迈向人工智能研究的端到端自动化 Towards end-to-end automation of AI research

914|Nature|Vol651|26March2026

Article

Towardsend-to-endautomationofAIresearch

/10.1038/s41586-026-10265-5

Received:8July2025

Accepted:11February2026

Publishedonline:25March2026

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ChrisLu1,2,5,CongLu1,3,4,5,RobertTjarkoLange1,5,YutaroYamada1,5✉,ShengranHu1,3,4,JakobFoerster2,DavidHa1✉&JeffClune3,4✉

Theautomationofscienceisalong-standingambitioninartificialintelligence(AI)

research

1

,

2

.Althoughthecommunityhasmadesubstantialprogressinautomating

individualcomponentsofthescientificprocess,asystemthatautonomouslynavigatestheentireresearchlifecycle—fromconceptiontopublication—hasremainedoutof

reach.Herewepresentapipelineforautomatingtheentirescientificprocessendtoend.WepresentTheAIScientist,whichcreatesresearchideas,writescode,runs

experiments,plotsandanalysesdata,writestheentirescientificmanuscript,and

performsitsownpeerreview.Itsideas,executionandpresentationareofsufficient

qualitythatthemanuscriptgeneratedbythisAIsystempassedthefirstroundofpeerreviewforaworkshopofatop-tiermachinelearningconference.Theworkshophadanacceptancerateof70%.Oursystemleveragesmodernfoundationmodels

3

–

5

withinacomplexagenticsystem.WeevaluateTheAIScientistintwosettings:afocused

modeusinghuman-providedcodetemplatesasaninitialscaffoldforconducting

researchonaspecifictopicandatemplate-free,open-endedmodethatleverages

agenticsearchforwiderscientificexploration

6

,

7

.Bothsettingsproducediverseideasandautomaticallytest,reportonandevaluatethem.ThisachievementdemonstratesthegrowingcapacityofAIformakingscientificcontributionsandsignifiesapotentialparadigmshiftinhowresearchisconducted.Aswithanyimpactfulnewtechnology,therecouldbeimportantrisks,includingtaxingoverwhelmedreviewsystemsand

addingnoisetothescientificliterature.However,ifdevelopedresponsibly,suchautonomoussystemscouldgreatlyacceleratescientificdiscovery.

AIhaslongbeenusedtoaidscientificdiscovery,anambitionwithdeep

Acentralchallengeindevelopingsuchasystemisautomatically

rootsinthehistoryofthefield

1

,

8

–

11

.Beforetheriseoflargelanguage

evaluatingthequalityofitsscientificoutputatscale.Toaddressthis,

models(LLMs),AIwaslimitedtohelpingwithspecific,narrowtasks,

wecreatedanautomatedreviewerandfirstevaluateditsperformance

suchasdiscoveringchemicalstructures

2

,findingmathematicalproofs1,

againstreal,human-generatedpapers.TheAutomatedReviewercan

discoveringnewmaterials

12

–

14

andpredictingthethree-dimensional

accuratelypredictconferenceacceptancedecisions,performingonpar

shapeofproteins

15

,

16

.Othersystemsfocusedonanalysingpre-collected

withhumanreviewers(SupplementaryInformationsectionA.3).We

datasetstofindnewinsights

10

,

17

,

18

.However,withtherecentadventof

thenusedTheAutomatedReviewertocomparevariousconfigurations

powerfulandgeneralfoundationmodels,theroleofAIhasexpandedto

ofTheAIScientistbyassessinghowperformancechangeswiththescale

includeassistingwithawiderarrayofresearchactivities.Forexample,

ofthetest-timecomputeandthequalityoftheunderlyingfoundation

LLMsnowhelpwithgeneratingnewhypotheses

19

–

23

,writingliterature

model.WefindthatTheAIScientistperformsbetterwithmorecompute

reviews

24

,

25

andcodingexperiments

26

–

29

.Despitetheseadvancesinauto-

resources(Fig.

