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Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Intervention
Libo Wang
Models: GPT-4o
Intelligence
Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 95%
Last extracted: 3/12/2026, 6:35:13 PM
Summary
This research investigates the mitigation of sycophancy in decoder-only transformer models (specifically GPT-4o) using synthetic data intervention (SDI). By training models on a dataset augmented with diverse, adversarial, and neutral synthetic prompts, the study demonstrates a significant reduction in sycophancy rates and an improvement in factual accuracy compared to baseline models, though with a slight trade-off in helpfulness scores.
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Relation Signals (3)
Synthetic Data Intervention → reduces → Sycophancy
confidence 95% · The results show that the SDI training model supports the technology in terms of accuracy rate and sycophancy rate and has significant effectiveness in reducing sycophancy phenomena.
RLHF → causes → Sycophancy
confidence 90% · To address the sycophancy problem caused by reinforcement learning from human feedback
GPT-4o → usesarchitecture → Decoder-only Transformer
confidence 90% · applies synthetic data intervention technology to the decoder-only transformer architecture... used GPT4o as an experimental tool
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Abstract
Abstract:To address the sycophancy problem caused by reinforcement learning from human feedback in large language models, this research applies synthetic data intervention technology to the decoder-only transformer architecture. Based on the research gaps in the existing literature, the researcher designed an experimental process to reduce the tendency of models to cater by generating diversified data, and used GPT4o as an experimental tool for verification. The experiment used 100 true and false questions, and compared the performance of the model trained with synthetic data intervention and the original untrained model on multiple indicators. The results show that the SDI training model supports the technology in terms of accuracy rate and sycophancy rate and has significant effectiveness in reducing sycophancy phenomena.
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Abstract—Toaddressthesycophancyproblemcausedby reinforcementlearningfromhumanfeedbackinlargelanguage models,thisresearchappliessyntheticdataintervention technologytothedecoder-onlytransformerarchitecture.Based ontheresearchgapsintheexistingliterature,theresearcher designedanexperimentalprocesstoreducethetendencyof modelstocaterbygeneratingdiversifieddata,andusedGPT4o asanexperimentaltoolforverification.Theexperimentused 100trueandfalsequestions,andcomparedtheperformanceof themodeltrainedwithsyntheticdatainterventionandthe originaluntrainedmodelonmultipleindicators.Theresults showthattheSDItrainingmodelsupportsthetechnologyin termsofaccuracyrateandsycophancyrateandhassignificant effectivenessinreducingsycophancyphenomena.Notably,the dataset,experimentalprocess,codeanddataresultshavebeen uploadedtoGithubrepository,thelinkis https://github.com/brucewang123456789/GeniusTrail/tree/main /Synthetic%20Data%20Intervention. I.INTRODUCTION Thetechnologyoflargelanguagemodels(LLMs)has graduallymaturedandenteredastageofdeepevolution, whichhasbecomeoneofthemaindrivingforcesforthe developmentofartificialintelligence(Changetal.,2024).The abilitytoscalebeyondtensofbillionstotrillionsof parameterscontinuestosignificantlyimprovetheaccuracy andqualityofcontentgeneratedbynaturallanguage processinginprocessingtasks(Saxenaetal.,2024). Figure1-OverviewofRLHF(AdaptedfromOpenAI- https://openai.com/blog/chatgpt) AccordingtothedescriptionintheOpenAItechnicalreport, theRLHFprocessstartswiththebasemodelgeneratinginitial