{"id":"https://openalex.org/W7139014651","doi":"https://doi.org/10.48550/arxiv.2603.16112","title":"ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning","display_name":"ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7139014651","doi":"https://doi.org/10.48550/arxiv.2603.16112"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16112","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16112","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2603.16112","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093760776","display_name":"Tik Yu Yim","orcid":"https://orcid.org/0009-0001-3835-0052"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yim, Tik Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111396963","display_name":"Wenting Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Wenting","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130052980","display_name":"Sum Yee Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Sum Yee","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077116003","display_name":"Tak\u2010Wah Lam","orcid":"https://orcid.org/0000-0003-4676-8587"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Tak-Wah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129992085","display_name":"Siu Ming Yiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiu, Siu Ming","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5093760776"],"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":0.4681999981403351,"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":0.4681999981403351,"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/T13629","display_name":"Text Readability and Simplification","score":0.08799999952316284,"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":0.05689999833703041,"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/adaptation","display_name":"Adaptation (eye)","score":0.6089000105857849},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5748000144958496},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49869999289512634},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.42260000109672546},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.3684999942779541},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.366100013256073},{"id":"https://openalex.org/keywords/case-based-reasoning","display_name":"Case-based reasoning","score":0.35510000586509705},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.329800009727478}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372999787330627},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5748000144958496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49869999289512634},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41850000619888306},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3684999942779541},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.329800009727478},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C199519371","wikidata":"https://www.wikidata.org/wiki/Q942695","display_name":"Source lines of code","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16112","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16112","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.16112","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16112","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8673099279403687}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adapting":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"to":[5,120,164],"specialized":[6],"financial":[7,38,86],"reasoning":[8,39,87,101,125],"typically":[9],"requires":[10],"expensive":[11],"fine-tuning":[12],"that":[13,25,63],"produces":[14],"model-locked":[15],"expertise.":[16],"Training-free":[17],"alternatives":[18],"have":[19],"emerged,":[20],"yet":[21],"our":[22],"experiments":[23],"show":[24],"leading":[26],"methods":[27],"(GEPA":[28],"and":[29,58,93,96,105,126,143,161],"ACE)":[30],"achieve":[31],"only":[32],"marginal":[33],"gains":[34],"on":[35,85,115,123,128],"the":[36,42,146],"FAMMA":[37],"benchmark,":[40],"exposing":[41],"limits":[43],"of":[44],"unstructured":[45],"text":[46],"optimization":[47],"for":[48],"complex,":[49],"multi-step":[50],"domain":[51,157,165],"reasoning.":[52],"We":[53],"introduce":[54],"Automated":[55],"Skill":[56],"Distillation":[57],"Adaptation":[59],"(ASDA),":[60],"a":[61,81,155,159],"framework":[62],"automatically":[64],"generates":[65],"structured":[66],"skill":[67,98,138],"artifacts":[68,139],"through":[69],"iterative":[70],"error-corrective":[71],"learning":[72],"without":[73,167],"modifying":[74],"model":[75,79],"weights.":[76],"A":[77],"teacher":[78],"analyzes":[80],"student":[82],"model's":[83],"failures":[84],"tasks,":[88],"clusters":[89],"errors":[90],"by":[91],"subfield":[92],"error":[94],"type,":[95],"synthesizes":[97],"files":[99],"containing":[100],"procedures,":[102],"code":[103],"templates,":[104],"worked":[106],"examples,":[107],"which":[108],"are":[109,140],"dynamically":[110],"injected":[111],"during":[112],"inference.":[113],"Evaluated":[114],"FAMMA,":[116],"ASDA":[117],"achieves":[118],"up":[119],"+17.33%":[121],"improvement":[122],"arithmetic":[124],"+5.95%":[127],"non-arithmetic":[129],"reasoning,":[130],"substantially":[131],"outperforming":[132],"all":[133],"training-free":[134],"baselines.":[135],"The":[136],"resulting":[137],"human-readable,":[141],"version-controlled,":[142],"compatible":[144],"with":[145,154],"Agent":[147],"Skills":[148],"open":[149],"standard,":[150],"offering":[151],"any":[152],"organization":[153],"labeled":[156],"dataset":[158],"practical":[160],"auditable":[162],"path":[163],"adaptation":[166],"weight":[168],"access":[169],"or":[170],"retraining.":[171]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}
