{"id":"https://openalex.org/W7101917114","doi":"https://doi.org/10.48550/arxiv.2510.22809","title":"A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)","display_name":"A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)","publication_year":2025,"publication_date":"2025-10-26","ids":{"openalex":"https://openalex.org/W7101917114","doi":"https://doi.org/10.48550/arxiv.2510.22809"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.22809","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22809","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.2510.22809","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hazard, Christopher J.","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hazard, Christopher J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Resnick, Michael","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Resnick, Michael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Beel, Jacob","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beel, Jacob","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xia, Jack","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Jack","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mack, Cade","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mack, Cade","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Glennie, Dominic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glennie, Dominic","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Fulp, Matthew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fulp, Matthew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Maze, David","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maze, David","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bassett, Andrew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bassett, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Koistinen, Martin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koistinen, Martin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"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":true,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.18039999902248383,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.18039999902248383,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.10429999977350235,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09790000319480896,"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/generalization","display_name":"Generalization","score":0.6407999992370605},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5842000246047974},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5705000162124634},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4805999994277954},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4528000056743622},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4212999939918518},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3831000030040741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.748199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.647599995136261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6424000263214111},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6407999992370605},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5842000246047974},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5705000162124634},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3831000030040741},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2779915298","wikidata":"https://www.wikidata.org/wiki/Q7604400","display_name":"Statistical learning theory","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.22809","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22809","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.2510.22809","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22809","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7775940299034119,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"machine":[1,87,99,146],"learning":[2,100,147],"relies":[3],"on":[4],"explicit":[5],"models":[6],"and":[7,12,27,38,45,69,77,89,148],"domain":[8],"assumptions,":[9],"limiting":[10],"flexibility":[11],"interpretability.":[13],"We":[14],"introduce":[15],"a":[16,82,106,118,135],"model-free":[17],"framework":[18,85],"using":[19],"surprisal":[20],"(information":[21],"theoretic":[22],"uncertainty)":[23],"to":[24,114,117,139,144],"directly":[25],"analyze":[26],"perform":[28],"inferences":[29],"from":[30],"raw":[31],"data,":[32],"eliminating":[33],"distribution":[34],"modeling,":[35],"reducing":[36],"bias,":[37],"enabling":[39],"efficient":[40],"updates":[41],"including":[42,62,125],"direct":[43],"edits":[44],"deletion":[46],"of":[47,108,121,156],"training":[48],"data.":[49,127],"By":[50],"quantifying":[51],"relevance":[52],"through":[53],"uncertainty,":[54],"the":[55,157],"approach":[56],"enables":[57],"generalizable":[58],"inference":[59],"across":[60,96],"tasks":[61],"generative":[63],"inference,":[64],"causal":[65],"discovery,":[66],"anomaly":[67],"detection,":[68],"time":[70],"series":[71],"forecasting.":[72],"It":[73],"emphasizes":[74],"traceability,":[75],"interpretability,":[76],"data-driven":[78],"decision":[79],"making,":[80],"offering":[81],"unified,":[83],"human-understandable":[84],"for":[86],"learning,":[88],"achieves":[90],"at":[91],"or":[92],"near":[93],"state-of-the-art":[94],"performance":[95],"most":[97],"common":[98],"tasks.":[101],"The":[102],"mathematical":[103],"foundations":[104],"create":[105],"``physics''":[107],"information,":[109],"which":[110],"enable":[111],"these":[112],"techniques":[113],"apply":[115],"effectively":[116],"wide":[119],"variety":[120],"complex":[122],"data":[123],"types,":[124],"missing":[126],"Empirical":[128],"results":[129],"indicate":[130],"that":[131,151],"this":[132],"may":[133],"be":[134],"viable":[136],"alternative":[137],"path":[138],"neural":[140],"networks":[141],"with":[142],"regard":[143],"scalable":[145],"artificial":[149],"intelligence":[150],"can":[152],"maintain":[153],"human":[154],"understandability":[155],"underlying":[158],"mechanics.":[159]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-29T00:00:00"}