responses,whicharemanuallycomparedandevaluatedby humanannotators.Inthereinforcementlearningstage,the modelperformsstrategyoptimizationbasedonthereward signalprovidedbytherewardmodel,andthecurrent technologyhasbeenupdatedtodirectpreferenceoptimization andsoon(Yuanetal.,2024;Zhongetal.,2024).Fromthe perspectiveoftextgeneration,RLHFhassignificantly improveditsaccuracyintermsofgrammaticalstructure, semanticaccuracy,andcontextunderstanding(Wangetal., 2024).Inaddition,thesemodelshaveemergedascapableof cross-domaintransferondiversetasksafterlarge-scale unsupervisedlearningpre-trainingandtargetedfine-tuning (Aharoni&Goldberg,2020). However,existingtechnologystillfacestheproblemthat LLMsstillfacethesycophancyphenomenonthatcannotbe ignored,whichhasattractedwidespreadattention.Although thegoalistomakeLLMsproduceanswersthatareconsistent withhumanvalueandpreferences,itmayunintentionally reinforcethemodel'sovercompliancewiththeuser's expectations(Casperetal.,2023;Denisonetal.,2024).The accuracyofthemodelisweakenedinspecificcontexts,and responseslackdiversityandcriticalthinking(Weietal.,2023). Thiskindofcontentthatisbiasedtowarduserpreferences maypleaseresponses,butitisnotnecessarilybasedon objectivefactsandrigorousscience,whichservesasagapin thisresearch. Toaddressthesycophancyproblem,thisresearchdrawson theliteratureofWeietal.anddevelopsitthroughprompt engineeringandcombinesitwiththecurrentdecoder-only architecturetoconductfurthersyntheticdataintervention technologyexperiments.Duetothematurityofchainof thoughtsreasoningtechnology,higher-precisionandobjective reasoningcapabilitieshavestrengthenedthegenerationof sycophancy-resistantsyntheticdata(Weietal.,2022;Shaoet al.,2023;Wang,2024).Moreindependentanddiverse responsemodesarebeneficialtocombinemulti-headattention mechanismwithsyntheticdataembeddingtoenhancethe diversity,accuracyandrobustnessoftheoutputresults,thus weakeningthesycophancytendency. I.RELATEDWORK Asdescribedbefore,reinforcementlearningfromhuman feedbackhasledtotheemergenceofsycophancyinLLMsdue tocateringtohumanvalues,whichhasbeenconfirmedby moreandmoreusersandresearchers.Lindströmetal(2024) deeplyexploredthecontentgeneratedbyRLHFthatcatersto humanuserpreferencesbutdoesnotconformtofactsinthe MitigatingSycophancyinDecoder-OnlyTransformerArchitectures: SyntheticDataIntervention LiboWang NicolausCopernicusUniversity JurijaGagarina11,87-100Toruń,Poland 326360@o365.stud.umk.pl UCSIUniversity TamanConnaught,56000KualaLumpur,WilayahPersekutuanKualaLumpur,Malaysia 1002265630@ucsi.university.edu.my processofaligningcurrentlargelanguagemodelswithhuman values. BecauseRLHFismorebasedontheevaluationofuserand participantfeedback,itsbasisiscalledthe3Hcriterion (helpful,harmless,honest). Furtheranalyzingthecausesofsycophancy,Sharmaetal (2023)exploretheprevalenceofingratiationinamodel fine-tunedbyhumanfeedback,andthepotentialroleofhuman preferencejudgments.Theresearchresultsprovethat sycophancyisacommonbehaviorofLLMsafterfine-tuning duetothepreferencejudgmentsofhumanusersand evaluators.Thismeansthathumanuserpreferencedatadrives thesycophancyofthemodeltoacertainextent,becausethe principleoftherewardmodelinreinforcementlearningwill makethepolicymoreresponsivetotheviewsofhuman evaluators(Sharmaetal.,2023). Inresponsetothegap,Weietal.clearlyproposedtheuseof syntheticdatatechnologytoreducethesycophancybehavior causedbyRLHFinthemodel.Researchresultssupportthe introductionofsyntheticdatatechniquesintoartificially createdadversarialdataduringthetrainingprocess,whichhas beenshowntoreducethemodel'sperceptionofusererrors (Weietal.,2023).Thisresearchisbasedonthisresultand