q137/random_k4/random_2.md

|     | -Bench: |     | Graduate-level |     |         | Multi-disciplinary |           |     | Benchmarks |     |     | for |     |
| --- | ------- | --- | -------------- | --- | ------- | ------------------ | --------- | --- | ---------- | --- | --- | --- | --- |
|     |         | LLM | & MLLM         |     | Complex |                    | Reasoning |     | Evaluation |     |     |     |     |
Meng-HaoGuo1 JiajunXu*1 YiZhang*1 JiaxiSong*1 HaoyangPeng*1 Yi-XuanDeng*1 XinzhiDong*1
KiyohiroNakayama2 ZhengyangGeng3 ChenWang4 BolinNi5 Guo-WeiYang6
YongmingRao†5 HouwenPeng†5 HanHu5 GordonWetzstein2 Shi-MinHu†(cid:66)1
5202 yaM 4  ]VC.sc[  1v81020.5052:viXra Abstract
Reasoningstandsasacornerstoneofintelligence, 92.3 90.0 M M L U - B e n c h - T
|     |     |     |     |     |     |     |     | 88.0 |     |     |     | M M M U | - B e n c h - M |
| --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- | ------- | --------------- |
enablingthesynthesisofexistingknowledgeto
78.2
| solve complex |     | problems. | Despite | remarkable |     |     |     |      |     |     |     |      |     |
| ------------- | --- | --------- | ------- | ---------- | --- | --- | --- | ---- | --- | --- | --- | ---- | --- |
|               |     |           |         |            |     |     |     | 69.0 |     |     |     | 69.1 |     |
progress,existingreasoningbenchmarksoftenfail
61.2
)%( ycaruccA
| torigorouslyevaluatethenuancedreasoningcapa- |     |     |     |     |     |     |     |     | 53.6 |     |     | 53.2 |     |
| -------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | ---- | --- |
bilitiesrequiredforcomplex,real-worldproblem-
| solving, | particularly |     | in multi-disciplinary |     | and |     |     |     |     |     |     |     |     |
| -------- | ------------ | --- | --------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
33.4
| multimodal | contexts. |     | In this paper, |     | we intro- |     |     |     |     |     |     |     |     |
| ---------- | --------- | --- | -------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
duceagraduate-level,multi-disciplinary,English-
Chinesebenchmark,dubbedasReasoningBench
| (R-Bench), | for | assessing | the reasoning |     | capabil- |     |     |     |     |     |     |     |     |
| ---------- | --- | --------- | ------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
R-
ityofbothlanguageandmultimodalmodels. o1-20241217 GPT-4o DeepSeek-R1 o1-20241217 GPT-4o
Benchspans1,094questionsacross108subjects
forlanguagemodelevaluationand665questions
|     |     |     |     |     |     |     | Figure1.Top-1 | accuracy | comparison |     | of different | models | on  |
| --- | --- | --- | --- | --- | --- | --- | ------------- | -------- | ---------- | --- | ------------ | ------ | --- |
across 83 subjects for multimodal model test- MMLU,MMMU,andR-Bench. R-Benchposesagreaterchal-
| ing in both | English |     | and Chinese. | These | ques- |     | lengetocurrentmodels. |     |     |     |     |     |     |
| ----------- | ------- | --- | ------------ | ----- | ----- | --- | --------------------- | --- | --- | --- | --- | --- | --- |
tionsaremeticulouslycuratedtoensurerigorous
difficultycalibration,subjectbalance,andcross-
linguisticalignment,enablingtheassessmentto Reasoning,thesystematicprocessofsynthesizingknowl-
be an Olympiad-level multi-disciplinary bench- edgetosolvenovelproblems,liesattheheartofintelligence.
mark. Weevaluatewidelyusedmodels,includ- Yet,asfoundationmodelsgrowincreasinglysophisticated,
| ingOpenAIo1,GPT-4o,DeepSeek-R1,etc. |     |     |     |     |     | Ex- |                     |     |      |                    |     |        |       |
| ----------------------------------- | --- | --- | --- | --- | --- | --- | ------------------- | --- | ---- | ------------------ | --- | ------ | ----- |
|                                     |     |     |     |     |     |     | existing benchmarks |     | fail | to comprehensively |     | assess | their |
perimentalresultsindicatethatadvancedmodels complex reasoning capabilities. As shown in the above
performpoorlyoncomplexreasoning,especially quote,beforeequippingfoundationmodelswithreasoning
multimodalreasoning. Eventhetop-performing skills,weshouldfirstdefinegoalsforthembyestablishing
modelOpenAIo1achievesonly53.2%accuracy areliableevaluationtoassesstheirreasoningcapabilities.
| onourmultimodalevaluation. |     |     | Dataandcodeare |     |     |     |                             |     |     |     |                     |     |     |
| -------------------------- | --- | --- | -------------- | --- | --- | --- | --------------------------- | --- | --- | --- | ------------------- | --- | --- |
|                            |     |     |                |     |     |     | Asnotedin(Kahneman,2011)and |     |     |     | (Weietal.,2022),re- |     |     |
madepubliclyavailableathere.
|     |     |     |     |     |     |     | alizing system-I, |     | a.k.a., quick | and | intuitive | thinking | and |
| --- | --- | --- | --- | --- | --- | --- | ----------------- | --- | ------------- | --- | --------- | -------- | --- |
system-II,a.k.a.,slowanddeliberatereasoningraisesdis-
|     |     |     |     |     |     |     | tinctrequirementsonfoundationmodels. |     |     |     |     | Similarly,assess- |     |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------------ | --- | --- | --- | --- | ----------------- | --- |
1.Introduction
|     |     |     |     |     |     |     | ing quick | thinking | and complex |     | reasoning | requires | sub- |
| --- | --- | --- | --- | --- | --- | --- | --------- | -------- | ----------- | --- | --------- | -------- | ---- |
“Settinggoalsisthefirststepinturningtheinvisi- stantiallydifferentassessmentmethods. Ontheonehand,
evaluatingsystem-Ineedstoevaluatetheknowledgeand
| bleintothevisible.” |     |     | —TonyRobbins |     |     |     |               |     |          |            |         |       |         |
| ------------------- | --- | --- | ------------ | --- | --- | --- | ------------- | --- | -------- | ---------- | ------- | ----- | ------- |
|                     |     |     |              |     |     |     | memory, which |     | requires | collecting | various | daily | conver- |
*Secondauthorslistedrandomly,†Jointprojectlead,1Tsinghua
|           |     |     |           |     |     |     | sations and | knowledge-based |     | questions |     | e.g., concept | and |
| --------- | --- | --- | --------- | --- | --- | --- | ----------- | --------------- | --- | --------- | --- | ------------- | --- |
| 2Stanford |     |     | 3Carnegie |     |     |     |             |                 |     |           |     |               |     |
University, University, Mellon University, common sense questions. On the other hand, evaluating
4UniversityofPennsylvania,5TencentHunyuanX,6Fitten.Corre-
|     |     |     |     |     |     |     | system-IIrequiresevaluatingcomplexreasoningskills. |     |     |     |     |     | It  |
| --- | --- | --- | --- | --- | --- | --- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- |
spondenceto:Shi-MinHu<shimin@tsinghua.edu.cn>.
