2026+人工智能全景报告+State+of+AI+Report+2026

STATEOFAIREPORT.

October8,2026

NathanBenaich

AIRSTREETCAPITAL.

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Abouttheauthor

NathanBenaich

GeneralPartnerofAirStreetCapital,investinginAI-firstcompanies.

profluentIIElevenLabs⃞Recursion.

nathan@

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Welcometothe9thannualStateofAIReport

Analyzingthelast12monthsofAIresearch,industry,politics,safetyandpredictions.Independentlycraftedeveryyearsince2018.

InformsconversationaroundAItodayanditsimplicationsforthefuture.Freelyavailable

atwww.stateof.ai

PeerreviewedbymembersoftopAIlabs,startups,policyandacademia.

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Whatyouneedtoknowfromthe2026StateofAIReport

Research

●Anthropic,OpenAI,andGoogleleadasbenchmarkssaturate.Betterpost-training,data,evaluations,andscaffoldingexpandagentcapabilities.

●AIacceleratesAIdevelopment:Claudeled26%ofAnthropic'smeasuredmodelR&Dundersupervision.Choosingworthwhileexperimentsisthenextfrontier.

●PhysicalAIapproachesitsGPT-2moment,openmathematicsproblemsfall,andAI-assisteddrugprogramsreachPhase3asdataandmodellicensinggrows.

Industry

●Agentuseandmonetizationbroadensbeyonddevelopers.AGeniusBarforAIcouldhelpmorepeopleextractvaluethroughbettertoolsandsetup.

●OpenAIandAnthropicreportcombinedannualizedrevenuerunratesofroughly$105Bacrosstheirbusinessesandinferencebusinessskyrockets.

●TheSaaSpocalypseexposesuncertainwinners.Financing,power,andconstructionconstrainsupply,whileourdata-centerNIMBYismpredictionplaysout.

Politics

●USmodelexportrestrictionsmakeaccessconditional.ThesuperintelligenceagendaandPentagondisputesharpenthefightoverAIcontrol.

●SelectedsovereignAIpledgestotalabout$138Bascountriesrealisetheycannotrentsovereignty.Manywillhavetobargainforaccesstothefrontier.

●Frontier-lableaderscallforslowercapabilitygains,withnoagreementonwhocanrequirepauses,authorizerestarts,andverifycompliance.

Safety

●Duringreduced-safeguardevaluations,OpenAIagentsattackedHuggingFacewhiletryingtocheattheirscorer.

●Anthropicdocumentsmisuseincyberattacks,surveillance,scams,andweaponsdevelopment.

●Frontierattacksrequirefrontierdefense.GuardrailsblockedHuggingFace'sforensics,leadingittouseaChineseopen-weightmodel.

●Reasoning-onlymonitorsmissedsabotageintests.Valuestrainingcutfictionalblackmailfrom65%to19%,withproductionreliabilityunproven.

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Section1:Research

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12monthspass,andthefrontierfightisnowathree-labrace

OnArtificialAnalysis’sIntelligenceIndex,ClaudeOpus5.5leadsat58,andGPT-6AstraandGoogle’sGemini4Argontieat53.OnArena’sdefaultranking,builtfrompeople’svotes,Argonleadsat1525,atightpackofClaudemodelsfollowsatabout1505andOpenAI’sbest,GPT-5.6Sol,ranks#20.China’sbestopen-weightsmodel,

Xiaomi’sMiMo-V2.6-Pro,scores46ontheindex.Argonissofaronlyavailabletoselectedusers.

IntelligenceIndex,bestmodelperlab

Arenadefaultview,bestmodelperlab

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Chineseopen-weightmodelsovertookAmericanonesinAIresearchpapersin2026

WeanalyzedAIpapersonarXivmentioning21modelfamilies.WhileclosedUSmodels(GPT,Claude,Gemini)

accountfor42%ofmentionsin2026,theyarelosinggroundtoChinesemodels.ZetaAlphaanalysisrevealsthatamongopen-weightmodels,Chinesefamiliessurgedfrom9%ofmentionsin2024to31%,USmodelsfellfrom31%to23%,andQwenovertookLlama.

