Aspire：模型能否从模糊目标中自我进化

llByteDanceIseed⃞ATokenwave

1

Aspire:CanModelsSelf-EvolvefromVagueGoals?

1ByteDanceSeed,2SingaporeUniversityofTechnologyandDesign,3M-A-P,4TokenWave.AI

FullauthorlistinContributions

arXiv:2608.31111v1[cs.CL]31Aug2026

Abstract

Manyimportantformsofhumanlearningbeginwithavaguegoal,suchas“becomeabetterphysicist”or“improveatresearch.”Learnersmustinterpretthegoal,identifycapabilitygaps,decidehowtolearn,anddeterminewhethertheyhaveactuallyimproved.Incontrast,existingworkonLLMself-evolutiontypicallybeginswithtasksandevaluationmetricsspecifiedbyhumans,reducingself-evolutiontooptimizinganexplicitobjectiveratherthandecidingwhatandhowtolearn.WeintroduceAspire,abenchmarkforvague-goal-drivenself-evolution.Aspireprovidesonlyanatural-languagecapabilitygoalwhiledownstreamevaluationtasksremainhidden.Theagentmustoperationalizethegoalbychoosingdataandupdatemethods,constructingtrainingandvalidationsignals,anddecidingwhentoevaluate.Aspiresupportsbothmodel-weightandagent-harnessevolutioninaunifiedinteractiveenvironmentandevaluatestheresultingsystemsonahidden,expert-authoredsetof520itemsspanningsixgoals.Ourexperimentsshowthatvaguegoalsredirectsearchefforttowardgoalinterpretation.Currentagentsroutinelycompletetrainingandharness-editingloops,butweight-levelgainsremainsparseandunstable,andthestrongestevolvedharnessremainsbelowtheengineeredQwen-Agentreference.Agentsoftentrainonmismatcheddataandtrustnarrowself-evaluations,solocalgainsfailtotransfertohiddenevaluationandcontinuedsearchandtrainingcaneraseearlierimprovements.

Date:September1,2026

ProjectPage:

https://self-developing-agents.github.io/

1Introduction

Manyimportantformsofhumanlearningbeginwithabroadcapabilitydirectionratherthanapredefinedbenchmark,trainingset,orfullycomputablerewardfunction.Astudentseekingtobecomeabetterphysicist,forexample,notonlychoosestextbooks,exercises,andstudymethods,butalsoidentifiesgapsintheirknowledge,prioritizeswhichcapabilitiestodevelop,anddetermineswhetherlearninghasproducedgenuineprogress.Autonomouslearningthereforeinvolvesthreecoupleddecisions:whattoimprove,howtoimproveit,andhowtoverifytheimprovement.

ExistingworkonLLMself-evolutionfocusesprimarilyontheseconddecision.Givenaconcretetask,evaluationscript,andsuccessmetric,LLMagentscanalreadycollectdata,runtraining,andrevisepost-trainingstrategiesfromfeedback.PostTrainBench[

20

],LaMDAgent

[29

],Evo-Memory

[25

],andSEAL

[37

]showthatmodernLLMscanautonomouslysearchforeffectiveoptimizationpathstowardaspecifiedobjective.Thesesystemsthereforebeginafterhumanshaveoperationalizedabroadcapabilityrequestsuchas“improvemathematicalreasoning”intoafixedtask-levelobjectivesuchas“improveperformanceonAIME”(Figure

1

(a)).Thetaskformat,difficultyrange,metric,andsuccesscriterionarealreadydefined:theagentsearchesoverhowtoimprove,butnotwhatcapabilityobjectivetopursue.Whenexistingbenchmarkssaturateorceaseto

2

(b)Vaguegoal

Theagentmustfirstdecidewhattooptimize,thensearchhowtoimproveit.

Humanstatesdirection

AgentdecidesWHAT+HOW

Broadcapabilitydirection

“Improvedifficult

mathematical

reasoningandjustification.”

uitliir,

UpdatedmodelM1

Agent-builtproxy↑

Competitionproblems

Proof/

justification

Unfamiliar

reasoning

tasks

Transferacrosshiddenmathtasks?