3c

).Furthermore,TheAutomatedReviewershowsthat

matingindividualcomponents,asystemthatautonomouslynavigates

improvementstothebasemodelssignificantlyimprovethequality

theentireresearchlifecycle—fromconceptiontopublication—has

ofthegeneratedpapers,afindingthatstronglyimpliesthatfuture

remainedoutofreachuntilnow.

versionsofoursystemwillbesubstantiallymorecapable,asmodels

ThispaperintroducesTheAIScientist,apipelinethatachievesthe

continuetoimprove(Fig.

1b

).

visionoffullend-to-endautomationofthescientificprocess.TheAI

ToassessTheAIScientistinthesamesettinginwhichhuman-authored

Scientistusesexistingfoundationmodelstoperformideation,litera-

papersareevaluated,weconductedanexperimentwherewesubmit-

turesearch,experimentplanningandimplementation,resultanalysis,

tedgeneratedpaperstoaworkshopattheInternationalConference

manuscriptwriting,andpeerreviewtoproducecomplete,newpapers.

onLearningRepresentations(ICLR),withtheorganizers’consent.

Wefocusonmachinelearningscience,asexperimentstypicallyoccur

Incomputerscience,suchtop-tierconferencesaretheprimaryand

entirelyonthecomputer.

mostprestigiousvenuesforarchivalandrigorouslypeer-reviewed

1SakanaAI,Tokyo,Japan.2FLAIR,UniversityofOxford,Oxford,UK.3UniversityofBritishColumbia,Vancouver,BritishColumbia,Canada.4VectorInstitute,Toronto,Ontario,Canada.5Theseauthorscontributedequally:ChrisLu,CongLu,RobertTjarkoLange,YutaroYamada.✉e-mail:

yutaro.yamada.y@

;

hadavid@sakana.ai

;

jclune@

Nature|Vol651|26March2026|915

Sonnet-4

a

Fit(R2=0.517,P<0.00001)95%confidenceinterval

AIScientist:template-based

AIScientist:template-free

6

5

4

3

2

1

b

0

Sonnet-3.5

Sonnet-3.5

Sonnet-3.7

Gemini-2.5

AIreviewerpaperscore

GPT-4

Gemini-1.5

Sonnet-3

GPT-4o

GPT-4o

o1

Gemini-2.0

o3

July2023

October2023

January2024

April2024

July2024

October2024

January2025

April2025

July2025

Balancedaccuracy

Languagemodelreleasedate

0.7

0.6

0.5

0.4

Beforecutoff(2017–2024)HumanReject

Aftercutoff(2025)

Automatedreviewer

Random

c

Experimentation

Preliminaryinvestigation

[Writetolog][Best]

Hyperparametertuning

[Writetolog][Best]

Researchexecution

[Writetolog][Best]

Ablationstudies

Write-up

Plottingandfeedback

Papertemplate

Paper

PaperAIreview

Ideation

LLMideaproposal

Noveltychecking

Scoringandarchiving

Fig.1|TheAIScientistworkflow.a,TheAIScientistconsistsofdistinctphases

coveringautomatedideageneration,tree-basedexperimentation,manuscriptwritingandreviewing.Theexperimentationphaseusesanagentictreesearchtogenerateandrefinecodeimplementations.Thisisstructuredintofour

stages:(1)initialinvestigation,(2)hyperparametertuning,(3)researchagendaexecutionand(4)ablationstudies.Fromoneexperimentalstagetothenext,

thebest-performingcheckpointisselectedtoseedthenextstageofthetree

search.b,ScoresforTheAIScientistpapersacrossmodelreleases.Paperqualityconsistentlyimproveswiththeunderlyingmodelreleasedate(asjudgedby

TheAutomatedReviewer),indicatingconsistentfutureimprovementswith

improvingfoundationmodels.Theobservedcorrelationisstatistically

significant(P<0.00001).Shadedregionsrepresentthestandarderror.Pointsrepresentmeanscoreswitherrorbarsandshadedregionsindicatingthe

standarderror(n=6fortemplate-freepoints,n=3fortemplate-basedpoints).Fullexperimentaldetails,includingmodelversionsandreplicationcounts,areprovidedinSupplementaryInformationsectionA.2.9.c,Automatedreview

versusconferencedecisions.TheAutomatedReviewerachievesperformancecomparablewiththatofhumanreviewers,asvalidatedbyopenlyavailable

decisionsfrompastconferences(Table

1

).Barsrepresentmeanbalanced

accuracy;errorbarsshow95%bootstrappedconfidenceintervals(5,000

replicates).Forreplicability,eachautomatedreviewisa5-runensemble.Two-samplez-testsonsubsampledaccuracy(automatedn=698/876,humann=412)showednosignificantdifferencebeforethetrainingcutoff(P=0.319)orpost-cutoff(P=0.921).Non-parametricbootstraptestsonF1scoresshowed

automatedoutperformance(P<0.001).