Allmodels:shareofmentionsbyregion(%)Open-weightmodelsonly(%)QwenvsLlama(%)

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Samemodel,betterharness=strongeragent

Witheachmodelfixed,changingtheharnessdelivereda6xgainonSWE-BenchMobile,whileMeta-HarnessraisedHaiku4.5from27.5%withClaudeCodeto37.6%onTerminalBench-2.Meaningfulmodelcomparisonsrequiredisclosingtheharnessandholdingitfixedorvaryingitsystematically.

●OnWeaveBench,LongHorizon-HarnessimprovesQwen3.7-Plusenoughtoexceedthebaseline

performanceofOpus4.7.

●Acontrolledtestpairedthreemodelswiththree

harnesseson100SWE-benchVerifiedtasks.GLM-5.1rosefrom52.5%to65.5%betweentheminimalandfullharnesses.Harness-inducedperformance

variancewas

7.8xmodel-inducedvariance

inthisexperiment.

Figure2(b),mathpanels.200taskspermodel.Circleareasareschematic.

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Agentsimprovebychoosingamongspecializedharnesses

Asingleharnesscanworkwellonaverageyetmisstasksthatneedadifferentapproach.ResearchersatMeta,

DukeandUCDavisevolvetwoharnessesondifferentdevelopmenttasks,thenchooseoneforeachnewproblem.Withtheexecutionmodelheldfixed,thisimprovesheld-outmathandcodingresultsoverMeta-Harness.

●WithGemini3Flash,thebranchesshare47%coverage,buteachsolvessomeproblemstheothermisses.

●Therouterreaches62%onGeminimathversusMeta-Harness's46%,belowthebranches'

combinedpotentialcoverageof67%.

●Codingalsoimproves:Terminal-Bench2.0risesfrom44.8%to50.0%,andSWE-benchLitefrom63.6%to66.0%,withSonnet4.5fixed.

Differentproblemssolvedbyeachbranch

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Recursivelanguagemodelstreatpromptsaspartsoftheenvironment

Longinputscanoverwhelmamodelevenbeforetheyfillitscontextwindow.MIT'sRecursiveLanguageModels(RLMs)keeptheinputinacodeworkspace,wherethemodelcaninspectitanddelegatepiecestofurther

(recursive)modelcalls.Thisletsafixedmodelworkthroughinputstoolargetoreadinonepass.

●TheauthorstrainedQwen3-30B-A3BwithRLon

shorttasksandfoundtransfertomuchlongerinputsandnewdomainssharingadecompositionstrategy.

●Theauthorsexplainthisasturningunfamiliartasksintofamiliarsubproblems:eachmodelcallstays

“locallyin-distribution”withitstraining.

●Transferdependsonlearningareusable

decomposition,andRLMsarenotuniformlycheaper.Recursivecallsusefixedweightsatinference.

Readlargeinputsthroughcodeandsmallermodelcalls

Storetheinputoutsidethemodel

Keepdocumentsinapersistentcodeworkspace

Inspect,splitanddelegate

Codecallsmodelsonselectedpiecesoftheinput

Saveintermediateanswersandassembletheresponse

Combinetheresults

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Skillsandmemoryletagentsreuseknow-howwithoutretraining

Skillspackagereusableinstructionsandcode,whilememorypreservesinformationforlatertasks.Theselet

developersimproveanagentbetweenmodelreleasesbychangingwhatitcanreuse.Papersmatchingthebroadskillsqueryrosefrom152to1,486inJanuary-August2025and2026.

Memorytoolmatches,Jan-Aug2026

Skillspapermatches,Jan-Aug2025vs2026

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Karpathy’sautoresearchpopularizedtherushtorecursiveself-improvement(RSI)

RSIisthepursuitofAIthatbuildsbetterAI,autonomously.InMarch,AndrejKarpathyreleasedautoresearch,a

projectinwhichanagenteditsthetrainingcodeofasmallnanochatmodel,trainsforafixedfiveminutes,keepswhatimproves,andrepeatsroughly100timesovernightononeGPU.Theuseredits

program.md

,theinstructionsthatdefinetheresearchorg.AsKarpathyputit:“Thisrepoisthestoryofhowitallbegan.”

●Thereporeachedroughly95,000GitHubstarsand13,400forksin5months.

