{"id":"https://openalex.org/W7134971668","doi":"https://doi.org/10.48550/arxiv.2603.08987","title":"MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment","display_name":"MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134971668","doi":"https://doi.org/10.48550/arxiv.2603.08987"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.08987","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08987","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.08987","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125645904","display_name":"Kailong Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Kailong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128758519","display_name":"Anqi Pu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pu, Anqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123725052","display_name":"Yichen Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128719917","display_name":"Wanhua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wanhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101762861","display_name":"Yicong Li","orcid":"https://orcid.org/0000-0002-5659-793X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yicong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128776085","display_name":"Hanspeter Pfister","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pfister, Hanspeter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128758251","display_name":"Huafeng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Huafeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128772857","display_name":"Xiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128778875","display_name":"Quanzheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Quanzheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128797770","display_name":"Ning Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ning","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/T13702","display_name":"Machine Learning in Healthcare","score":0.5049999952316284,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.5049999952316284,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.1915999948978424,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12574","display_name":"Clinical Reasoning and Diagnostic Skills","score":0.05249999836087227,"subfield":{"id":"https://openalex.org/subfields/2714","display_name":"Family Practice"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.6492999792098999},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6425999999046326},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6402999758720398},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5562000274658203},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5418000221252441},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4383000135421753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7502999901771545},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6492999792098999},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6425999999046326},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6402999758720398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6071000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5580999851226807},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5418000221252441},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4165000021457672},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.08987","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08987","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.08987","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08987","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,34],"medical":[3,36,64,110,166],"large":[4],"language":[5],"models":[6,67],"have":[7,131],"explored":[8],"Test-Time":[9],"Reinforcement":[10],"Learning":[11],"(TTRL)":[12],"to":[13,70,155],"enhance":[14],"reasoning.":[15],"However,":[16],"standard":[17],"TTRL":[18,69,86,142],"often":[19],"relies":[20],"on":[21,127],"majority":[22],"voting":[23],"(MV)":[24],"as":[25],"a":[26,56,94],"heuristic":[27],"supervision":[28,97],"signal,":[29],"which":[30],"can":[31],"be":[32],"unreliable":[33],"complex":[35],"scenarios":[37],"where":[38],"the":[39,47,72,85,90,121],"most":[40],"frequent":[41],"reasoning":[42],"path":[43],"is":[44,107,159],"not":[45],"necessarily":[46],"clinically":[48],"correct":[49],"one.":[50],"In":[51],"this":[52],"work,":[53],"we":[54,83],"propose":[55],"novel":[57],"and":[58,78,138,143,164],"unified":[59],"training":[60],"paradigm":[61,98],"that":[62,104,133,150],"integrates":[63],"process":[65],"reward":[66],"with":[68,93],"bridge":[71],"gap":[73],"between":[74],"test-time":[75],"scaling":[76],"(TTS)":[77],"parametric":[79,123],"model":[80],"optimization.":[81],"Specifically,":[82],"advance":[84],"framework":[87],"by":[88,109],"replacing":[89],"conventional":[91],"MV":[92],"fine-grained,":[95],"expert-aligned":[96],"using":[99],"Med-RPM.":[100],"This":[101],"integration":[102],"ensures":[103],"reinforcement":[105],"learning":[106],"guided":[108],"correctness":[111],"rather":[112],"than":[113],"mere":[114],"consensus,":[115],"effectively":[116],"distilling":[117],"search-based":[118],"intelligence":[119],"into":[120],"model's":[122],"memory.":[124],"Extensive":[125],"evaluations":[126],"four":[128],"different":[129],"benchmarks":[130],"demonstrated":[132],"our":[134],"developed":[135],"method":[136],"consistently":[137],"significantly":[139],"outperforms":[140],"current":[141],"standalone":[144],"PRM":[145],"selection.":[146],"Our":[147],"findings":[148],"establish":[149],"transitioning":[151],"from":[152],"stochastic":[153],"heuristics":[154],"structured,":[156],"step-wise":[157],"rewards":[158],"essential":[160],"for":[161],"developing":[162],"reliable":[163],"scalable":[165],"AI":[167],"systems":[168]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}