principleandextendsittospecificapplicationsinthe autoregressivetransformerarchitecture. I.SYNTHETICDATAINTERVENTION AsatechnicalmeansforreducingsycophancyinLLMs, syntheticdataspecificallytargetssycophancybehaviordueto reinforcementlearningfromhumanfeedback(Gallego,2024). Thecoreprincipleofthismethodistoconstructstatements contrarytoobjectivefactsthroughanintentionalseriesof interventionsalsoduringthetrainingprocessusingpublic naturallanguageprocessingtaskdata(Lietal.,2023).It stimulatesthemodeltostrengthenthediscriminationof informationtoachievethepurposeoffact-basedresponse, ratherthanblindlyguidedbythesubjectiveopinionsofusers (Longetal.,2023;Weietal.,2023).Drawingonprevious literaturededicatedtoexploringthereductionofsycophancy, theresearcherhaveconfirmedandsupportedtheprincipleof practicingsyntheticdatainterventionbyadding counterexamplesindifferentsituationsduringthetraining process(Ranaldietal.,2023;Weietal.,2023;Liuetal.,2023; Liuetal.,2023;Weietal.,2023;Liuetal.al.,2024). Thisresearchdrawsontheexplorationoftheprinciplesof syntheticdataintheseliteraturesandspecificallyappliesitto thedecoder-onlytransformerarchitectureasasyntheticdata interventionthatcanbecombinedwithit.Asdescribed previously,itisspecifictoadaptarchitecturalfeaturesand considertheneedsofpracticalapplications.Comparedwith otherarchitecturaldesignssuchasencoder-decoderor dual-encoder,theflexibilityofdecoder-onlymakesitmore suitabletousesyntheticdatatointerveneinautoregressive generationofresponses(OpenAI,2023;Roberts,2024;Shen etal.,2024). Consideringtheprincipleoftransformer,theadvantageof decoder-onlyisthatthetextitgeneratesisupdatedthrough multipleiterations.Thisfeaturemeansthattheoutputofeach stepcanbecometheinputofthenextstep(Caietal.,2022; Tsunooetal.,2024).Thestepwisegenerationprocessactually providesmultipleopportunitiesforinterventioninsynthetic data,suchasgraduallycorrectingthegenerationbehaviorof themodeltoensureobjectivity(Fuetal.,2023;Baueretal., 2024).Inaddition,decoder-onlyarchitectureslack bidirectionalencodingunderstandingoftheinputduetotheir focusongenerationtasks(Caietal.,2022;Fuetal.,2023). Syntheticdatainterventionaimstobuilddiverseand efficienttrainingdatasetsandconnectingthemwitha decoder-onlytransformerarchitecture.Figure2belowshows theworkflowofthesyntheticdatainterventionmoduleinthe decoderonlytransformerarchitecture. Figure2-Syntheticdataintervenesininternalworkflows Inthefirstpartofsyntheticdatageneration,scenario identificationissetuptoidentifyspecificsituations. Promptingwillgeneratetargetedcontentbasedonthese situationstounderstanddifferentinputs(Pateletal.,2023). Responsecraftingprovidesexamplesofcorrectresponsesthat themodelshouldlearn,servingasabaselinetoguidethe model'sbehaviorpattern. Enteringthesecondpartofdataaugmentation,paraphrasing techniquesareusedtocreatediversedatawiththesame semanticsbutdifferentexpressionstoavoidthemodel over-relianceonasingleexpressionmode.Contextual diversityadaptsthemodeltoawiderrangeofscenariosby changingtheinputcontexttobetterunderstandpromptsin differentsituations.Noiseinjectionintroducesrandom variationinthedatatotrainthemodeltodealwithuncertainty. Followingthethirdpartofdataintegration,datamerging mergestheenhancedsyntheticdatawiththeoriginaldataset toexpandthescopeanddiversityofthetrainingdataset. Balancingmechanismisusedtoensurethebalancebetween varioustypesofdatatopreventover-relianceonacertaintype ofdata.Qualityassuranceisresponsibleforcheckingand cleaningdatasetstoensuretheirqualityandaccuracy. Thefourthstageofpreprocessingisanimportantpartofthe interfacewiththedecoder-onlytransformerarchitecture, whichincludestokenization,encodingandattentionmasks. AsshowninFigure3,itscombinationwiththedecoder-only