●Afixed5-minutetrainingbudgetmakeseveryexperimentcomparableandturnsoneGPUintoroughly100experimentsanight,scoredonvalidationbitsperbyte.

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We’reseeingarapidgrowthinself-improvementpapers

Papersmatchingverifiablerewardsgrew10.4xinJanuary-August2026versusthesamemonthsof2025,

comparedwith2.7xforRSI.Thepracticalopportunityistoautomateexperimentswithreliablescoring.Choosingvaluableresearchquestionsremainsaseparate(andveryvaluable)problem…

NarrowRSIquery:2.7x

Growthinpapermatches,Jan-Aug2026/2025

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Agentscanimprovetheirownscaffolds,butaccelerationisunproven

DarwinGödelMachinerewritesanagent’sscaffold:theprompts,toolsandworkflowaroundafrozenmodel.

Hyperagentsalsorewritetheprocedurethatmakesthoseedits.Thiscanautomatemoreofagentengineering,butabetteragentdoesnotnecessarilybecomeabetterinventoroffutureagents…yet!

●Huxley-GödelMachineselectsagentsbytheir

descendants’scores.WithGPT-5,itfixed57.0%ofSWE-benchLiteissuesvs.SWE-agent’s56.7%.

●RedQueenco-evolvesagentsandevaluators.Itspaperacceptancerateroseto38.8%vs.HGM-H’s21.8%at

matchedsearchcost,asratedbyAIjudges.

●Wecoreportsbeatingitshand-tunedagentin100

trials.Usingthenewagenttoimprovefurtheragentsgavenosignificantefficiencygain,however.

Makingtheimprovementprocedureeditable

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Strongermodelscanoutgrowtheirharnesses

Manyharnesseshaveincludedelaboratebehavioralrules,remindersandplanningmechanismstocompensateforoldmodellimitations.However,asmodelsbecomemorecapableandsuccessfulagentbehavioristrainedback

intothem,theseworkaroundsbecomeredundantandcanevenhobblestrongermodels.Harnessesshouldprovideinterfacesthatmaketheenvironmenteasiertoacton,withoutaddingredundancyinmodelbehavior.

●ClaudeCoderemoved80%ofthesystempromptforadvancedmodels,withnomeasurablelossoncodingevaluations.Few-shottooluseexamplesarealsofoundtoreduceexploration,withgoodinterfacedesignbeingmoreimportant.

●Harnesseshavebecomecurricula,wheretrajectoriesproducedinsideanevolvingharnesscanupdatemodelweights(seeContinualHarness).RecentGPTmodelsaretrainedonthecodexharness.

●Performancebetweenlightweightandsophisticatedharnessesshrinkasmodelcapabilityimproves:thedurableharnessinvestmentsthatremainareenvironmentinterfaces(tools,MCPservers),state

management,observabilityandagentinfrastructure(sandboxes,runtimes,etc).

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AgentsapproachofficialinstructscoresonPostTrainBench’srevisedevaluation

PostTrainBenchtestsautonomouspost-training:eachruntargetsonesmallbasemodelandonebenchmark,with

10hoursononeH100.Version1.2coversfourbasemodelsandsixbenchmarks.Resultsmeasuretask-specific

optimization;theofficialinstructmodelsarereferencepointstrainedwithdifferentresources.

●Onv1.2,Fable5.1scores44.6%,Opus5.543.8%andGPT-6Astra41.9%,versus48.4%forofficialinstructmodels.

●BFCLwasremovedaftertask-qualityand

item-targetedtrainingconcerns.Theformer95%resultisinsufficientevidenceofgeneral

function-callingsuperiority.

●Thesearebenchmark-specificmodeladaptations,

notonebroadlyimprovedassistant.FiveFable5.1

GPQArunsOpusasa.

used5fallback

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Frontieragentssustainmulti-dayresearchwithlimitednoveltyinaspeedrun

PrimeIntellecttested18modelsin153runsonthenanoGPToptimizerspeedrun:traina124M-parameterGPTtoatargetlossof3.28infewersteps.Runsused8xH200GPUswithoutinternet.Fable5'sbesttrajectorylasted

8.7days,withlimitednoveltyreportedinthissetting.

