{"id":"https://openalex.org/W7160838781","doi":"https://doi.org/10.48550/arxiv.2605.07546","title":"On the Invariance and Generality of Neural Scaling Laws","display_name":"On the Invariance and Generality of Neural Scaling Laws","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160838781","doi":"https://doi.org/10.48550/arxiv.2605.07546"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07546","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.2605.07546","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135842293","display_name":"Xing Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Xing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135907848","display_name":"Ziyin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135891808","display_name":"Suchi Saria","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saria, Suchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135829963","display_name":"Paul Pu Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Paul Pu","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":0.3725999891757965,"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":0.3725999891757965,"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":0.10740000009536743,"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":0.04500000178813934,"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/generality","display_name":"Generality","score":0.7886999845504761},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.698199987411499},{"id":"https://openalex.org/keywords/scaling-law","display_name":"Scaling law","score":0.5773000121116638},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4546999931335449}],"concepts":[{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.7886999845504761},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.698199987411499},{"id":"https://openalex.org/C2988430800","wikidata":"https://www.wikidata.org/wiki/Q428971","display_name":"Scaling law","level":3,"score":0.5773000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5604000091552734},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49900001287460327},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3384000062942505},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2784999907016754},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07546","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.2605.07546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07546","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":{"Neural":[0],"scaling":[1,73,105,116,173,192],"laws":[2,27,75,117,183],"establish":[3],"a":[4,41,79,91,98,143,148],"predictable":[5],"relationship":[6],"between":[7],"model":[8,43],"performance":[9],"and":[10,23,83,103,127,165,167,188],"data":[11,126],"or":[12],"compute,":[13],"offering":[14],"crucial":[15],"guidance":[16],"for":[17,40,174],"resource":[18],"allocation":[19],"in":[20,129,151],"new":[21,42,87],"domains":[22,88],"tasks.":[24],"Yet":[25],"such":[26],"are":[28,34,118],"most":[29],"needed":[30],"precisely":[31],"where":[32,89],"they":[33],"hardest":[35],"to":[36,60,70,86,157,203],"obtain:":[37],"fitting":[38],"one":[39,152],"task":[44],"pair":[45],"demands":[46],"expensive":[47],"sweeps":[48],"that":[49,137],"typically":[50],"exhaust":[51],"the":[52,56,65,113,125,200],"very":[53],"compute":[54],"budget":[55],"law":[57,149],"is":[58,94],"meant":[59],"economize.":[61],"This":[62],"paper":[63],"poses":[64],"research":[66],"question":[67],"of":[68,101,124,196],"how":[69],"develop":[71],"generalizable":[72],"laws:":[74],"fit":[76,150,184],"once":[77],"on":[78,178,185],"well-resourced":[80],"source":[81],"domain":[82,153],"reliably":[84],"transported":[85,156],"running":[90],"full":[92],"sweep":[93],"infeasible,":[95],"which":[96,147],"requires":[97],"fundamental":[99],"understanding":[100],"when":[102],"why":[104],"properties":[106],"change.":[107],"We":[108,159],"address":[109],"this":[110,161],"by":[111],"identifying":[112],"right":[114],"invariants:":[115],"preserved":[119],"under":[120,134,193],"bijective":[121],"(information-preserving)":[122],"transformations":[123,136],"modified":[128],"predictable,":[130],"information-theoretically":[131],"grounded":[132],"ways":[133],"non-bijective":[135],"lower":[138],"its":[139],"information":[140],"resolution":[141],"$\u03c1$:":[142],"single":[144],"axis":[145],"along":[146],"can":[154],"be":[155],"another.":[158],"validate":[160],"across":[162],"language,":[163],"vision,":[164],"speech,":[166],"demonstrate":[168],"two":[169],"cross-domain":[170],"applications:":[171],"predicting":[172,189],"language":[175],"models":[176],"trained":[177],"electronic":[179],"health":[180],"records":[181],"from":[182],"general":[186],"text,":[187],"time-series":[190],"classification":[191],"varying":[194],"levels":[195],"noise":[197],"injection,":[198],"recovering":[199],"data-scaling":[201],"exponents":[202],"within":[204],"$3\\%$":[205],"error.":[206]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}