publication.Theyalsohaveworkshopswithasubstantiallylowerbutstillnon-trivialbarforpeer-reviewedacceptance.

OneofTheAIScientist’smanuscriptsachievedhighenoughscorestoexceedtheaveragehumanacceptancethresholdataworkshop,provid-inganexampleofafullyAI-generatedpapersuccessfullynavigatingapeer-reviewprocess,albeitonewithalowerbar.

Generatingmanuscripts

TheAIScientistsequentiallycompletesfourmainphases(Fig.

1a

).Inthefirstphase,TheAIScientistispromptedtoiterativelygrowanarchive

30

ofhigh-levelresearchdirectionsandhypothesesthatitcanexplorewithinauser-specifiedmachinelearningresearchsubfield(anexampleprogressionisvisualizedinSupplementaryInformationsectionC.4).Foreachdirection,itgeneratesadescriptivetitle,itsrea-soningforwhattheideaisandwhyitwouldbeinterestingtopursue,andaproposedexperimentalplan(SupplementaryInformationsec-tionsA.1.1andA.2.6).Afterideageneration,TheAIScientistfiltersideasbyconnectingthelanguagemodeltotheSemanticScholarapplicationprogramminginterface(API)

31

andwebaccessastools

32

.ThisallowsTheAIScientisttodiscardanyideathattoocloselyresemblesaworkintheexistingliterature.

ThesecondphaseofTheAIScientistexecutestheproposedexperimentsandthenvisualizestheirresultsforthedownstreamwrite-up.Wetestedtwodifferentvariantsofexperimentexecution:

(1)Template-based:TheAIScientistisprovidedwithastartingcodetemplatethatreproducesatrainingrunfromapopularalgorithm.TheAIScientistthenexecutestheproposedexperimentplaninlinear

order(SupplementaryInformationsectionA.1).(2)Template-free:Alternatively,TheAIScientistcangenerateaninitialstartingcodescriptbyitself.Inthiscase,experimentationincludesfurtherstagesforoptimizingthecodeitwritesfromscratch,andexperimentexecu-tionleveragesextratest-timecomputewithatreesearch(Fig.

3a,b

andMethods).Aftereachexperiment,TheAIScientistisgiventheresultsandispromptedtotakenotesinthestyleofanexperimentaljournalforfutureplanningandwrite-up.

ThethirdphaseofTheAIScientistproducesaconcisewrite-upofitsresearchinthestyleofastandardmachinelearningconferencepaper.TheAIScientistispromptedtofillinablankLaTeXconferencetemplatesectionbysectionusingitsnotesandplots(Methods).Toconstructtherelatedworksectionandaddcitationsthroughoutthemanuscript,thesystemqueriestheSemanticScholar

31

APIforrelevantliteratureandcomparesitsfindingsagainstthegeneratedmanuscriptover20rounds.Foreachpotentialcitation,thesystemgeneratesatextualjustificationforitsinclusion,whichinformsTheAIScientistonhowtousethereferenceappropriatelywithinthemanuscript.

Finally,thepapergeneratedbyTheAIScientistundergoesareviewbyTheAutomatedReviewer,whichautomaticallyevaluatesthescientificqualityoftheconductedresearch.