requiresgatheringadiverserangeofreasoningquestions,
suchasanalyticalanddeductiveones,whichismorechal-
1

R-Bench
|              |         |                    |     |     |                 | benchmarksasexamples. |     | MMLU(Hendrycksetal.,2021) |     |     |
| ------------ | ------- | ------------------ | --- | --- | --------------- | --------------------- | --- | ------------------------- | --- | --- |
| Table1.Comp. | denotes | comprehensiveness. |     | o1  | saturation rep- |                       |     |                           |     |     |
isacomprehensivebenchmarkformulti-disciplineunder-
| resentso1(OpenAI,2024b)performanceonthisbenchmark. |     |     |     |     | It  |     |     |     |     |     |
| -------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
standing,whichhasservedasacriticalguideforthedevelop-
reflectsthechallengethatthebenchmarkposestoadvancedmod-
mentoffoundationmodelsinrecentyears.However,consid-
els.
eringthecurrentlevelofmodelintelligence,thisbenchmark
| Name |     | Comp. | o1Saturation |     | Language |     |     |     |     |     |
| ---- | --- | ----- | ------------ | --- | -------- | --- | --- | --- | --- | --- |
isclosetosaturation(e.g.,o1(OpenAI,2024b)hasachieved
|      |     | ✓   |       |     |     | 92.3%accuracyonit). |     | Besides,itdosenottakemultimodal- |     |     |
| ---- | --- | --- | ----- | --- | --- | ------------------- | --- | -------------------------------- | --- | --- |
| MMLU |     |     | 0.923 |     | en  |                     |     |                                  |     |     |
AIME@2024 ✗ 0.744 en ityandmultilingualismintoconsideration,whichisalsocrit-
R-Bench-T ✓ 0.690 en&zh icalforanidealreasoningtest. MMMU(Yueetal.,2024a)
|     |     |     |     |     |     | isaholisticevaluationformultimodalreasoningtests. |     |     |     | With |
| --- | --- | --- | --- | --- | --- | ------------------------------------------------- | --- | --- | --- | ---- |
✓
| MMMU |     |     | 0.782 |     | en  |     |     |     |     |     |
| ---- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- |
thelaunchofo1(OpenAI,2024b),thisbenchmarkisalso
| R-Bench-M |     | ✓   | 0.532 |     | en&zh |                                           |     |                                   |     |        |
| --------- | --- | --- | ----- | --- | ----- | ----------------------------------------- | --- | --------------------------------- | --- | ------ |
|           |     |     |       |     |       | closetosaturation.                        |     | Also,itcannotbeusedtoevaluatelan- |     |        |
|           |     |     |       |     |       | guagemodelsandignoresmultilingualtesting. |     |                                   |     | Weshow |
thecomparisonofMMLU,MMMU,andR-BenchinFig.1
lengingtocollectandfilterthantheformer.Inthispaper,we
|     |     |     |     |     |     | andTab.1. | Frontiermath(Glazeretal.,2024)collectssome |     |     |     |
| --- | --- | --- | --- | --- | --- | --------- | ------------------------------------------ | --- | --- | --- |
focusonbuildingareliablecomplexreasoningbenchmark
|                |          |        |        |     |            | challenging  | problems  | specifically | designed | for advanced   |
| -------------- | -------- | ------ | ------ | --- | ---------- | ------------ | --------- | ------------ | -------- | -------------- |
| for both large | language | models | (LLMs) | and | multimodal |              |           |              |          |                |
|                |          |        |        |     |            | mathematical | reasoning | evaluation,  | which    | indicates that |
largelanguagemodels(MLLMs).
currentmodelsstillexhibitweaknessesinmathematicalrea-
Howcanwedesignanidealassessmentforcomplexreason- soning. However,itfallsshortincomprehensivenessand
ing? Webelievefollowingfourpropertiesarecritical. multilingualtesting. ThisalsoappliestoOmni-Math(Gao
|     |     |     |     |     |     | et al., 2024) | and AIME | (OpenAI, | 2024b), | both of which |
| --- | --- | --- | --- | --- | --- | ------------- | -------- | -------- | ------- | ------------- |
• Comprehensiveness. Evaluating the intelligence of serveasbenchmarksfocusedonemployingmathematical
olympiadchallenges.
foundationmodelsisakintoevaluatinghumanintel-
| ligence. | Wecannotfocusonjustoneaspect,suchas |     |     |     |     |     |     |     |     |     |
| -------- | ----------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Inthispaper,ourgoalistobuildabenchmarkR-Benchthat
mathematics. Acomprehensiveevaluationisessential. alignswiththefourpropertiesweproposedforevaluating
|               |     |              |            |        |         | the reasoning | abilities | of intelligent | models. | To achieve |
| ------------- | --- | ------------ | ---------- | ------ | ------- | ------------- | --------- | -------------- | ------- | ---------- |
| • Difficulty. |     | A meaningful | evaluation | should | exhibit |               |           |                |         |            |
that,wefollowmorethan100collegecoursesfrom19de-
thecapabilitytoeffectivelydiscriminatebetweenthe
performanceofdifferentmodelsandprovidevaluable partmentsatTsinghuaUniversityandcollectchallenging
problemsfromtheirexams,textbooks,quizzes,homework,
| insightsforguidingmodelimprovement. |     |     |     |     | Atpresent, |     |     |     |     |     |
| ----------------------------------- | --- | --- | --- | --- | ---------- | --- | --- | --- | --- | --- |
etc. Aftermultipleroundsofrigorousscreeningbyexperts
foundationmodelsaredevelopingrapidly,andsome
andmodels,wefinallyselect1,094questionsspanning108
simplebenchmarkshavebeensaturatedandcannotpro-
subjectsforlanguagemodelsreasoningtest,and665ques-
videguidanceanddiscriminationforadvancedmodels.