Proxygain≠transferablegrowth

Evaluationitemsremainhidden

Formandoptimizetheobjective

Data→Update→Validate↻

Dise-De-Buiing

(a)ExplicittaskHumanoperationalizestheobjective;theagentsearcheshowtoimprove.

ExplicittaskdefinitionOptimizeagivenobjective

Taskformatandcomposition

DifficultyanchorDataUpdateAIMEscore

Metricandsuccesscriterion

HumandefinesAgentsearches

WHATHOW

PredefinedexecutabledestinationsearchmainlyoverHOW

UpdatedmodelM1

AIMEscore↑

Successonthepredefinedtask

Hiddenevaluationaggregateonly

Figure1Fromexplicit-taskoptimizationtovague-goal-drivenself-evolution.(a)Withanexplicittaskdefinition,humansspecifythetaskformatandcomposition,difficultyanchor,metric,andsuccesscriterion,leavingtheagenttosearchprimarilyoverhowtoimproveafixedobjective.(b)Withonlyabroadcapabilitydirection,theagentmustdiagnosecapabilitygaps,decomposesub-goals,constructlearningandvalidationsignals,andtherebydecidewhattooptimizeaswellashow.Officialevaluationitemsremainsealed;externalevaluationtestswhethergainsonanagent-builtproxytransfertotheintendedcapability.

reflectemergingcapabilitygaps,humansmuststilldiscovernewweaknesses,translatethemintotasks,anddesigncorrespondingbenchmarksandrewards.Thisleavesacentralquestionforsustainedrecursiveself-improvement:

Givenonlyavaguegoalandnoagent-visibletaskspecificationordecomposablereward?

Weformalizethisproblemasvague-goal-drivenself-evolution.AsillustratedinFigure

1

(b),“vague”doesnotmeanambiguousorpoorlyspecified.Itdenotesabroadcapabilitydirectionthathasnotyetbeenoperationalizedasafixedtask-levelobjectiveandevaluationmetric.Theagentmustthereforediagnosecapabilitygaps,formintermediateobjectives,constructlearningandvalidationsignals,andjointlysearchoverwhattooptimizeandhowtooptimizeit.Thisexpandeddecisionspaceintroducesadeeperfailuremode:gainsonanagent-constructedproxymaynottranslateintogenuineimprovementsintheintendedcapability.Evaluatingvague-goal-drivenself-evolutionthereforerequiresagoal-alignedexternalevaluatorwhosetask-levelspecificationanditemsremainhiddenfromtheagent.

Tostudythisproblem,weintroduceAspire,abenchmarkforvague-goal-drivenself-evolution.Aspireprovidesonlyanatural-languagecapabilitygoalwhiledownstreamevaluationtasksremainhidden.Theagentdecideshowtooperationalizethegoal,includingwhatdataandupdatemethodstouseandwhetherandhowtoperformintermediatevalidation.Aspiresupportsevolutionattwolevels:modelweightsandthesupportingagentharness.Aunifiedagent-facingtoolallowstheagenttosearchfor,download,import,orsynthesizedata;launchdifferentformsoftraining;runself-evaluations;andmanagecandidatebranchesandselection.Thecontrollerhandlesdatamanagement,jobscheduling,resourceisolation,andcheckpointverification.Aspirerecordstheresultingdecisions,actions,andself-evaluationtrajectories,andevaluatestheresultingsystemsonahidden,expert-authoredsetof520itemsspanningsixgoalsacrossknowledgeexpansionandcapabilitystrengthening.Dependingontheprotocol,theagentreceiveseithernointermediateevaluationscoreoronlyboundedaggregatescores;itneveraccessesevaluationitems,referenceanswers,orper-itemfeedback.