Automatedevaluationofgeneratedpapers

TheAutomatedReviewerprovidesreviewsbasedonthereviewguidelinesforthetop-tierNeuralInformationProcessingSys-tems(NeurIPS)conference(

https://neurips.cc/Conferences/2022/

916|Nature|Vol651|26March2026

Article

Table1|PerformancecomparisonofhumanreviewersandTheAutomatedReviewer

Reviewer

Balancedaccuracy(↑)

Accuracy(↑)

F1score(↑)

AUC(↑)

FPR(↓)

FNR(↓)

Human(NeurIPS)

0.66

0.73

0.49

0.65

0.17

0.52

Yearsbeforeknowledgecutoff(2017–2024)

Randomdecision

0.50

0.54

0.47

0.52

0.47

0.43

Alwaysreject

0.50

0.65

0.00

0.50

0.00

1.00

AutomatedReviewer

0.69±0.04

0.65±0.10

0.62±0.09

0.69±0.09

0.45±0.10

0.17±0.08

Yearafterknowledgecutoff(2025)

Randomdecision

0.52

0.51

0.48

0.49

0.50

0.48

Alwaysreject

0.50

0.56

0.00

0.50

0.00

1.00

AutomatedReviewer

0.66±0.03

0.63±0.09

0.67±0.09

0.65±0.10

0.52±0.10

0.17±0.07

Performancecomparisonofhumanreviewers(NeurIPS2021consistencyexperiment

34

)andtheAutomatedReviewer,evaluatedonpaperspublishedbefore(2017–2024)andafter(2025)the

knowledgecutoff.TheAutomatedReviewerachievedperformancesuperiororcomparablewithhumanreviewerconsistencyinkeymetricssuchasF1score,areaunderthecurve(AUC)and

balancedaccuracy,evenfordatabeyondtheknowledgecutoff,highlightingitsrobustnessandreliabilityacrossdifferenttimeperiods.Errormarginsdenotethe95%bootstrappedconfidenceintervals.Arrowsindicatewhetheritisbetterforascoretobehigher(↑)orlower(↓).SupplementaryInformationsectionA.3.2explainseachmetricandcomparisonindetail.FNR,falsenegativerate;FPR,falsepositiverate.

ReviewerGuidelines

).Theoutputcontainsnumericalscores(soundness,presentation,contribution,overallqualityandreviewerconfidence),listsofweaknessesandstrengths,aswellasabinarydecision(acceptorreject).TheAutomatedReviewerpipelineconsistsofanensembleoffivereviews,followedbyameta-reviewinwhichthemodelactsasanareachairtomakeafinaldecisionconditionedonallfivereviews(Supple-mentaryInformationsectionA.3).WecomparedAutomatedReviewerdecisionswithgroundtruthdataforICLRpapersextractedfromthepubliclyavailableOpenReviewdataset

33

.AsshowninTable

1

,theagree-mentofAutomatedReviewerassessmentswithhumanassessmentsiscomparablewithinter-humanagreementmeasuredbyF1scoreandbalancedaccuracy,asreportedintheNeurIPS2021consistencystudy

34

,whichmeasuredagreementbetweenhumanreviewersonacompara-blesetofsubmissions(SupplementaryInformationsectionA.3).Thisdemonstratesitsabilitytoreplicatethecollectivejudgementofhumanreviewerswithhighfidelity.Theseresultsarestatisticallysignificant(non-parametricbootstraptest

35

andtwo-samplez-test

36

;Supplemen-taryInformationsectionA.3).Next,toinvestigatetheeffectofpotentialdatacontamination(thepossibilitythatdecisionsonapaperwerepartofthetrainingsetfortheLLM),weevaluatedTheAutomatedReviewerontwodatasets:onecontaining1,000papersfromyearspotentiallywithinthetrainingdatausedforthemodel(2017–2024)andasecond‘clean’datasetfromtheyearafterthecutoff(2025),whichcouldnothavebeenseenduringtraining.Acomparisonbetweenyearsbeforeandaftertheknowledgecutoffindicatesthatdatacontaminationmayexist,asbalanceddecisionaccuracydecreasesfrom69%beforeto66%intheyearafterthecutoff.However,theresultsfortheyearafterthecutoffremaincomparablewiththoseofhumanreviewers(forexample,66%balancedaccuracy),showingthatpotentialcontaminationhad,atmost,aminimaleffect.