tionscovering83subjectsformultimodalmodelsreasoning
• Multimodality. Weliveinamultimodalworld,con- test. Wewillpresentthedetailedscreeningprocessinthe
stantlyprocessingvariousvisualandlinguisticsignals. Sec2. AfterbuildingtheR-Benchbenchmark,wetestthe
Therefore,anidealbenchmarkshouldbedesignedto reasoningcapabilitiesofvariouspowerfulproprietarymod-
assessbothLLMsandMLLMs. elssuchaso1(OpenAI,2024b),GPT-4o(OpenAI,2024a),
Gemini(Teametal.,2023),Claude(Anthropic,2024a),and
| • Multilingualism. |     | We  | believe | that performing | com- |     |     |     |     |     |
| ------------------ | --- | --- | ------- | --------------- | ---- | --- | --- | --- | --- | --- |
open-sourcedmodelssuchasLlama3(Touvronetal.,2023),
plexreasoningismorechallengingthanunderstanding Qwen2.5(Yangetal.,2024),etc. Fromexperiments,our
| multiplelanguages. |     | Amodelwithrobustcomplexrea- |     |     |     |     |     |     |     |     |
| ------------------ | --- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
observationsandfindingsaresummarizedasfollows:
soningskillsshouldbecapableofsolvingreasoning
| problemsacrossdifferentlanguages. |     |     |     | Thisislikefora |     |     |     |     |     |     |
| --------------------------------- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- | --- |
• Withtheemergenceofadvancedmodelslikeo1,exist-
humanexpert,heorshewillnotlosetheabilitytoad-
ingmultidisciplinaryevaluationshavenearlyreached
| dressproblemsduetolanguagechanges. |     |     |     |     | Thus,assess- |             |                                      |     |     |     |
| ---------------------------------- | --- | --- | --- | --- | ------------ | ----------- | ------------------------------------ | --- | --- | --- |
|                                    |     |     |     |     |              | saturation. | Besides,solelyrelyingonmathproblems, |     |     |     |
ingmodelperformanceonequallydifficultquestions
e.g.,mathematicalolympiadproblems,maybringbias
| across | languages | is essential. | It  | will provide | insight |                    |     |                             |     |     |
| ------ | --------- | ------------- | --- | ------------ | ------- | ------------------ | --- | --------------------------- | --- | --- |
|        |           |               |     |              |         | inmodelevaluation. |     | Therefore,thecommunityneeds |     |     |
intowhetherthemodelhasgenuinelylearnedtoreason
|     |     |     |     |     |     | challenging |     | multi-disciplinary | benchmarks | to guide |
| --- | --- | --- | --- | --- | --- | ----------- | --- | ------------------ | ---------- | -------- |
orismerelyoverfittingtoaspecificlanguage.
foundationalmodelsinenhancingtheirreasoningabil-
ities,andthegoalofR-Benchistoaddressit.
Whiletherehavebeenattemptstocreateanidealreasoning
benchmark,tothebestofourknowledge,existingbench- • We illustrate from three dimensions — expert scor-
marks cannot incorporate all four of these key properties ing,modelscoring,andmodelthinkingtime—that
simultaneously. Here,wetakesomewidelyusedreasoning R-Bench is a more complex benchmark with higher
2

R-Bench
requirementsformodelreasoningcomparedtoexisting
multidisciplinarybenchmarksMMLUandMMMU.
Step1:
Step2:Experts collectand
| • Multimodalcomplexreasoningremainschallenging. |     |     |     |     |     |     | Define a list  |     |     |     |     |
| ----------------------------------------------- | --- | --- | --- | --- | --- | --- | -------------- | --- | --- | --- | --- |
selectreasoningquestions
| Despiterapidadvances,modelslagbehindtext-based |     |     |     |     |     |     | of collected  |     |     |     |     |
| ---------------------------------------------- | --- | --- | --- | --- | --- | --- | ------------- | --- | --- | --- | --- |
KQ
disciplines
| reasoning. | Forinstance,GPT-4oscores53.6%ontext |     |     |     |     |     |     |     |     |     |     |
| ---------- | ----------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
RQ
butonly33.7%inmultimodalreasoningonR-Bench.
| • ChainofThought(CoT)canenhancereasoningabili- |     |     |     |     |     | Step3: |     |     |     |      |      |
| ---------------------------------------------- | --- | --- | --- | --- | --- | ------ | --- | --- | --- | ---- | ---- |
| tiesinmostchatmodels,suchasGPT-4o.However,for  |     |     |     |     |     |        |     |     |     | Some | Some |
Digitize the questions
|     |     |     |     |     |     |     |     |     | text-only  |     | multimodal |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ---------- | --- | ---------- |
reasoningmodelslikeo1-mini,CoTdoesnothavethe
|             |     |                                 |     |     |     |     |     |     | questions |     | questions |
| ----------- | --- | ------------------------------- | --- | --- | --- | --- | --- | --- | --------- | --- | --------- |
| sameeffect. |     | Thismaybebecausereasoningmodels |     |     |     |     |     |     |           |     |           |
inherentlybuildCoT,makingexplicitCoTineffective.
• ModelsmaintainhighconsistencyinansweringChi-
neseandEnglishquestionsofequaldifficulty,exceed- Step4:o1 model rescreen
|     |     |     |     |     |     | Step5:The third round  |     |     | based on reasoning difficulty |     |     |
| --- | --- | --- | --- | --- | --- | ---------------------- | --- | --- | ----------------------------- | --- | --- |
ing70%formostmodels,demonstratingstrongcross-
screensfor completeness,
| lingualreasoningcapabilities. |     |     |     |     |     |               |           |     |     | <2000 |     |
| ----------------------------- | --- | --- | --- | --- | --- | ------------- | --------- | --- | --- | ----- | --- |
|                               |     |     |     |     |     | repetitionand | ambiguity |     |     |       |     |
AQ
| • Foundation | models     | perform | differently      | across      | disci- |     |     |     |     |     |       |
| ------------ | ---------- | ------- | ---------------- | ----------- | ------ | --- | --- | --- | --- | --- | ----- |
| plines.      | Specifical | ly , G  | P T -4o achieves | 30.4%–68.3% |        |     |     |     |     |     |       |
|              |            | ℛ -B e  | nc h- T          |             |        |     |     | CQ  |     |     | >2000 |
accuracyacrossvariousfields.
2.R-Bench
!-Bench
|     |     |     |     |     |     |     |     |     | Step6: | Constructing  |     |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ------ | ------------- | --- |
optionsandtranslations
Inthissection,wewillthoroughlyintroducetheconstruc-
| tionprocessofR-Bench. |     |                              |     |     |     | ℛ-Bench |     | ℛ-Bench |     |     |     |
| --------------------- | --- | ---------------------------- | --- | --- | --- | ------- | --- | ------- | --- | --- | --- |
|                       |     | Theentireprocessinvolvesmul- |     |     |     | -T      |     | -T(zh)  |     |     |     |
tiplestepssuchasdatacollection,filteringandimproving.
|     |     |     |     |     |     | ℛ-Bench |     | ℛ-Bench |     |     |     |
| --- | --- | --- | --- | --- | --- | ------- | --- | ------- | --- | --- | --- |
TheoverallpipelineisillustratedinFig.2.