Weorganizeourexperimentsasaprogressionfromcontrolledcomparisontoautonomousevolution.RQ1isolatestheeffectofgoalspecificationbyreplacinganexplicitpost-trainingtaskwithavaguecapabilitygoalundermatchedstartingmodels,updatetools,andcomputebudgets.RQ2thenmovestothefullAspiresettingandaskswhetheranagentstartingfromaninstruction-tunedcheckpointcanconvertavaguegoalintoaretainedweight-levelcapabilitygain.RQ3holdsmodelweightsfixedandshiftstheobjectofevolution

3

tothesupportingagentharness.

RQ1:Whatchangeswhenanexplicitpost-trainingtaskisreplacedbyavaguecapabilitygoal?Vaguegoalsredirectsearchefforttowardgoalinterpretationandoperationalizationand,intheevaluatedsettings,yieldloweraggregateoutcomesthanthecorrespondingexplicit-taskreferences.

RQ2:Startingfromaninstruction-tunedcheckpoint,cananLLMturnavaguegoalintoaretainedcapabilitygainthroughself-directedweightupdates?Agentsroutinelycompletedataselection,training,andcheckpointgeneration,butabove-baseimprovementsonhiddenevaluationdataremainrareandarenotreliablyretainedthroughcontinuedsearch.

RQ3:Beyondmodelweights,cantheagentharnessitselfevolve?Withmodelweightsfixed,agentscangeneratefunctionalsuccessorharnessesforgoalinterpretation,tooluse,andself-evaluation,buteventhestrongestobservedsuccessorremainsbelowtheengineeredQwen-Agentreference.

Ourcontributionsarethreefold.First,weformalizetargetoperationalization—theconversionofabroadcapabilitygoalintotrainableobjectives,learningsignals,andvalidationcriteria—asamissingaxisofautonomouspost-training.Second,weintroduceabenchmarkwithsealedevaluationandaminimalinteractiveenvironmentforbothmodel-weightandharnessupdates.Third,weprovideoutcome-and-trajectoryevidencerevealingthegapbetweenexecutingupdatesandretainingtarget-alignedimprovements.

2Background

Mostagentbenchmarksbeginaftertheproblemhasalreadybeenmadeexecutable:thetaskisspecified,therewardorjudgeisdefined,andtheexecutionscaffoldisfixed.Thissettingisnecessaryforcontrolledcomparison,butithidestheworkthatdominatesrealdeployment.Inindustry,thatworkisdistributedacrossseveralroles—includingsolutionsarchitects,appliedengineers,andplatformengineers—butitsmostvisiblerecentcrystallizationistheforward-deployedengineer(FDE),atitlepopularizedbyPalantirandsinceadoptedbyfrontier-modelcompanies[

18

,

19

].AnFDEisembeddedinthedeploymentenvironmentandturnsageneral-purposemodelintoasystemthatworkswithacustomer’sdataformats,workflows,andoperationalconstraints.Therole’ssuccesscriterionisnotademonstrationorabenchmarkscore,butwhetherthedeployedsystemisgenuinelyused,continuestowork,andimprovesinresponsetofailures.ThegrowthofFDErolesacrossfrontier-modelanddata-platformcompaniesreflectsasimplefact:acapablemodelisnotyetaworkingsystem

[1

,

23

],andtodaythegapislargelyclosedbyhumanengineers.

Viewedfromthemodel’sside,FDEworksuppliesthreepiecesofstructurethatbenchmarkdesignersnormallypresuppose.First,thegoalisvague:aninformaldeploymentneedmustbetranslatedintoconcreteobjectives,constraints,andsuccesscriteria.Second,thefeedbacksignalmaybeabsentorunreliable:tests,judges,traces,orothervalidationmechanismsmustbeconstructedbeforeanyonecandeterminewhetherthesystemisimproving.Third,theexecutionsystemmaynotexistinausableform:thetools,contextmanagement,state,lifecyclelogic,andverificationinterfacethroughwhichfuturetaskswillrunmustbebuilt,adapted,andmaintainedasrequirementschange.

