves":[161],"superior":[162],"zero-shot":[163],"performance":[165],"while":[166],"maximizing":[167],"utility":[169],"TSFMs,":[174],"presenting":[175],"promising":[177],"direction":[178],"efficient":[180],"interpretable":[182],"agents.":[185],"project":[187],"page":[188],"at":[190],"https://foundation-model-research.github.io/KairosAgent":[191],".":[192]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-30T00:00:00"}