|     |     |     |     |     |     | -M  |     | -M(zh) |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | --- | ------ | --- | --- | --- |
2.1.Datacollection
Before gathering reasoning questions, we conducted an Figure2.PipelineofbuildingR-Bench. Theprocessisdivided
intosixsteps,whicharedetailedinSec.2.Thefunnelrepresents
| investigation | of the | curriculum | systems | of graduate | and |     |     |     |     |     |     |
| ------------- | ------ | ---------- | ------- | ----------- | --- | --- | --- | --- | --- | --- | --- |
screening.WealwaysRfiBlteenrcohuttheblueballandpreservethebrown
undergraduatestudentsacross19diffeCrentdepDartmentsat
-TC
TsinghuaUniversity. Basedonoursurvey,weobtaineda one. InStep2,KQandRQdenoteknowledge-basedquestions
|     |     |     | B   |     | E   | andreasoning-basedquestions,respectively. |     |     |     | InStep4,<2000 |     |
| --- | --- | --- | --- | --- | --- | ----------------------------------------- | --- | --- | --- | ------------- | --- |
collectionlistcoveringover100coursesacross19depart-
indicatesthatthereasoningtokensofo1arelessthan2000.Finally,
| ments,whichisshowninFig.2Step1. |     |     |     |     |     | RBench        |                                             | RBench |     |     |     |
| ------------------------------- | --- | --- | --- | --- | --- | ------------- | ------------------------------------------- | ------ | --- | --- | --- |
|                                 |     |     | A   |     |     | inStep5,-AMQE | andCQ-rMeCpresentambiguousquestionsandclear |        |     |     |     |
F
After acquiring a collection list, we recruit senior under- questions,respectively. -Tindicatestext-onlytestingforLLMs.
graduatesandgraduatestudentsfromdifferentdepartments -Mmeansmultimodaltesting.zhrepresentstheChineseversion.
| asexpertstoprovidereasoningquestion-answerpairs. |     |     |     |     | We  |     |     |     |     |     |     |
| ------------------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
recruitatotalof51experts,withatleasttwoparticipants
| from each                          | department,   | to  | help us collect | and filter      | ques-     |            |           |        |                  |         |              |
| ---------------------------------- | ------------- | --- | --------------- | --------------- | --------- | ---------- | --------- | ------ | ---------------- | ------- | ------------ |
|                                    |               |     |                 |                 |           | After the  | two steps | above, | we collected     | a total | of 10,270    |
| tions. Duringthecollectionprocess, |               |     |                 | wemainlyfocuson |           |            |           |        |                  |         |              |
|                                    |               |     |                 |                 |           | questions. | Among     | them,  | 7,163 questions, |         | which do not |
| controlling                        | the following |     | key aspects:    | 1) The          | questions |            |           |        |                  |         |              |
includeimages,aredesignatedfortestinglanguagemodels,
should align with the collection list we provide. 2) The whiletheremaining3,107questions,containingimages,are
professionalexpertiseshouldfilterout“knowledge-based”
allocatedfortestingmultimodalmodels.
| questions—those |                                    | that rely | solely on memory | rather | than     |     |     |     |     |     |     |
| --------------- | ---------------------------------- | --------- | ---------------- | ------ | -------- | --- | --- | --- | --- | --- | --- |
| reasoning,      | suchasconcept-definitionquestions. |           |                  |        | Simulta- |     |     |     |     |     |     |
2.2.Datadigitization
| neously, | experts should | retain | reasoning-based |     | questions |     |     |     |     |     |     |
| -------- | -------------- | ------ | --------------- | --- | --------- | --- | --- | --- | --- | --- | --- |
andensuretheypresentasufficientdegreeofdifficulty. 3) Afterinitiallycollectingthequestions,wefindthatthecol-
Allquestionsshouldhavecorrespondinganswersthatcan lectedquestionsareinamessyformat,includingpictures,
beautomaticallyverified. Inthiscollectingprocess,weex- screenshots, text, etc. In addition, the summary question
cludeproof-basedquestions,ascurrentautomatedmethods filesprovidedbydifferentexpertsarealsodifferent,includ-
cannotverifythecorrectnessofproofs. Theprocessabove ingpdf,word,excel,etc. Therefore,weneedtoorganize
| isshowninFig.2Step2. |     |     |     |     |     | anddigitizethisdata. |     |     |     |     |     |
| -------------------- | --- | --- | --- | --- | --- | -------------------- | --- | --- | --- | --- | --- |
3

R-Bench
Examples for language models reasoning ability test
|     |     | Major: computerscience; |     |     |     | Major:math;                        |     |     |     |     |
| --- | --- | ----------------------- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --- |
|     |     | Subject:data structure. |     |     |     | Subject:complex variable function. |     |     |     |     |
In the undirected graph G=(V,E) where
V={1,2,3,4,5,6,7} and E={(1,2), (1,3), (2,3),  English Chinese
(4,5), (3,6), (4,7), (5,7)}, how many different  Consider the polynomial  p  (  z  )  =   z  5  +   z  3  +   5  z  2  +   2  . p(z)=z5+z3+5z2+2。
|     | s p a n n  | i n g   f o r e s ts   d o e s   th e   gr | a p h  G  c o n t a in ?      |           |                               |                                     | 考虑多项式                         |     |     |     |
| --- | ---------- | ------------------------------------------ | ----------------------------- | --------- | ----------------------------- | ----------------------------------- | ----------------------------- | --- | --- | --- |
|     |            |                                            |                               | H o w   m | a n y  z e r o  p o in t s  ( | c o u n ti n g   m u ltiplicities)  | 在环域 1<|z|<2 中p 有多少个零点(计入重数)?  |     |     |     |
|     | N o t e :  | I f  t h e   e d g e   s e ts   o f  t w o |  s p a nn in g   f o re s ts  | do e s  p | h a v e  i n   th e  a n n u  | l u s  1 < |z | < 2  ?              |                               |     |     |     |
are different, they are considered different
|     | spanning forests.  |     |     | A:5   B:3 |     |     | A:5   B:3 |     |     |     |
| --- | ------------------ | --- | --- | --------- | --- | --- | --------- | --- | --- | --- |
C:2   D:其他答案都不正确
C:2   D:All other answers are incorrect
|     | A:14   B:9  C:10   D:12  E:11   |     |     | E:4   F :6 |     |     | E:4   F :6 |     |     |     |
| --- | ------------------------------- | --- | --- | ---------- | --- | --- | ---------- | --- | --- | --- |
F:All other answers are incorrect
Examples for multimodal models reasoning ability test
Major:mechanical engineering;
Subject:theoretical mechanics.