●Fable5reached2,726stepsfroma3,290-stepbaseline,closing81.7%ofthegaptoa2,600-stephumanrecordclaim.

●Researchbudgetsdiffered:after24hours,Fable5hadreached3,010steps,versus3,045forOpus5.

●Strongagentsre-testedborderlineresultsacrossseedsandrevisitedearlierfailures,butlimitednoveltyheredoesnotestablishagenerallimitonAIresearch.

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Canagentsproduceatop-tierresearchpaper?No,buttheycandoitsengineering.

Researchersintroducedshadowevaluations,inwhichtheygiveanagentthecentralresearchquestionofahigh-qualityunpublishedNeurIPSsubmission(socontaminationisimpossible)andletthepaper’soriginal

authorsgradetheoutputasreviewers.Eachagentgotsixdays,$3,000ofAPIcredit,GPUsandtheopenweb.

●Opus4.8completedalltheengineeringwithouthumanhelp,butbothitspaperswerescored2/6and1/6-

unambiguousrejections.

●Fivefailuremodesrecurred:nosenseofthepublishablebar,uncreativefixes,ineffectivebacktracking,poor

resourceawarenessandinstructiondrift.

●BothrunsendedwithunderhalftheAPIbudgetspent,despitethemodelsknowingtheirusageinreal-time.

●Self-reviewbyagentsneverreturnedanacceptancein

weresameonesthehumanreviewerslaterraised.

dozensofroundsandtheissuesitflaggedoftenthe

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Theworkofsmartermodelsisincreasinglyacceptedbylab’sstaff

InanessayonbuildingRSI,Anthropic(leftchart)shareddataonthecodecontributionsoftheiremployees.Theoutputhasincreasedby8xinQ22026comparedtopre-2025,correspondingwiththeuseofMythosPreview.OpenAIseesthesameresultstoo(rightchart).

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…andstartssuggestingwheretheresearchshouldgonext

Evenmoreinterestingly,researchersratednext-directionturnssuggestedbyMythosPreviewasbetterthantherouteahumanresearcherpicked64%ofthetime.Thissuggeststhatfrontiermodelsaregeneratingquality

contributionsthatexhibitresearchtaste.

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ClaudenowleadsaquarterofAnthropic’smodelR&D,withhumanssupervising

Anthropic’sinternalautomationindextrackshowmuchresponsibilityClaudetakesonacrossmodelR&D.Thesharerated“AIleads”rosefromunder1%inFebruaryto26%inAugust2026:Claudecompletesmostofataskfromahigh-levelpromptwhileahumansupervises.Morethan90%ofmeasuredworknowinvolvessubstantialAIcollaborationorleadership,butnoneisfullyautonomousyet.

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Within6months,OpenAIresearchersaresolvingmuchlongertasksautonomously

InJanuary2026,OpenAIresearchersusedAIagentstoautonomouslycompletetasksnormallyrequiring4-8hoursofhumanlaborwithan18%successrate.ByJuly2026,thatsame18%successratewasachievedonsignificantlymorecomplextasksrequiring32-64hoursofhumanlabor.

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Butcodingagentsaremostlyusedpost-experimentalideationanddesign

Sofar,themajorityofcodingagentuseinOpenAIinternalresearchhasbeenonexecution-relatedworkflows,namelyworkingonresearchinfrastructurecode,launching,monitoringanddebuggingruns,andtechnicalhelpandreview.Decidingwhattoresearchandhowtodoitisstillnotsolvedbycodingagents.

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Astaskbenchmarkssaturate,RSIevidenceismovinginsidethelabs

PublicAIR&Dsuitesnolongerruleoutadvancedcapabilitiesoncemodelspasstheirthresholdsorexploittheirscoring.Labsincreasinglyrelyoninternalevidenceofresearchacceleration.RSI-Examtests88tasksexternally,butnomodelreachesitsfrontier-calibratedreference.

●METR’sOpus5.5reviewcitesapreliminary

estimateof~1.5xAI-drivenresearchacceleration.Theestimatedtimeperiodisunspecified.

●OpenAIreports3.1agent-workdaysperhuman

workday.Thismeasuresactivity,notanequivalentgaininresearchoutput.

●NoamBrownestimatesabouta3xspeedup,limitedbyserialexperimentsandGPUsupply.