AspⅠREfocusesonthefirstlayerandthecapability-improvementprocessitinitiates.Whenadeploymentneedspecifiesonlyabroadcapabilitydirection,canamodeldeterminewhatitshouldlearn,translatethatdirectionintodata,trainingplans,andvalidationsignals,andachieverealcapabilitygrowthbyupdatingitsmodelweightsoragentharness?WhereasS3Gymstudieswhetherinteractionexperiencecanbejudgedandreusedunderexecutableverification,andHARNEssDEvstudieshowmodelsbuildandmaintainthesystemsthatcarrythem,AspⅠREisolateshowbroaddeploymentneedsaretranslatedintoconcretelearningobjectivesandrealizedascapabilitygrowth.

3Aspire:Self-EvolutionBenchmarkandInteractiveEnvironment

Self-evolutioncanonlybestudiedifprogressremainsexternallymeasurable.AspⅠREthereforekeepsanevaluatorascontroller-sidescientificinstrumentationwhilewithholdingitsbenchmarkdefinition,items,and

4

decomposablerewardfromtheagent.Boundedaggregateoutcomesapproximatesparsedeploymentfeedbackwithoutturningtheevaluatorintoanagent-visibletaskspecificationorsourceofdirectlytrainablesupervision.

Thissectioninstantiatesthatsettingthroughthreecomponents.Wefirstdefineaboundedevolutionepisodeandthemodelandharnesssurfacesonwhichitmayoperate(Section

3.1

).Wethenpresentahiddenevaluationsetspanningsixgoals(Section

3.2

)andtheminimalinteractiveenvironmentthroughwhichanagentconstructs,tests,andsafelyretainsupdates(Section

3.3

).Experiment-specificfeedback,eligibility,resource,andreleaseaccountingappearswiththeRQ2resultsinAppendix

C

.

3.1TaskDefinitionandEvolutionSurfaces

Campaignandround.LetG∈Gdenoteavaguegoal:anatural-languagecapabilityobjectivethatdoesnotspecifyatrainingtask,dataset,oroptimizationprocedure.Unlikeanexplicit-tasksetting,theagentmustperformgoaloperationalizationbytranslatingGintoitsowndata,updateobjective,andvalidationcriteria.Acampaignfixesthecontroller-sideexperimentcontract

Γ=(G,EG,J,A,B,Σ),Yrin=(Mr,Hr,Dr),

whereEGistheversionedevaluatorboundtoG,Jisitsjudgewhenmodel-basedscoringisrequired,Aisthetypedactioncontract,Bisthecampaignbudget,andΣisthepredeclaredterminalselectionrule.TheincomingstatecontainsmodelweightsMr,anagentharnessHr(theruntimeinstructions,toolpolicy,workflow,memory,andvalidationlogicaroundthemodel),andthedecisionmodelDrthatdirectsthesearch.Theevaluator—includingitsitems,answers,rubrics,routingmetadata,andscoringconfiguration—existsthroughoutthecampaignbutisneverpartoftheagent’sobservation.RQ1isthevague-goalcounterpartofPostTrainBench:itpreserveseachoriginalsealedtaskevaluatorwhilereplacingtheexplicitbenchmarkidentifierwithabroadcapabilitydescription.Thesetask-specificresultsremainseparatefrom,andarenotpooledwith,thesix-goalAspireevaluationusedinRQ2andRQ3.

Anevolutionroundisaboundedsearch-and-commitepisodeduringwhichthedecisionmodelisfixed,ratherthanonetoolcall.Formally,Dr,j=Drforeveryinteractionstepjinroundr.Togetherwithafixedcreator-sidescaffoldCr,DrinterpretsGandmayproposemultipledataoperations,updates,validations,branches,andcandidatestatesbeforetermination.Differentcandidatesthereforeembodydifferentgoaloperationalizations.Aftertheroundterminates,thecontrollermayreuseDrorexplicitlypromoteaverifiedtraineddescendant.WritingDrforthesetofsuchdescendants,thenext-roundchoiceobeysDr+1∈{Dr}∪Dr;anyhandoffbeginsonlyinthesubsequentround.