|     |     | English |     |     |     |     | Chinese |     |     |     |
| --- | --- | ------- | --- | --- | --- | --- | ------- | --- | --- | --- |
Thewidthofthebrickclampis25cm,and
|     | thecurvedrodsAGBandGCEDarehinged        |                               |     |     |     | 砖夹的宽度为            | 25 cm , 曲杆 AGB |     |     |     |
| --- | --------------------------------------- | ----------------------------- | --- | --- | --- | ----------------- | -------------- | --- | --- | --- |
|     |                                         |                               |     |     |     | 与 在               | 点铰接，尺寸如图       |     |     |     |
|     | atpointG,withdimensionsasshowninthe     |                               |     |     |     | GCED              | G              |     |     |     |
|     | figure.Supposetheweightofthebrickis     |                               |     |     |     | 所示。设砖重            | Q=120N 提起砖的力   |     |     |     |
|     | Q=120NandtheforcePthatliftsthebrick     |                               |     |     |     | P 作用在砖夹的中心线上，砖夹与砖 |                |     |     |     |
|     |                                         |                               |     |     |     | 间的摩擦系数            | f=0.5, 若想把砖夹起， |     |     |     |
|     | actsalong                               | thecenterlineofthebrickclamp. |     |     |     |                   |                |     |     |     |
|     | Thecoefficientoffrictionbetweenthebrick |                               |     |     |     | 试求距离b的最大值？        |                |     |     |     |
clampandthebrickisf=0.5.Determinethe
A : 9 cm         B : 11 cm         C : 15 cm
maximumvalueofdistancebrequiredtolift
|     | thebrickusingtheclamp. |     |     |     |     | D:20 cm        E : 25 cm |     |     |     |     |
| --- | ---------------------- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- |
F: 其他答案都不正确
|     |     | A : 9 cm         B : 11 cm | C : 15 cm |     |     |     |     |     |     |     |
| --- | --- | -------------------------- | --------- | --- | --- | --- | --- | --- | --- | --- |
D : 20 cm       E : 25 cm        F : All other answers are incorrect
Figure3.SomeexamplesinR-Bench.TheseexamplesshowthatR-Benchismultidisciplinary,multimodal,andmultilingual.Asshown
inthefigure,theproblemsinR-Bencharecomplexandcannotbesolvedbyquickthinking,whichshowsthatR-Benchfocusesondeep
reasoningproblemsratherthanknowledgeproblems,suchasconceptualproblems.
| To      | do so, | we recruit a data    | annotation      | team of | about 20    | 2.3.Datafiltering |     |     |     |     |
| ------- | ------ | -------------------- | --------------- | ------- | ----------- | ----------------- | --- | --- | --- | --- |
| people. |        | They are responsible | for organizing, |         | digitizing, |                   |     |     |     |     |
AsshowninFig.2,thefunnelsinsteps2,4,and5represent
checking,andcompilingallthequestionsintoExcelsheets.
threedifferentroundsofdatafiltering.Thesethreeroundsof
Thequestionsusedforlanguagemodelsareorganizedin
screeningrepresentexpertscreening,model-basedfiltering,
thefollowingformat:
andmanualreview.
| “Department                                    |     | - Subject - | Question (text) | - Answer | (text)    | -   |     |     |     |     |
| ---------------------------------------------- | --- | ----------- | --------------- | -------- | --------- | --- | --- | --- | --- | --- |
| OriginalQuestion(text,screenshots,photos,etc.) |     |             |                 |          | -Original |     |     |     |     |     |
Answer(text,screenshots,photos,etc.)”. Expert-screening. AsmentionedinSec.2.1,werecruit
expertsfromdifferentdepartmentstoprovidequestionsfor
Asforquestionsdesignedformultimodalmodels,theyare
|     |     |     |     |     |     | us. They primarily | rely on their | professional | knowledge |     |
| --- | --- | --- | --- | --- | --- | ------------------ | ------------- | ------------ | --------- | --- |
organizedintothefollowingformat:
tofilterout“knowledge-based”questionswhileretaining
“reasoning-based”questions.
| “Department |     | - Subject - | Question (text) | - Answer | (text) | -   |     |     |     |     |
| ----------- | --- | ----------- | --------------- | -------- | ------ | --- | --- | --- | --- | --- |
QuestionImages-OriginalQuestion(text,screenshots,pho-
tosetc.) -OriginalAnswer(text,screenshots,photosetc.)”.
|     |     |     |     |     |     | Model-screening. | OpenAI | o1 (OpenAI, | 2024b) | is a |
| --- | --- | --- | --- | --- | --- | ---------------- | ------ | ----------- | ------ | ---- |
Inthisprocess,weutilizetoolssuchasGPT-4oandMathpix widely used reasoning model. When we call its API, it
|     |     |     |     |     |     | returns the | number of reasoning | tokens, | which, | to some |
| --- | --- | --- | --- | --- | --- | ----------- | ------------------- | ------- | ------ | ------- |
forOCRprocessing,followedbymanualproofreadingto
ensureitiscorrect. Afterthedatateamorganizesthedata, extent,reflectsthedifficultyofthequestion. Inthisround
weperformadouble-checkontheOCRresults. ofscreening,wemainlyfocusonthedifficultyofreasoning.
Wefilteroutthequestionswithlessthan2,000reasoning
tokenstoensurethatourR-Benchisabenchmarkforrea-
soningevaluation.