RSI-Exam:88tasks

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Lessshootinginthedarkasmoreofthepretrainingrecipegotwrittendown

Detailedtechnicalreports,codebasesandtraininglogsmakebuildingcapablemodelslessopaquethanthreeyearsago.Moreofthepathfrompretrainingtoreasoningandagenticbehaviorisnowpublic.Butscaling-law

guidanceisstillincomplete,andchoosingtherightdatamixremainsexperimental.Trainingastrongmodelstilltakesresearchjudgment(theinfamous“taste”)aswellasenoughcomputetotestmanyideas.

●Trainingbecomesmoreselective:Nemotron3Superuses20Ttokensforbroadcoverage,then5T

emphasizingquality.Step3.5Flashlateremphasizescode,reasoningandtooluse,retainingsomegeneraldataasitspecializes.

●Longercontextcanweakenotherskills.Aftermathscoresdippedduringmillion-tokentraining,NVIDIAaddedaphasealternating1M-tokenand4K-tokensequencestomitigatethedecline.

●Healthytraininglosscanhidefailingcomponents.StepFunfoundMoEexpertsbecominginactiveor

developingextremeinternalvalueswhileaggregatemetricslookednormal.Monitoringindividualexpertsexposedtheseproblems.

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TheRLrecipegotwrittendowntoo

Severalwell-documentedopenreproductionsofagentictrainingpipelineswerepublishedthisyear,achievingresultsclosetothestateoftheart.Whileopenagentmodelreproductionsremainafewstepsbehindthe

frontier,thetrickstheydiscloselowerthebarrierforanewlabtostartsuccessfullytraininganagent,withThinkingMachinesandOpenThoughtsAgentbeinggoodexamples.

●Meta’sScaleRLreports400k+GPUhoursofablationscoveringprecisionfixes(fp32logits),RLobjectivesandbatchsize.Manyfindingsreverseconclusionsfromsmallerscales.

●Cursor’sComposer2technicalreportincludesmanylong-horizonrolloutdetailssuchasupdatingbehaviourpolicyweights

mid-rollout,andfixesforsampler/trainerinconsistencysuchasMoE

routerreplay.

waysto

.

●DeepSeekMath-v2sharestheiragenticdata-generationpipelineindetailKimiK3detailstrainreasoningefforts,compatibility

withmultipleharnesses,andanti-reward-hackingmachinery.

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ScalingagenticRLcreateshugedemandforCPUsandmemoryalongsideGPUs

Long-horizonagenticRLspendsmuchofitscomputeoninference(rollouts)ratherthanbackprop,runsstatefulrolloutsagainstrealisticoftensandboxedenvironments,andmustcontinuouslysyncfreshweightsinto

distributedinferencefleetswithoutlettingtrajectoriesgostale.

●InferencedominatesRLcompute:MAI-Thinking-1,a1T-A35BMoE,uses4,096of4,864GB200sforinference,roughlyfivetimesitslearnerallocation,withgenerationsupto128ktokens.

●CPUandmemorydemandrisestoo:KimiK3used51.2Mstatefulsandboxeswithpause/resume,fork,

snapshot,andsub-secondlaunch.DeepSeek’sDSecreports30,000CPUcoresand250TBofRAMserving3Msandboxinstancesdailyinonedeploymentunit.Yet~90%ofsandboxesaverage≤5%ofrequestedCPUcapacity,makingresourcesharingandidle-memoryreclamationessential.

●Trainingenvironmentsincreasinglyresemblerealwork:KimiK3combinesmocksofGmail,Notion,Slack,andCanvaswithharnessesincludingKimiCode,ClaudeCode,andCodex.

●Inferencecanbedistributed:Composer2synchronizesweightseverytrainingstepthroughS3.Delta

compressionreducesupdatesfora1T-parametermodeltoafewGB,enablinggeographicallydistributedRLinference.

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Thetraininggymgetsharderastheagentgetsbetter

RLagentslearnlittlefromtaskstheyalwayspassoralwaysfail.Microsoft'sTaskPilotgeneratescodingproblemsandhiddentestsinrealrepositories,thenrevisestheirdifficultyusingthecurrentmodel'sattempts.AsFrogNanoimproves,newtaskbatcheskeepitstrainingneartheedgeofwhatitcansolve.