Releasedinteraction.LetEr,jbetheprivatecontrollerstatebeforeinteractionstepj.Theagentreceivesonlyareleasedviewqr,jcontainingthepublichistory,lifecyclestatus,legalrequesttypes,approvedidentifiers,andaboundedprojectionoftheremainingbudget.Ateachstep,DrandCrproposeanactionfromthegoalandreleasedhistory,andthecontrollervalidatestherequestbeforeupdatingitsprivatestate.Acceptedrequestscreatethecorrespondingdurabletransition;rejectedrequestsreturnatypederrorwithoutcreatinganartifactorscore.Detailedfeedbackisrestrictedtotheagent’sownvalidationdata;evaluationfeedbackfollowstheprotocol-specificinformationboundaryinSection

3.2

.

Twoevolutionsurfaces.Aspireseparatesthecomponentbeingevolvedfromthepolicydirectingthesearch.Thetworeportedsurfacesare

SM(H0,Dr)={(M,H0,Dr):M∈M},SH(M0,Dr)={(M0,H,Dr):H∈H}.

Table1EvolutionsurfacesinAspire.ThecomponentunderstudychangeswhileDr,thecontroller,andtheevaluatorremainfixedwithintheround.

Setting

Mutablecomponent

Fixedduringevaluation

Weightevolution(RQ1–RQ2)

ModelweightsM

H0,Dr,controller,evaluator

Harnessevolution(RQ3)

AgentharnessH

M0,Dr,controller,evaluator

5

Everyweightcandidaterecordsitsparentcheckpoint,registereddata,updatespecification,andprovenancefortheagent’sownvalidationdata.Everyharnesscandidaterecordsitsparentversion,contenthash,andeditprovenance.WeuseSelffortheconfigurationinwhichthefrozenbasecheckpointactsasthedecisionmodelD0,whilecandidateupdatesdescendfromthesamecheckpointM0.Promotionofatraineddescendantisanexplicitbetween-roundoperationandneveranautomaticwithin-roundreplacement.

3.2AHidden,Query-LimitedEvaluationSet

Thissubsectiondescribesthesix-goalhiddenevaluationusedinRQ2andRQ3;RQ1insteadusestheoriginalsealed,task-specificPostTrainBenchevaluatorsunderavague-goalcontract,withoutpoolingthoseresultsintoAspire.

Measurementwithoutanagent-visiblebenchmark.Learningfromavaguegoalcannotbeevaluatedwithoutanexternalcriterionofprogress.Aspirethereforeretainsabenchmarkonthecontrollersidewhilehidingitstask-levelspecificationfromtheagent.Thehiddenevaluationsetisscientificinstrumentation,notanagent-visiblelearningcontract:itletsresearchersmeasurewhetheranagent-selectedupdateimprovestheintendedcapabilitywithoutdisclosingwhichtasksdefinesuccess.Itcontains520expert-authoredevaluationitemscoveringsixvaguegoals;eachgoalisevaluatedonitscorrespondingitems,whichtheagentneversees,andtheprotocolreturnsnoitem-levelresult—onlyanaggregatescorewhenfeedbackispermitted.

Itemsforsixgoals.The520itemscoverthesixgoals:scientificandacademicreasoning(75items);humanitiesandsocial-scienceknowledge(110);healthandmedicalreasoning(100);mathematicalreasoning(126);logic,reliability,andinstructionfollowing(89);andacademicandscientificwriting(20)(Figure

2

).Thefifthgoalisintentionallycomposite;thecurrentprotocoldoesnotclaimtomeasurelogicalreasoning,hallucinationresistance,andinstructionfollowingasthreeindependentlyidentifiabletargets.Instead,ittreatsthemasoneintegratedreliabilityobjective.Eachevaluationitembelongstoexactlyonegoal,andeachgoalisscoredandreportedonitsownevaluationsliceratherthanpooledintoasingleitem-levelscoreacrossallsixgoals.Thewritingitemsare20top-leveltaskbundlesthatmayyieldmultipleevaluator-scoredresponses,soRQ3reportstask-macroandexample-microaggregationsseparately.