4

R-Bench
 Inorganic Chemistry
 Analytical Mechanics
|                 |     |     |              |     |     |   C h | e m i c a l   T h e r m o d y n a m i c s |     |     |     |     |     |   F u n d a m e n t a l  P  | h ysics |     |
| --------------- | --- | --- | ------------ | --- | --- | ----- | ----------------------------------------- | --- | --- | --- | --- | --- | --------------------------- | ------- | --- |
|   C h e m i s t | ry  |     |              |     |     |   P h | y s i c a l   C h e m i s t r y           |     |     |     |     |     |                             |         |     |
|   M a t h       |     |     | 3.7%3.7%3.4% |     |     |       |                                           |     |     |     |     |     |   E l e c t r o m a g n e t | i s m   |     |
4.4%   C h e m i c a l   R e a c t i o n   K i n e t i c s 4.1%3.9%3.8%   E l e c t r o d y n a m i c s
|   P h y s ic s |     |     |     | 2.9% |     |   C o | m p u t a t i o n a l   C h e m i s t r y |     |     |     |     |     |     |     |     |
| -------------- | --- | --- | --- | ---- | --- | ----- | ----------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
  C o m p u t e r S. 4.7% 2.3%   O rg a n i c   C h e m i s t r y 4.7% 3.8%   O p t i c s
|   E l e c t ro n | ic  E. |      |     |     | 2.3 % |                    |      |     |      |     | 3.5% |      |     |     |     |
| ---------------- | ------ | ---- | --- | --- | ----- | ------------------ | ---- | --- | ---- | --- | ---- | ---- | --- | --- | --- |
|                  |        | 4.8% |     |     | 2 .1% |  Quantum Chemistry |      |     | 4.8% |     |      |      |     |     |     |
|   A e r o s pa   | c e    |      |     |     |       |                    |      |     |      |     |      | 3.2% |     |     |     |
|  Automation      |        |      |     |     | 2 %   |                    |      |     |      |     |      |      |     |     |     |
|                  |        |      |     |     | 1 .6% |                    |      | 5%  |      |     |      | 2.7% |     |     |     |
|  Statistics      |        | 5.4% |     |     |       |                    |      |     |      |     |      | 2.6% |     |     |     |
|  Mechanical E.   |        |      |     |     |       |                    | 4.6% |     |      |     |      |      |     |     |     |
 Materials
|                  |     |      |     |     |     |       |      | 5.9% |     |     |     |     | 3.8%    |     |     |
| ---------------- | --- | ---- | --- | --- | --- | ----- | ---- | ---- | --- | --- | --- | --- | ------- | --- | --- |
|   B i o l o g y  |     | 7.3% |     |     |     |       |      |      |     |     |     |     |         |     |     |
|   C i v i l  E . |     |      |     |     |     | 14.7% | 4.4% |      |     |     |     |     | 9% 1.5% |     |     |
1.5%
|  Vehicle E.       |        |      |     |     |     |      |      | 6.6% |      |     |       |     |      |     |     |
| ----------------- | ------ | ---- | --- | --- | --- | ---- | ---- | ---- | ---- | --- | ----- | --- | ---- | --- | --- |
|  Physics E.       |        |      |     |     |     |      | 2%   |      |      |     |       |     | 1.4% |     |     |
|   C h e m i ca    | l   E. | 8.1% |     |     |     |      |      |      |      |     |       |     |      |     |     |
|                   |        |      |     |     |     | 1.8% | 1%   |      |      |     |       |     | 0.9% |     |     |
|   Ec o n o m      | i c s  |      |     |     |     |      |      |      |      |     | 10.2% |     |      |     |     |
|  Microelectronics |        |      |     |     |     |      | 0.5% |      | 7.5% |     |       |     |      |     |     |
17.2%
|  Environment  |     |                          | 9.4% |     |     |     | 0.5% |     |                          |     |      |     |     |     |     |
| ------------- | --- | ------------------------ | ---- | --- | --- | --- | ---- | --- | ------------------------ | --- | ---- | --- | --- | --- | --- |
|  Architecture |     |                          |      |     |     |     |      |     | 9.2%                     |     | 9.8% |     |     |     |     |
|               |     | (a)StatisticsofR-Bench-T |      |     |     |     |      |     | (b)StatisticsofR-Bench-M |     |      |     |     |     |     |
Figure4.AccordingtostatisticsonR-Bench,thebenchmarkspans19departments,includingmathematics,physics,biology,computer
science,andchemistry,coveringover100subjectssuchasInorganicChemistry,ChemicalReactionKinetics,andElectromagnetism.It
features1,094questionsdesignedfortestinglanguagemodelsand665questionsspecificallytailoredforevaluatingmultimodalreasoning
capabilities.Foradetailedlistofsubjects,pleaserefertotheappendix.
2.5.OverviewofR-Bench
| Manualreview. |     | Ourmanualreviewfocusesonwhether |     |     |     |     |     |     |     |     |     |     |     |     |     |
| ------------- | --- | ------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
thequestionconditionsarecomplete,whetherthequestions
|                                                    |     |     |     |     |     |     | After    | completing | the             | aforementioned |                   | steps, | we develop   |     |     |
| -------------------------------------------------- | --- | --- | --- | --- | --- | --- | -------- | ---------- | --------------- | -------------- | ----------------- | ------ | ------------ | --- | --- |
| arerepeated,whetherthequestionsareambiguous,andthe |     |     |     |     |     |     | R-Bench, |            |                 |                |                   |        |              |     |     |
|                                                    |     |     |     |     |     |     |          | a          | graduate-level, |                | multi-discipline, |        | multilingual |     |     |
balanceofsubjects.
benchmarkdesignedtoevaluatecomplexreasoningcapa-
Checking for completeness, repetition, and ambiguity re- bilitiesforbothlanguageandmultimodalmodels. Fig.3
quire multiple rounds of thorough review by different in- illustrates several examples from R-Bench, clearly high-
dividuals to eliminate ambiguities, along with the use of lightingitsabovedistinctivefeatures.
| duplicationdetectiontoolstoavoidrepetition. |     |           |         |           |         | Asforthe |         |     |            |     |           |                 |     |     |     |
| ------------------------------------------- | --- | --------- | ------- | --------- | ------- | -------- | ------- | --- | ---------- | --- | --------- | --------------- | --- | --- | --- |
|                                             |     |           |         |           |         |          | R-Bench | can | be divided |     | into four | sub-benchmarks: |     | R-  | 1   |
| balance check,                              |     | to reduce | testing | bias from | subject | imbal-   |         |     |            |     |           |                 |     |     |     |
Bench-TandR-Bench-T(zh)forlanguagemodelevaluation,
| ance, we | limit | the number | of questions |     | per | subject | to a      |     |               |     |     |                |     |       |     |
| -------- | ----- | ---------- | ------------ | --- | --- | ------- | --------- | --- | ------------- | --- | --- | -------------- | --- | ----- | --- |
|          |       |            |              |     |     |         | R-Bench-M |     | R-Bench-M(zh) |     |     |                |     |       |     |
|          |       |            |              |     |     |         |           |     | and           |     |     | for multimodal |     | model |     |
maximumof50byfilteringoutexcess.
|     |     |     |     |     |     |     | evaluation.    | Here,R-Bench-TdenotesR-Benchusingtext- |            |     |         |             |         |     |     |
| --- | --- | --- | --- | --- | --- | --- | -------------- | -------------------------------------- | ---------- | --- | ------- | ----------- | ------- | --- | --- |
|     |     |     |     |     |     |     | only questions |                                        | in English |     | for LLM | evaluation, | whereas |     |     |
2.4.Conductingoptionsandtranslations
R-Bench-T(zh)representsR-Benchusingtext-onlyques-
|          |           |         |       |           |               |     | tionsinChineseforLLMevaluation. |     |     |     |     | Likewise,theother |     |     |     |
| -------- | --------- | ------- | ----- | --------- | ------------- | --- | ------------------------------- | --- | --- | --- | --- | ----------------- | --- | --- | --- |
| In order | to enable | answers | to be | evaluated | automatically |     |                                 |     |     |     |     |                   |     |     |     |
and accurately, we convert all questions such as analyti- twonotationsfollowthesamenamingconvention.
cal,fill-in-the-blank,andmultiple-choicequestionsintothe
WeconductstatisticalanalysisonR-Benchwiththeresults
single-choicequestionformat. WeuseGPT-4otoconstruct ItpresentstheR-Bench-Tstatisticsfor
presentedinFig.4.
| 5 options | for each | question | and | add an | option | “All | other |     |     |     |     |     |     |     |     |
| --------- | -------- | -------- | --- | ------ | ------ | ---- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
text-onlyquestionsusedinevaluatingthereasoningcapabil-
answersareincorrect”,whichequipseachquestionwith6 R-Bench-Tspans18departments,
itiesoflanguagemodels.
