{"id":"https://openalex.org/W7129009703","doi":"https://doi.org/10.48550/arxiv.2602.12828","title":"GRAIL: Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories","display_name":"GRAIL: Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129009703","doi":"https://doi.org/10.48550/arxiv.2602.12828"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.12828","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112122024","display_name":"Zhan Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qu, Zhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126101690","display_name":"Michael F\u00e4rber","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"F\u00e4rber, Michael","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112122024"],"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":0.9882000088691711,"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":0.9882000088691711,"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/T10028","display_name":"Topic Modeling","score":0.00279999990016222,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/inference","display_name":"Inference","score":0.708299994468689},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5989999771118164},{"id":"https://openalex.org/keywords/holy-grail","display_name":"Holy Grail","score":0.5385000109672546},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5289000272750854},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4925999939441681},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4602000117301941},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.4025999903678894},{"id":"https://openalex.org/keywords/hallucinating","display_name":"Hallucinating","score":0.3977999985218048}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.708299994468689},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5989999771118164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5680000185966492},{"id":"https://openalex.org/C2781270358","wikidata":"https://www.wikidata.org/wiki/Q5885594","display_name":"Holy Grail","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5162000060081482},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39259999990463257},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3734999895095825},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35760000348091125},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2734000086784363},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.12828","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.12828","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.12828","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":"pmh:doi:10.48550/arxiv.2602.12828","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7748425602912903}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"future":[1,119],"clinical":[2,16,49,75],"events":[3,50,120],"from":[4],"longitudinal":[5,62],"electronic":[6],"health":[7],"records":[8],"(EHRs)":[9],"is":[10],"challenging":[11],"due":[12],"to":[13,29,44],"sparse":[14,106],"multi-type":[15,148],"events,":[17],"hierarchical":[18,123],"medical":[19],"vocabularies,":[20],"and":[21,68,95,124,127,151],"the":[22],"tendency":[23],"of":[24,116],"large":[25],"language":[26],"models":[27,61],"(LLMs)":[28],"hallucinate":[30],"when":[31],"reasoning":[32],"over":[33],"long":[34],"structured":[35,65,114],"histories.":[36],"We":[37,55],"study":[38],"next-visit":[39,149],"event":[40,87],"prediction,":[41],"which":[42],"aims":[43],"forecast":[45],"a":[46,58,73,100,113,136],"patient's":[47],"upcoming":[48],"based":[51],"on":[52,141],"prior":[53],"visits.":[54],"propose":[56],"GRAIL,":[57],"framework":[59],"that":[60,104,144],"EHRs":[63],"using":[64,132],"geometric":[66],"representations":[67],"structure-aware":[69],"retrieval.":[70],"GRAIL":[71,111,145],"constructs":[72],"unified":[74],"graph":[76,91],"by":[77],"combining":[78],"deterministic":[79],"coding-system":[80],"hierarchies":[81],"with":[82,122],"data-driven":[83],"temporal":[84,125],"associations":[85],"across":[86],"types,":[88],"embeds":[89],"this":[90],"in":[92],"hyperbolic":[93],"space,":[94],"summarizes":[96],"each":[97],"visit":[98],"as":[99,135],"probabilistic":[101],"Central":[102],"Event":[103],"denoises":[105],"observations.":[107],"At":[108],"inference":[109],"time,":[110],"retrieves":[112],"set":[115],"clinically":[117],"plausible":[118],"aligned":[121],"progression,":[126],"optionally":[128],"refines":[129],"their":[130],"ranking":[131],"an":[133],"LLM":[134],"constrained":[137],"inference-time":[138],"reranker.":[139],"Experiments":[140],"MIMIC-IV":[142],"show":[143],"consistently":[146],"improves":[147],"prediction":[150],"yields":[152],"more":[153],"hierarchy-consistent":[154],"forecasts.":[155]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-17T00:00:00"}