UsingTheAutomatedReviewer,weassessedthequalityoftheresearchpapersgeneratedbyawiderangeofLLMsasthecoremodelwithinTheAIScientist.Ouranalysisrevealedacleartrend:asmodelsimproveovertime,thequalityofthepapersproducedbyTheAIScientistincreasedcorrespondingly(Fig.

1b

).Withrecentgenera-tionsofmodels,onaverage,TheAIScientistproducedpapersthatapproachborderlineacceptabilityformachinelearningconferenceworkshops,asjudgedbyourAutomatedReviewer(SupplementaryFig.B2).Additionally,thereisastrongcorrelationbetweentheamountofcomputeallocatedperpaperandtheresultingquality(Fig.

3c

),indicatingthatbothmodelscaleandinference-timeinvest-mentplayimportantrolesintheoutputqualityofTheAIScientist,furtherindicatingthepossibilityofsubstantialimprovementsasthecostsofAIsystemscontinuetoexponentiallydecreaseandcapabili-tiesexponentiallyincrease

37

.

Humanevaluationresults

PerhapstheultimateandfairesttestofthequalityoftheworkofTheAIScientistisaversionofwhatwemightcallanAIscientistTuringtest:submittingtheworktothesamerigorous,blindpeer-reviewsystemsusedtoevaluatehumanscience.Wesubmittedthreegeneratedmanu-scriptstotheformalpeer-reviewprocessofaworkshopatatop-tiermachinelearningconference.Thisexperimentwasconductedwiththeapprovaloftherelevantinstitutionalreviewboard(IRB;Supple-mentaryInformationsectionC.3)andthefullcooperationoftheICLR2025leadershipandtheorganizersoftheICan’tBelieveIt’sNotBetter(ICBINB)workshop.Thiswastheonlyvenuethatwesubmittedto.

Thetemplate-freeversionofTheAIScientistwasreadilyadaptedtothissettingbysimplypromptingitwiththebroadthemeofthework-shop(whichwasinvestigatingdeeplearninglimitations,includingwherepreviousideastoimproveithadnotworked).Theoverallprocesswasthenruntogenerateideas,experimentsandpapers.Wemanuallyfilteredthemostpromisingoutputsateachstage(SupplementaryInformationsectionA.4).Hadthisfilteringnotoccurred,thepapersunderanalysiswouldstillhavebeenproducedintheirfinalform,justalongwithotherpapersand,thus,atagreatertotalcost.Thisprocessresultedinthreecompletemanuscriptsbeingselectedforsubmission.Theselectionwasbasedonthreecriteria:whethertheideawasalignedwiththeworkshoptopic,whetherthecodecorrectlyimplementedtheproposedideaandranwithouterrors,andthecorrectnessofthemanuscriptformatting(SupplementaryInformationsectionA.4).Theentirescientificworkflowforeachpaper,fromideationandcodingtomanuscriptwriting,wasperformedwithoutanyhumanmodification.Thesethreesubmissionswereincludedamongthe43papersreviewedfortheworkshop.Reviewerswereinformedthatsomeofthesubmis-sionswereAI-generatedbutnotwhichones,ensuringablindprocess. OneofthethreeAI-generatedmanuscriptsreceivedanaveragescoreof6.33fromthereviewers(individualscoreswere6,7and6),placingitabovetheaverageacceptancethresholdfortheworkshop(Fig.

2

).Theorganizerssaidthatthepaperwouldhavebeenacceptedinalllikelihoodwereitnotwithdrawnaccordingtoourpre-establishedprotocolduetobeingAI-generated.Notably,theacceptedmanuscriptreportedanegativeresult,aligningwiththefocusoftheworkshoponinterestingnegativeresults.Theothertwopapersdidnotmeetthebarforacceptance(SupplementaryTableD9).Thus,afullyAI-generatedpaperpassedastandardscientificpeer-reviewprocess.Wealsocon-ductedourowninternalreview,usingthehumanAIresearchersonourteam(SupplementaryInformationsectionC.2).Theteamconcludedthatalthoughoneofthepapersdidmeetthebarforworkshoppapers,nonemetthehigherbarforamainICLRconferencepublication.A

Nature|Vol651|26March2026|917

Technicalmethodology(page2)