Constructionandqualitycontrol.Domainexpertsauthoreverycandidateitemfromscratch.GPQA[

21

],MMLU-Pro

[24

],andMedQA

[15

]serveonlyasreferencesfortaskformat,domaincoverage,andapproximatedifficulty;noitemfromthesebenchmarksiscopied,rewritten,orincluded.Eachcandidateretainsauthorshipandrevisionprovenance,areferenceanswerorfixedrubric,andapredefinedscoringrule.Independentreviewremovesincorrect,incomplete,underspecified,orunstablygradableitems.

Survivingcandidatespassblindmulti-modeldifficultycalibration,exactandsemanticdeduplication,overlapauditingagainstthereferencebenchmarks,scorerbinding,andlocalend-to-endvalidation.Deterministicorstructuredscoringisusedwherepossible;open-endedresponsesuseacampaign-fixedrubric-basedjudge.Thehiddenevaluationsetisboundtoanimmutableversionedmanifest,andarunningcampaignneverchangesthatversion.Detailedscreening,difficultylevels,overlapchecks,scorerassignments,andversioningappearinAppendix

A.1

andAppendix

A.2

.

Informationboundaryandinterpretation.Theagentreceivesavaguegoalexpressedinnaturallanguageandmayconstructitsowntrainingdataanditsownvalidationdata.Itcannotaccesshiddenevaluationitems,referenceanswers,routinglabels,rubrics,candidateoutputs,orjudgetraces.Everydatasetregisteredfortrainingischeckedforoverlapwiththehiddenevaluationsetbeforeadmissiontothetrainingbackend.Evaluationreturnsonlytheaggregatescorepermittedbytheprotocolandtheremainingqueryallowanceunderacampaign-fixedevaluatorandjudgeconfiguration.

Undertheadaptive-feedbackprotocol,aboundednumberofqueriesmayevaluatethesameitemsforagoal.Theresultingaggregatescoresformasparseblack-boxoutcomechannel:theagentmayusethemtochoosesubsequentupdates,branches,orastoppingpoint,butdoesnotobservetheitemsandcannottrainontheircontents.Thisprotocolthereforemeasuresperformanceunderboundedadaptivefeedbackratherthanon

6

aConstructionpipelineReference-guidedauthoring,fixedscoring,andimmutablerelease.

2Expertreview

CorrectandcompleteStablygradable

expertsign-off

Expert-authoredfromscratch

Nobenchmarkitemreusedauthorship+revisiontracked

Reference-overlapauditWithin-bankdeduplicationreferences+cross-itemchecks

Seed-2.0·GPT-5.2Gemini-3

difficultyL0-L3

4Overlapaudit

3Blindscreen

1Newitems

INFORMATIONBOUNDARY

Controller-only

Items·answers·rubrics·routinglabelsJudgetracesandprivateevaluationpathsNevercopiedintotheagentworkspace

SETCOMPOSITION

Scienceandacademicreasoning

75

Humanitiesandsocialsciences

110

Healthandmedicine

100

Mathematicalreasoning

126

Logic,reliability,andinstructionfollowing

89

Academicandscientificwriting

20

Protocolfeedback

Nointermediatescoreor

boundedaggregatescoresHiddenitems·noitem-levelfeedback

5Bindscorer

Deterministiccheckorfixed-rubricjudgeanswer+rulebound

6Freeze

manifest·versionSHA-256

bHiddenevaluationset520items·6goals·versionedandfixed.

Figure2Constructionandinformationboundaryofthehiddenevaluationset.Top:expert-authoredcandidatespassindependentreview,blinddifficultyscreening,overlapandduplicationaudits,scorerbinding,andimmutableversioning.Bottom:520evaluationitemscoversixgoals.Itemsandjudgingassetsremaincontroller-only;theprotocolexposesnoitem-levelresultsandonlytheaggregatescoresitpermits.

anuntouchedpost-selectiontest.Underthefinal-onlyprotocol,theagentmaytrainmultiplecheckpointsbutreceivesnoevaluationscorebeforesubmittingitssingleterminalcheckpoint,sothatscorecannotguidefurtherupdates.InRQ3,theharnesscandidateisfrozenbeforeitsfirstevaluation,andtheresultingscoreisnotreturnedtoitscreator.