| candidate                                     | answers. | Then, | we check | the | options | multiple |                       |            |              |          |            |         |                |     |     |
| --------------------------------------------- | -------- | ----- | -------- | --- | ------- | -------- | --------------------- | ---------- | ------------ | -------- | ---------- | ------- | -------------- | --- | --- |
|                                               |          |       |          |     |         |          | includingmathematics, |            |              | biology, | chemistry, |         | computersci-   |     |     |
| timestoensurethecorrectnessofourconstruction. |          |       |          |     |         | Further- |                       |            |              |          |            |         |                |     |     |
|                                               |          |       |          |     |         |          | ence,                 | electronic | engineering, |          | and        | others. | It encompasses |     |     |
more,wemanuallyadjusttheoptionstoensureasufficient
over108subjects,suchascalculus,numbertheory,analytic
numericalgapbetweenthem,therebyavoidingerrorscaused
|     |     |     |     |     |     |     | geometry, | ordinary |     | differential | equations, |     | and functional |     |     |
| --- | --- | --- | --- | --- | --- | --- | --------- | -------- | --- | ------------ | ---------- | --- | -------------- | --- | --- |
bynumericalapproximations. analysis,andcomprisesatotalof1,094questions. Fig.4
alsopresentsthestatisticsofR-Bench-M,whichevaluates
Besides,inordertoenableR-Benchtoacquirethemulti-
|     |     |     |     |     |     |     | thereasoningcapabilitiesofmultimodalmodels. |     |     |     |     |     | R-Bench- |     |     |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------------------- | --- | --- | --- | --- | --- | -------- | --- | --- |
lingualproperty,wemanuallyconstructedEnglish-Chinese
translationsforeachquestion. Duringthetranslationpro- Mincorporatesadiversesetofquestiontypesrequiringboth
cess,weutilizetoolslikeGPT-4o. Eachquestionismeticu- textualandvisualinputs. Itcovers18departments,suchas
physics,biology,architecture,andeconomics,andincludes
louslyreviewedandrefinedbythreeexpertsfluentinboth
EnglishandChinesetoensurecorrectnessandclarity. 83 subjects, such as thermodynamics, molecular biology,
structuraldesign,andmicroeconomics,includingatotalof
|     |     |     |     |     |     |     | 665questions. |     | ItisworthnotingthatweprovideEnglish |     |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ------------- | --- | ----------------------------------- | --- | --- | --- | --- | --- | --- |
andChineseversionsforallquestions.
5

R-Bench
Table2.Comparisonofreasoningrequirementsforproblemsin Table4.Theaveragethinkingtimeofo1on30randomlyselected
R-Bench-TandMMLUviaexpertando1voting. samplesfromdifferentbenchmarks.TTdenotesthinkingtime.
|     |     | R-Bench-Twin |     | MMLUwin | Tie |     | MMLU |     | R-Bench-T | MMMU |     | R-Bench-M |     |
| --- | --- | ------------ | --- | ------- | --- | --- | ---- | --- | --------- | ---- | --- | --------- | --- |
Expertvoting 85.94% 10.62% 3.44% TT 13.5s 98.2s(7.3×) 20.3s 91.7s(4.5×)
| o1voting |     | 76.67% |     | 20.00% | 3.33% |     |     |     |     |     |     |     |     |
| -------- | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
Table3.Comparisonofreasoningrequirementsforproblemsin
R-Bench-MandMMMUviaexpertando1voting.
3.2.Evaluatingreasoningcapabilityofdifferentmodels
R-Bench-Mwin
|     |     |     |     | MMMUwin | Tie |     |     |     |     |     |     |     |     |
| --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
WeemployR-Bench-Ttoassessthereasoningcapabilities
| Expertvoting |     | 76.88% |     | 15.94% | 7.19% |     |     |     |     |     |     |     |     |
| ------------ | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
ofvariousLLMssuchaso1(OpenAI,2024b),GPT-4o(Ope-
| o1voting |     | 83.33% |     | 13.33% | 3.33% |     |     |     |     |     |     |     |     |
| -------- | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
nAI,2024a),DeepSeek-R1(AI,2025),Gemini(Teametal.,
2024),Claude3.5(Anthropic,2024b),Qwen2.5(Yangetal.,
|     |     |     |     |     |     | 2024),              | Llama3 | (Dubey | et                                | al., 2024), | etc, | in both | English |
| --- | --- | --- | --- | --- | --- | ------------------- | ------ | ------ | --------------------------------- | ----------- | ---- | ------- | ------- |
|     |     |     |     |     |     | andChinesesettings. |        |        | TheevaluationinvolvesutilizingAPI |             |      |         |         |
3.Experiments
|     |     |     |     |     |     | callsanddeployingopen-sourcemodelslocally. |     |     |     |     |     |     | ForAPI |
| --- | --- | --- | --- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | --- | ------ |
calls,weutilizetheofficialinterfaceswithdefaulthyperpa-
AfterdevelopingR-Bench,weutilizeittoassessthecom-
rameters. Foropen-sourcemodels,wedeploytheirweights
plexreasoningcapabilitiesofvariousLLMsandMLLMs,
|     |     |     |     |     |     | locally | usingvLLM |     | (Kwon | et al.,2023), |     | settingthe | tem- |
| --- | --- | --- | --- | --- | --- | ------- | --------- | --- | ----- | ------------- | --- | ---------- | ---- |
includingbothopen-sourcemodelssuchasLlamaandclose-
sourcemodelssuchasGPT-4o. Firstly,weaimtodemon- perature to 0 while keeping all other parameters at their
stratethatR-Benchisabenchmarkforcomplexreasoning default values. The evaluation was conducted using the
|     |     |     |     |     |     | toolsprovidedbyOpenCompass(Contributors,2023). |     |     |     |     |     |     | In  |
| --- | --- | --- | --- | --- | --- | ---------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
throughexpertscoring(userstudy)ando1modelscoring.
|     |     |     |     |     |     | alltests,theCoTpromptisusedbydefault. |     |     |     |     |     | Fordetailson |     |
| --- | --- | --- | --- | --- | --- | ------------------------------------- | --- | --- | --- | --- | --- | ------------ | --- |
Then,weevaluatethereasoningcapabilitiesofmodelswith
|                                               |     |     |     |     |     | thespecificprompts, |     |     | pleaserefertoourappendix. |     |     |     | Inthe |
| --------------------------------------------- | --- | --- | --- | --- | --- | ------------------- | --- | --- | ------------------------- | --- | --- | --- | ----- |
| andwithoutCoTpromptingunderazero-shotsetting. |     |     |     |     | Fi- |                     |     |     |                           |     |     |     |       |
resultsshowninTab.5,wefoundthatmodelsdesignedfor
nally,weanalyzetheexperimentsandsummarizeobserva-
|     |     |     |     |     |     | reasoning |     | tasks, | such as o1, | outperform |     | chat models | like |
| --- | --- | --- | --- | --- | --- | --------- | --- | ------ | ----------- | ---------- | --- | ----------- | ---- |
tionsandfindingsfromtheexperimentalprocess.
|     |     |     |     |     |     | GPT-4o | in  | complex | reasoning. | Besides, |     | there | remains a |
| --- | --- | --- | --- | --- | --- | ------ | --- | ------- | ---------- | -------- | --- | ----- | --------- |
significantgapincomplexreasoningbetweenopen-source
3.1.Reasoningcomparisonwithotherbenchmarks
modelsandcommercialmodels.