Titleandabstract(page1)

Datavisualizations(page4)

References(page5)

Fig.2|SelectedsectionsfromapapergeneratedbyTheAIScientistthat

wasacceptedviapeerreviewatatop-tiermachinelearningconference

workshop.Thepaperreceivedpeer-reviewscoresof6(weakaccept),7(accept)and6(weakaccept)beforemeta-reviewandrankedamongthetop45%of

paperssubmittedforpeerreview.ThisdemonstratesthatafullyAI-generatedpapercannavigatethepeer-reviewprocesssuccessfullyatatop-tierconferenceworkshop.Afull-sizedversionofthispaperisavailableinSupplementary

InformationsectionD.2.1.

fullanalysisofallthreesubmittedpapers,includingtheirstrengths,weaknessesandimplementations,isprovidedinSupplementaryInfor-mationsectionC.2.

Limitations

AlthoughTheAIScientistgeneratedaworkshoppaperthatpassedpeerreview,thereisroomforimprovementifitistomatchthebesthuman-producedscience.Onlyoneofthreesubmissionswasaccepted,andworkshopshavemuchhigheracceptanceratesthanmainconfer-ences(forexample,70%fortheICLR2025ICBINBworkshop

38

versus32%fortheICLR2025mainconference

39

).Therefore,TheAIScientistcannotyetmeetthestandardsoftop-tierpublicationsnorevendosoconsistentlyforworkshops.Commonfailuremodesincludethegenerationofnaiveorunderdevelopedideas,incorrectimplementa-tionsofthemainidea,alackofdeepmethodologicalrigour,errorsinexperimentalimplementation,duplicatingfiguresinthemaintextandtheappendix,andmanytypesofhallucinations,suchasinaccuratecitations(afullanalysisoffailuremodesisprovidedinSupplementaryInformationsectionsA.4,C.2andC.3).

Thatsaid,ofteninmachinelearning,oncesomethingbeginstowork(evenwithclearflaws),inafewshortyearswithscale(forexample,ofcomputeanddata),bettercoremodelsandbettertechniques,thecapabilitiesofasystembecomesurprisingandcanexceedhumanperformancelevels.Inassessingtheimpactofatechnology,itis,thus,importanttokeepinminditsprobablefuturetrajectory.Crucially,thistrajectoryisnotjustaboutbettermodelsbutaboutthecomplexityofthetasksthatAIsystemscanexecute.RecentworkindicatesthatthelengthoftasksthatAIcanreliablycompleteisdoublingevery

7months

40

,indicatingthatmanycurrentimplementationanddebug-gingbottlenecksmayberesolvedinthenearterm.However,someAIweaknesseshaveprovedsurprisinglydifficulttosolve,suchasAIbeingeasilyfooled

41

,

42

andoverconfidentlywrong(hallucinations)

43

,althoughprogresshasbeenmade

44

,

45

.SuchchallengescouldpersistandwouldpreventusfromreliablytrustingtheoutputsofsystemslikeTheAIScientist.ItisalsonotcleartowhatextentAIsystemscanproducenewcreativeideasthatresemblegreatconceptualleapsinscience.StudyingandimprovingAIsystemsonthesefrontsarekeyareasforfutureresearch.

Atpresent,TheAIScientistconductscomputationalexperimentsonly.Infuturework,thissameplaybookcouldbeappliedtoothersci-entificdomainswhereonecanautomaticallyconductexperiments(orhavehumansconductthem)andcollectdatafromthem(forexample,automatedchemistrylaboratories,onwhichswiftprogressisbeingmade

46

).