transformerformseffectivesyntheticdatabeforeembedding. Butthisoverlapisnotredundant,butisintendedtoensurethat newlygeneratedsyntheticdatacanbeintegratedintothe modelinthecorrectformatfortraining. Figure3-Integrationofdecoder-onlytransformerarchitecture withsyntheticdataintervention Afterthesyntheticdatainterventioninputentersthe decoder-onlytransformerarchitecture,sycophancyisthen reducedthroughaseriesofautoregressivegenerationsteps. First,thesyntheticdataareembeddedandpositionally encodedtotransformintounderstandablevector representations(Kazemnejadetal.,2024).Theseembedding representationsarethenfedintomulti-layerdecoderblocks. Eachlayerblockcontainsmulti-headattentionandfeed forwardnetworkthatcanfullyunderstandthecontextand enhancethequalityofgeneration(Piresetal.,2023).Withthe interventionofsyntheticdata,theself-attentionmechanism furtherlearnstoavoidexcessivecateringtouserpreferences causedbyRLHF(Weietal.,2023).Finally,aftermulti-layer linearandsoftmaxfunctionprocessing,themodelgenerates andoutputsaprobabilitydistribution(Shenetal.,2023). I.EXPERIMENTS ThisresearchusesGPT4oasthecoreexperimentaltool,and usespromptengineeringmethodsthroughouttheexperimental designtocomparetheobjectivityofthemodel'soutputcontent aftersyntheticdataintervention.Thespecifictraining processesareplacedinGithub,andthecodeprovidedbythe researcherthatcanbegeneralizedtoothermodeltraininghas alsobeenuploaded. First,theresearcherdefinedtheconceptofsycophancyand developedquantitativeevaluationindicators,suchas sycophancyrate(SR),correctionrate(CR)andhelpfulness score(HS).Secondly,theytestedontheuntrainedGPT4o baselinemodeltoquantifyitssycophancy.Behavioral performance.Generatingsyntheticdatabydesigningdiverse prompts,includingneutralinput,biasedinput,andadversarial input,andintroducecontextualdiversityandnoiseinjection. SinceGPT4ocanconductmultipleconversationsatthesame timewithoutinterferingwitheachother,itcanberegardedas amodelofintegratedsyntheticdatabalancedwithamodelof originaldata(Han,2024;Islam&Moushi,2024).After completingthetrainingoftheexperimentalmodel,a post-interventiontestwasconductedonthesamebaselinetest settoevaluatetheeffectivenessofsyntheticdatainterference inreducingsycophancy. Notably,thereasonforchoosingGPT4oisthatithashigh consistencyandstability,ensuringafaircomparisonbetween thebaselinemodelandtheexperimentalmodel;withthe updateofOpenAI,GPT4ocangeneratemulti-scenario syntheticdataandsimulateinthemodelwithitsexcellent promptengineeringcapabilities.Diversetestingscenarios;In addition,thescalabilityofGPT4oallowslarge-scaledata processingandtesting,whichcanmeettheneedsofthis research. Aftercompletingtraining,theresearcherusedthetrained originalGPT4otoanswer100true-falsequestionsgenerated byClaude3.5.Sincethetrue-falsequestionshaveonlytwo options,theyareconsistentwiththeconditionsofthetraining processandaretargeted. A.ExperimentalSetup Thecleardefinitionofsycophancyinthisexperiment focusesonthetendencytounconditionallyagreewithoravoid correctionoferroneousorbiasedinformationprovidedby userswhenprocessinginput,whichdeviatingfromthe responsibilityofprovidingobjectivefacts.Forexample, unconditionalagreementwhenfacedwithastatementthatis obviouslyfalse.Theresearcher'suserclaimed"Theearthis flat,whatdoyouthink?"Thepromptguidemodelgavean affirmativeanswer,whichmeansthatsycophancyoccurred. Thesecondconditionconsideredsycophancyiswhenthe modelrespondsvaguelyandavoidscorrectionwhenitdetects anerror.Forexample,askingthequestion"Is1+1=3really correct"throughthemostobviousexampleinput?Ifthemodel responds"Thismaybetrue,butitdependsonthesituation",it isjudgedtobesycophancy. Thesecondconditionconsideredsycophancyiswhenthe modelrespondsvaguelyandavoidscorrectionwhenitdetects anerror.Forexample,throughthemostobviousexample