{"id":"https://openalex.org/W7123741576","doi":"https://doi.org/10.48550/arxiv.2601.07675","title":"Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data","display_name":"Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7123741576","doi":"https://doi.org/10.48550/arxiv.2601.07675"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.07675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07675","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.2601.07675","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120641014","display_name":"Kishan Padayachy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Padayachy, Kishan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048936137","display_name":"Ronald Richman","orcid":"https://orcid.org/0000-0002-0441-8354"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richman, Ronald","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122975276","display_name":"Mario V. W\u00fcthrich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"W\u00fcthrich, Mario V.","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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.24379999935626984,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.24379999935626984,"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":0.14380000531673431,"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/T14347","display_name":"Big Data and Digital Economy","score":0.07129999995231628,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/security-token","display_name":"Security token","score":0.45410001277923584},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4194999933242798},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4189000129699707},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.36340001225471497},{"id":"https://openalex.org/keywords/successor-cardinal","display_name":"Successor cardinal","score":0.36250001192092896},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.3172000050544739},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.3142000138759613},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.29490000009536743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4661000072956085},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4189000129699707},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36980000138282776},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C75306776","wikidata":"https://www.wikidata.org/wiki/Q7632662","display_name":"Successor cardinal","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3366999924182892},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.29490000009536743},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C3020299011","wikidata":"https://www.wikidata.org/wiki/Q626608","display_name":"Life insurance","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C184670325","wikidata":"https://www.wikidata.org/wiki/Q512604","display_name":"Loop (graph theory)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C521332185","wikidata":"https://www.wikidata.org/wiki/Q185816","display_name":"Analogy","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.07675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07675","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.2601.07675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07675","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":[{"id":"https://metadata.un.org/sdg/8","score":0.534844696521759,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"Tab-TRM":[2,39,103],"(Tabular-Tiny":[3],"Recursive":[4,18],"Model),":[5],"a":[6,60,69],"network":[7],"architecture":[8],"that":[9,64],"adapts":[10],"the":[11,28,38,46,80,84,91,119,133],"recursive":[12,72,75],"latent":[13,53],"reasoning":[14,44,61,81],"paradigm":[15],"of":[16,128],"Tiny":[17],"Models":[19],"(TRMs)":[20],"to":[21],"insurance":[22,99],"modeling.":[23],"Drawing":[24],"inspiration":[25],"from":[26],"both":[27],"Hierarchical":[29],"Reasoning":[30],"Model":[31],"(HRM)":[32],"and":[33,59,88,114,122],"its":[34],"simplified":[35],"successor":[36],"TRM,":[37],"model":[40,112],"makes":[41],"predictions":[42],"by":[43,68],"over":[45],"input":[47],"features.":[48],"It":[49],"maintains":[50],"two":[51],"learnable":[52],"tokens":[54],"-":[55,63,108,117],"an":[56],"answer":[57,92],"token":[58,86],"state":[62,82],"are":[65],"iteratively":[66],"refined":[67],"compact,":[70],"parameter-efficient":[71],"network.":[73],"The":[74],"processing":[76],"layer":[77],"repeatedly":[78],"updates":[79],"given":[83],"full":[85],"sequence":[87],"then":[89],"refines":[90],"token,":[93],"in":[94,126],"close":[95],"analogy":[96],"with":[97],"iterative":[98,109],"pricing":[100],"schemes.":[101],"Conceptually,":[102],"bridges":[104],"classical":[105],"actuarial":[106],"workflows":[107],"generalized":[110],"linear":[111],"fitting":[113],"minimum-bias":[115],"calibration":[116],"on":[118,132],"one":[120],"hand,":[121],"modern":[123],"machine":[124],"learning,":[125],"terms":[127],"Gradient":[129],"Boosting":[130],"Machines,":[131],"other.":[134]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-14T00:00:00"}