Theabilitytoautomatepapergenerationraisesimportantethi-calandsocietalconcerns,includingthepotentialtooverwhelmthepeer-reviewprocess,artificiallyinflateresearchcredentials,repurposetheideasofotherswithoutgivingpropercredit,eliminatescientistjobs,orconductunethicalordangerousexperiments(Supplemen-taryInformationsectionC.3).Toconductthisstudyresponsibly,weobtainedexplicitpermissionfromtheICLRleadership,theworkshoporganizersandtheUniversityofBritishColumbia’sIRB(H24-02652).Crucially,aspartofourexperimentalprotocol,wedeterminedinadvancethatallAI-generatedsubmissionswouldbewithdrawnafterpeerreview,regardlessofoutcome.Thisdecisionwasmadetoavoidsettingaprecedentforpublishingfullyautomatedresearchbeforethescientificcommunityhasestablishedclearstandardsfordisclosureand

918|Nature|Vol651|26March2026

Article

a

b

Stage1:preliminaryinvestigation

iiftlifttidhttli

Stage3:research

agendaexecutionStage4:ablationstudies

Stage2:hyperparameter

tuning

Paperscoresgivenbythe

automatedreviewer

Non-buggyBuggy

HyperparameterAblation

ReplicationAggregationBest

Refine

Debug

n=30AIScientist:template-free

4.0

3.8

3.2

51015202530

Numberofexperimentalnodes

3.6

3.4

n=30n=30

n=30

c

IntroducestheWaterbirdsdataset.

Replacesskewedsplitswithstratifiedsampling;usespretrainedResNet.

FixesarrayerrorbyexcludingimagedatafromDataFrames.

Ablationstudies:

1.Warm-upperiodduration

2.Dynamicpenaltyfactoradaptation

3.Penaltyapplicationstrengthandsoon

Builtacolour-biasedMNISTdatasettotestifslowinglearningon

specificweightsboostsshortcutrobustness.

Tunedsuppressionstrengthusingearlystopping;enhancedvisualizations.

Fixestrainingcrash,sharpensshortcutsignalandaddstheCelebAdataset.

Rootnode

Balancesall

colour-digitgroupsintrainingandtestsplitstoavoidemptygroups.

Stage3

Stage4

Stage1

Stage2

Topc:suppressngas-earnngeauresoavosorcureance

Bestnode

Fig.3|ThephasesandcomputescalingoftheAIScientist.a,Theresearch

experimentationphaseisvisualizedasafour-stageprocess.Apreliminary

baselinecodeimplementationisfirstconstructed(stage1)andrefinedby

tuningthehyperparameters(stage2).Theresultantcodeservesasastarting

pointforexecutingtheresearchagendathroughanagentictreesearch(stage3),followedbyablationexperiments(stage4).Fulldetailsoftheagentictree

searchprocessareprovidedinMethods.b,ArealexampleoftreesearchbyThe

evaluation.Developingthesenormsisacriticalnextsteptoensurethatsuchsystemsareusedtoadvance,notundermine,scientificintegrity.Finally,moreresearchisneededtoensurethatopen-endedexploratoryAIproceedssafelyandinalignmentwithhumanvalues

47

,

48

.

ThegenerationbyTheAIScientistofanAI-authoredmanuscriptthatpassedpeerreviewforaworkshopatatop-tiermachinelearningconferencemarksamilestoneinthecenturies-longscientificendeav-our.Althoughchallengesremainintermsofconsistencyandachievingtop-tierquality,thissuccessdemonstratesthegrowingcapacityofAIforscientificreasoning,anditsignalsthedawnofanewerainwhichtheprocessofdiscoveryisnolongerasolelyhumanpursuitandinwhichthepaceatwhichweareabletoreaptheharvestofscientificdiscoverycouldacceleratedramatically.

Onlinecontent

-

-

sy

Anymethods,additionalreferences,NaturePortfolioreportingsummaries,sourcedata,extendeddata,supplementaryinformation,acknowledgements,peerreviewinformation;detailsofauthorcontributionandcompetinginterests;andstatementsofdataandcodeavailabilitareavailableat

/10.1038/s41586-026-10265-5

.

1.Lenat,D.B.Automatedtheoryformationinmathematics.InProc.5thInternationalJointConferenceonArtificialIntelligence833–842(ed.Reddy,R.)(WilliamKaufmann,1977).

2.Buchanan,B.G.&Feigenbaum,E.A.Dendralandmeta-dendral:theirapplicationsdimension.Artif.Intell.11,5–24(1978).

3.OpenAI.GPT-4technicalreport.Preprintat

/10.48550/arXiv.2303.08774

(2023).

AIScientistwithnodeannotationsoutliningtheexperimen