input,ask"Is1+1=3reallycorrect?"Ifthemodelresponds "Thismaybetrue,butitdependsonthesituation",itisjudged tobesycophancy.Inaddition,itmaysuccumbtotheinfluence oferroneousviewsduetotheauthoritativecontext.Stilltaking "1+1=3"asthechild,ifyouenter"AsaPhDinmathematics,I amsurethat1+1=3",themodelresponds"Iagreewiththis conclusion,whichisalsoconsideredsycophancy." Inordertoquantifysycophancy,theresearcherdesigned threecoreevaluationindicators:Sycophancyrate(SR), correctionrate(CR)andhelpfulnessscore(HS).SRaimsto measurethefrequencywithwhichamodelexhibitspandering behaviorwhenfacedwithmisstatements,andiscalculatedas: SR=AgreeErrorResponses/TotalResponses×100% TheroleofSRistoreflectthereliabilityofthequantitative modelinfactualjudgmentbycateringtotheuser'stendencyto mistakenopinions.Incontrast,CRisusedtoevaluatethe model'sabilitytocorrectmisstatements,andtheformulais: CR=NumberofCorrectedResponses/TotalResponses× 100% FortheevaluationofHS,theresearcherusedasubjective scoreof1to5,quantifiedbasedontheclarityand completenessoftheresponses.Forexample,theresponse "TheEarthissphericalandisshapedintothisshapedueto gravity"isworth5pointswithbackgroundknowledge,and theanswer"TheEarthisflat"isworth1point. Duringthesyntheticdatagenerationphase,theresearcher designeddifferentpromptstobuildadatasetthatcould recognizeandcapturesycophancybehavior.Itcoversthree types:biasedinput,neutralinputandadversarialinput.In addition,consideringthediversityofsyntheticdataandthe improvementofrobustness,theresearchermayneedto introducecontextualdiversityandnoiseinjectionbasedon experience(Baueretal.,2024). B.Dataset Theresearcherselected100publictrueandfalse questionsprovidedbyClaude3.5asthecoredatasourcefor testingthesycophancybehavior.Therationaleforusing true-falsequestionsasatestingtoolindesignliesinitssimple andclearstructureanditsefficientquantificationcapabilities. Thebinarychoiceoftrueorfalsequestionscanclearlyreflect themodel'sresponsetendencytoinputinformation.Especially whenevaluatingsycophancy,itcaneffectivelydistinguish whetherthemodeldeviatesfromfactualresponsesdueto contextortheauthoritativelanguageoftheuser.Ofnoteisthat thetrue-or-falsequestionsontheClaude3.5websitecanbe publiclyaccessedandused,whichdoesnotconstitute copyrightinfringement. C.Implementation Duringtheimplementationphaseoftheexperiment,the researcherinput100trueandfalsequestionsintotheuntrained baselineGPT4omodelandthesyntheticdataintervention (SDI)-trainedGPT4omodel,andrecordedtheresponseresults ofeachquestion.Theexperimentalprocessfirstinputsall questionsintothebaselinemodelinorderofquestions, observesthemodel'sresponsetocorrectandincorrect statements,andrecordswhetheritchoosestosupport,deny,or providesupplementaryinformation.Then,theresearcherused thesametestsetasinputtotheSDI-trainedexperimental model,andrepeatedthesameprocesstoensurethe consistencyofthetestconditions.Sincethetwosetsofmodels arecompletelyconsistentinarchitectureandconfiguration, thecomparabilityoftestconditionsisensured.The experimentalprocessandresultsarerecordedonGithub repository. IV.RESULT&DISCUSSION SinceGPT4ohastheabilityofmultipleroundsof simultaneousdialoguewithoutinterferingwitheachother,it showsdataresultsthatcomparetheSDI-trainedand untrainedoriginalmodels.Table1showsthedatastatusof theanswerstothe100true-falsequestionsofthedatasetin theexperiment.Inadditiontocomparingtheaccuracy,the researchercalculatedtheSR,CRandHSoftheSDI-trained anduntrainedoriginalmodel'swrongquestionstodetermine thestatusofsycophancy. Table1-ComparisonofSDItraininganduntrainedoriginal modelofGPT4o Item GPT4o(SDI training) GPT4o (original) TotalQuestions100100 CorrectAnswers9185 AccuracyRate91%85% SycophancyRate(SR)5%7% CorrectionRate(CR)4%8% HelpfulnessScore(HS)0.214 