●Agent-Worldgenerated1,978environmentsand19.8ktoolstotrain8Band14Bagents,improvingtooluse,planningandcodingacross23benchmarks.

●FrogNanocombinesasimplertoolinterfacewithfive

batchestotaling1.5ktasks.Qwen3.5-4Bsolves61.5%ofSWE-benchVerified(validation)and37.6%ofheld-outSWE-benchProissues.

●Checksstillmatter,ofcourse.WithGPT-5.2fixed,UniversalVerifierfalselyaccepted1%offailed

computer-useruns,vs.WebJudge

WebVoyager.

7%forand10%for

Computer-usefalsepositives(internalset)

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Modelscanlearnfromstrongerteachers,specialists,orthemselves

Distillationteachesastudenttoimitateateacher.Off-policyusesteacherorearlier-modelresponses,whileon-policyusesthecurrentstudent’sattempts.Self-distillationmeanstheteacherisacopyofthesamemodel.

●Cheapertransfer:AlibabacomparedtwomethodsfromthesameQwen3-8Bcheckpoint.On-policydistillationreached74.4%onAIME24using1.8kGPU-hours.Bycontrast,RLneeded17.9kGPU-hourstoreach67.6%.

●Combiningspecialists:Xiaomitrainsmath,codingandinstruction-followingteachersseparatelytoavoidinterference,thencombinestheirskillsviaMulti-TeacherOn-PolicyDistillation(MOPD).

●Learningfromitself:Self-DistilledReasonergivesafrozencopytheworkedsolution.Itguidesthestudenttokenbytokenonthestudent’sownattempt,sonolargerteacherisrequired.

Howon-policyself-distillationworks

Studentprobabilities

Matchdistributions

CurrentstudentQuestion+prefix

Frozenteacher

Question+solution

+sameprefix

CurrentstudentQuestiononly

Generate

Student’sownattempt

Teacherprobabilities

Updatestudentonly

Prefix=attemptsofar.Samestartingcheckpoint.Teacherprobabilitiesarenotcorrectnessscores.

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Frontierlabs'owncheapermodelsdecodedthereasoningtheytriedtohide

Labshidetheirmodels'reasoningbecauserivalscantrainonitusingaprocesscalleddistillation.Panfilovandcolleaguesfoundthatcheapermodelscouldrevealstrongermodels'hiddenreasoning.Providerspatchedtheflaw.Couldrestrictingaccesstothesetraceswidenthegapbetweenopen-weightandclosedmodels?

●GivenOpus4.8'sencryptedreasoningblock,Haiku4.5wroteoutthereasoninginplaintext.Thesame

attackworkedatOpenAIandGoogle.

●Areasoningtraceshowstheteacher'ssteps,notjustitsanswer.Inpriorwork,reconstructedtraceslifteda7Bstudentfrom68.4%to76.0%onMATH500.

●Closingthisextractionroutecouldmakefrontierreasoninghardertoobtainfortraining.Thepaperdoesnotestablishhowmuchrecentopen-modelprogressdependedonit,orwhetherthecapabilitygapwillwiden-wesuspectitcould.

Howhiddenreasoningwasexposed

1.Opus4.8generatesreasoning

APIreturnsanencryptedreasoningblock.

2.ReplaytheblocktoHaiku4.5

ThesameprovideracceptsOpus'sblock.

3.Haikurevealsthehiddenreasoning

Apromptinducesittowritethetraceasplaintext.

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Self-playcanlearnfromdocumentsorfromprogramsitinvents

Self-playcreatesitsowntrainingtasks,soisanappealingapproachtoAItraining.SPICEpost-trainsareasonerondocument-groundedquestions,whereaszero-dataself-playlearnsfromprograms,startingfromrandomweights.Naturaltext/DNAvalidationselectshyperparametersbutsuppliesnopretraininggradients.

SPICE:document-groundedpost-training

Qwen3-4B-Base:35.8%→44.9%overall(+9.1pp)across11reasoningbenchmarks.R-Zero:39.5%;

AbsoluteZero:40.7%underthesameevaluation.

Zero-dataself-play:syntheti