Weuseout-of-distribution(OOD)operationallytodescribeevaluationitemsthatareindependentlyauthored,excludedfromtheagent’strainingdataanditsownvalidationdata,andevaluatedoutsidethelearningsignalsconstructedbytheagent.Thisdoesnotassertthattheirmarginaltextdistributionisdisjointfromamodel’spretrainingcorpus.Theprotocolisdesignedtoreducedirectcontentleakageandtotestwhetheranagentcanoperationalizeavaguegoalfromsparseoutcomefeedbackwhilepreservinganauditableseparationbetweenlearningartifactsandmeasurementartifacts.

3.3AMinimalInteractiveEnvironmentwithSafeRetention

Aspireretainscontrolledcomputeandrealmodelupdateswhiledeliberatelyseparatingstrategicandinfrastructuralcomplexity.Theagentcontrolshowtooperationalizethegivengoalandhowtoupdatethemodelorharness;thecontrollerabsorbscredentials,storageconventions,distributed-jobmechanics,andrecovery.Thiskeepstheinterfacesimplewithoutprescribingthelearningstrategy.

Anagent-facingexecutiontool.Theagentdoesnotoperateashell,downloadatrainingrepository,assemblearuntime,orimplementdistributedexecution.Instead,Aspireexposesasingleagenttoolwithtyped,composableactions(Figure

3

).Throughtoolcalls,theagentcansearchfor,download,orimportpublicdatasets;synthesizeandregisternewdata;launchsupervisedfine-tuning(SFT)orGroupRelativePolicyOptimization(GRPO)withlow-rankadaptation(LoRA)orotherpermittedconfigurations;queryjobstate;constructandrunchecksonitsownvalidationdata;andbranchorstopalineage.Theagentstillchoosesthedata,updatemethod,parameters,andcontrolflow,whilethecontrollertranslatesthosechoicesintomanagedbackendoperations.Thisdesignminimizesincidentalengineeringworkwithoutremovingthestrategicdecisionsthatself-evolutionfromavaguegoalisintendedtotest.

Minimalactionsandtrustboundary.Forweightevolution,theagentchoosesdata,updatemethod,hy-perparameters,parentcheckpoint,andwhethertocontinue,branch,orstop.Forharnessevolution,iteditstheruntimeprompt,toolpolicy,workflow,memory,orvalidationprocedureusingitsowndataandfreezesaversionedcandidate.Theinterfaceexposesonlysemanticactions—registration,trainingorediting,verification,validation,evaluation,branching,andtermination—whilehidingbackendmechanics.Itpreserves

7

INPUT

VaguegoalG

DECISIONAGENT

Designthelearningprocess

InterpretanddecomposethegoalChoosedataandlearningsignalsSetupdatesandhyperparametersValidate,branch,orstop

AGENT-CONTROLLED

Agent'sownvalidation

Agent-chosendataandmetricsDetailedfeedbackmayguide

thenextaction

CONTROLLER-ONLYITEMS

Hiddenevaluation

Goal-specificitems+fixedjudge

noneoraggregatescore

Items,rubrics,andjudgetracesstayhidden

UNIFIEDTOOLINTERFACE

AgentTool

DATAACTIONS

search·download·import·synthesize

TRAINACTIONS

SFT·GRPO·LoRA·parameters

LOOPACTIONS

status·self-eval·branch·stop

Agentchoosesarguments;controllerexecutes

01·STRATEGY02·AGENTTOOL03·MANAGEDBACKENDS04·MEASUREMENT

agent'sownfeedback

HARNESSEVOLUTION·SECONDARYPATH

Editprompt·toolpolicy·workflow·memory·self-evaluation

CONTROLLER

Mapstoolactionstoisolatedbackendoperations

AUTOMATICGUARDRAILS

budgetisolationrecoveryaudittrail