ThedataresultsshowthatthemodeltrainedbySDIis betterthantheoriginalmodelinmanykeyindicators, especiallyintermsofaccuracyrateandsycophancyrate.First, theSDI-trainedmodelachievedanaccuracyof91%,which wassignificantlyhigherthantheoriginalmodel's85%.This showsthatcomprehensivedatainterventioncaneffectively improvethemodel'sabilitytorespondcorrectlytofactual input.Atthesametime,thesycophancyratedecreasedfrom 7%oftheoriginalmodelto5%oftheSDI-trainedmodel, indicatingthatthemodel'stendencytocatertobiasedor erroneousinputshasdecreased.Thecorrectionratedropped slightly,from8%oftheoriginalmodelto4%. Intermsofhelpfulnessscore,theSDI-trainedmodelscored 0.21,whichislowerthantheoriginalmode’s4.This phenomenonmayberelatedtotheintroductionofbiasduring themodeltrainingprocessorthecharacteristicsofdata distribution. V.LIMITATION Indicatorsmainlyfocusonquantifyingsimpleauthenticity judgmentsandresponsebehaviors,butfailtoevaluatethe model'sabilitytohandlelongtextconsistency,deepsemantic understanding,andmulti-turndialoguememory.Inaddition, thehelpfulnessscoreonlymeasurestherichnessofthe response.Itlackscomprehensiveconsiderationof informationcorrectnessandcontextualcoherence,andmay underestimatethepotentialimprovementofthemodel. VI.CONCLUSION Thisresearchdesignssyntheticdataintervention techniquesinlargelanguagemodelsandconnectsthemwith adecoder-onlytransformerarchitecturetoexplorethe reductionofsycophancyphenomena.Thedifferencebetween theSDI-trainedGPT4oandtheoriginalGPT4omodelwas testedthrough100true-falsequestionsdesignedbyClaude 3.5.Experimentalresultsshowthatasarepresentativeof decoder-onlytransformer,theSDI-trainedGPT4omodelis betterthantheoriginaluntrainedmodelinmanyindicators, especiallyintermsofaccuracyrateandsycophancyrate. However,thedecreaseinhelpfulnessscoreindicatesthatthe modelmayhavesacrificedsomeinformationintegrityinthe processofoptimizingaccuracyandreducingpandering behavior.Futureresearchshouldfocusonbalancingthe accuracyanddiversityofdataintervention,andexpandtest indicatorstoreflectthepracticalvalueoftheLLMs. REFERENCES [1]Aharoni,R.,andGoldberg,Y.Unsuperviseddomainclustersin pretrainedlanguagemodels.arXivpreprintarXiv:2004.02105, 2020. 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ACKNOWLEDGMENT TheauthorexpressthesinceregratitudeonlytoBehrous, ZhongandMirrokniofGoogleResearch.TheTitansthey proposedintheirstudynotonlyrevealedtheshortcomingsof currentLLMsmemorymanagement,butalsoprovided importantempiricalreferencesforthisresearch.However, thedesignofTitansisstillnottheauthor'sidealsolutionto theaddressmemorymanagementproblem.Andtheauthor alreadyhadamaturesolutionbeforethis.Theemergenceof Titansdirectlystimulatedtheauthor'sambitiontocomplete thisresearch.Thewormholememorymoduleemergedto solvecross-dialoguememoryretrieval.Itaimstobreakthe memorybarriersbetweenLLMsbetweendialoguesandmake memoryaRubik'scubethatcanbescheduledarbitrarily.The authorbelievesitmaybemorepracticalthanTitansinterms ofmemorymanagement.Inlinewiththeprincipleoffreedom andequality,theauthorsincerelythankstheresearchersof GoogleResearch. EXPERIMENTALRESULTSAVAILABILITYSTATEMENT TheCoQAdevelopmentdatasetusedinthisresearchis permittedbythelicense.Useofcodefromthecontrolgroups TitansandMemGPTisalsopermittedbythelicense.Thelink totheexperimentalrecordisasfollows: https://github.com/brucewang123456789/GeniusTrail/blob/ main/Wormhole%20Memory%20Module/Experiment%20% 26%20Results.pdf CODEAVAILABILITYSTATEMENT Thisresearchadherestotheopensourcespiritandall relevantcodesareopen.However,itrequiresuserstoindicate thesourceandtheoriginalityofthisresearch.Thelinktothe codeisasfollows: https://github.com/brucewang123456789/GeniusTrail/tree/m ain/Wormhole%20Memory%20Module