ToillustratethatR-Benchisabenchmarkdesignedtoeval-
R-Bench-M
|                |          |     |           |              |        | Moreover, |     | we utilize |     |     | to evaluate | the | reason- |
| -------------- | -------- | --- | --------- | ------------ | ------ | --------- | --- | ---------- | --- | --- | ----------- | --- | ------- |
| uate reasoning | ability, |     | we employ | two methods: | expert |           |     |            |     |     |             |     |         |
ingcapabilitiesofvariousMLLMs,includingo1(OpenAI,
scoringandreasoningmodelscoring.
2024b),GPT-4o(OpenAI,2024a),Claude3.5(Anthropic,
Weconductedexpertscoringthroughuserstudies.Tobespe- 2024b), Qwen2.5-VL (Yang et al., 2024), and InternVL
cific,werandomlyselected30questionsfromR-Bench-T 2.5(Chenetal.,2024),etc,acrossbothEnglishandChinese
andanother30questionsfromMMLUandpresentedthem languages. TheevaluationalsoinvolvesutilizingAPIcalls
to experts for pairwise comparisons to determine which anddeployingopen-sourcemodelslocally. ForAPIcalls,
questionrequiredmorereasoningskillstosolve. Wecon- weutilizetheofficialinterfaceswithdefaulthyperparam-
structed similar experiments using the same settings be- eters. For open-source models, we deploy their weights
tweenR-Bench-MandMMMU. locallyusingVLMEvalKit(Duanetal.,2024),settingthe
temperatureto0whilekeepingallotherparametersattheir
Forreasoningmodelscoring,weadoptedtwoapproaches.
|              |     |      |        |                    |       | defaultvalues. |     | Inalltests,theCoTpromptisusedbyde- |     |     |     |     |     |
| ------------ | --- | ---- | ------ | ------------------ | ----- | -------------- | --- | ---------------------------------- | --- | --- | --- | --- | --- |
| On one hand, | we  | used | the o1 | model to determine | which |                |     |                                    |     |     |     |     |     |
fault. Fordetailsonthespecificprompts,pleaserefertoour
questionrequiredmorereasoningabilitybasedonthenum-
|     |     |     |     |     |     | appendix. |     | We draw | three | conclusions | from | the | results in |
| --- | --- | --- | --- | --- | --- | --------- | --- | ------- | ----- | ----------- | ---- | --- | ---------- |
berofreasoningtokens(reasoningtime);ontheotherhand,
Tab.6. First,wefoundthatmodelsperformworseinmulti-
weaskedtheo1modeltodirectlycomparethetwoquestions
modalcomplexreasoningcomparedtoreasoninginapurely
anddeterminewhichonerequiredmorereasoning.
|     |     |     |     |     |     | linguistic |     | environment. | Second, |     | the reasoning |     | model o1 |
| --- | --- | --- | --- | --- | --- | ---------- | --- | ------------ | ------- | --- | ------------- | --- | -------- |
TheresultsareshowninTab.2,3and4. Theresultsindicate stilldemonstratesoutstandingperformanceinmultimodal
thatbotho1’sjudgmentandtheexperts’judgmentconsider complexreasoningevaluation. Third,thegapbetweenopen-
R-Bench to require significantly higher reasoning ability sourceandclosed-sourcemodelsisevenmorepronounced
| comparedtoMMLUandMMMU. |     |     |     |     |     | inmultimodalcomplexreasoning. |     |     |     |     |     |     |     |
| ---------------------- | --- | --- | --- | --- | --- | ----------------------------- | --- | --- | --- | --- | --- | --- | --- |
6

R-Bench
Table5.PerformancecomparisonofvariousmodelsonR-Bench- Table6.PerformancecomparisonofvariousmodelsonR-Bench-
Tinzero-shotsettingswithCoT.Thetableisdividedbyamiddle Minzero-shotsettingswithCoT.Thetableisdividedbyamiddle
line:API-basedmodelsarelistedabovetheline,whileopen-source line:API-basedmodelsarelistedabovetheline,whileopen-source
modelsareshownbelow.‘zh’indicatestheChineseversion.The modelsareshownbelow.’zh’indicatestheChineseversion.The
valuesinthetablerepresenttheTop-1accuracy,in%. valuesinthetablerepresenttheTop-1accuracy,in%.
|             | R-Bench-T | R-Bench-T(zh) |             |     | R-Bench-M | R-Bench-M(zh) |
| ----------- | --------- | ------------- | ----------- | --- | --------- | ------------- |
| ModelName   |           |               | ModelName   |     |           |               |
| o1-20241217 | 69.0      | 70.1          | o1-20241217 |     | 53.2      | 55.0          |
Gemini-2.0-flash-thinking 68.4 67.5 Claude3.5-sonnet@1022 39.7 38.3
Doubao1.5pro-20250121 62.0 63.4 Doubao1.5pro-20250121 37.9 42.4
| o1-preview@20240912   | 62.3 | 62.6 | GPT-4o-20241120        |     | 33.4 | 33.2 |
| --------------------- | ---- | ---- | ---------------------- | --- | ---- | ---- |
| o1-mini@20240912      | 64.0 | 59.9 | Gemini-1.5-Pro         |     | 35.5 | 35.9 |
| Doubao-pro-20241215   | 60.7 | 60.8 |                        |     |      |      |
|                       |      |      | Qwen2-VL-72B           |     | 25.1 | 25.7 |
| Claude3.5-sonnet@0620 | 57.5 | 57.0 |                        |     |      |      |
|                       |      |      | Qwen2-VL-7B            |     | 19.6 | 22.3 |
| GPT-4o-20241120       | 53.6 | 51.6 |                        |     |      |      |
|                       |      |      | LLaVA-OneVision-7B     |     | 23.8 | 23.5 |
| MiniMax-Text-01       | 53.8 | 53.6 |                        |     |      |      |
|                       |      |      | DeepSeek-VL2           |     | 21.8 | 24.4 |
| GLM-Zero-Preview      | 53.6 | 48.6 |                        |     |      |      |
|                       |      |      | Llama3.2V-11B-Instruct |     | 20.0 | 18.6 |
| ERNIE-4.0-8K-Latest   | 39.7 | 50.1 |                        |     |      |      |
|                       |      |      | InternVL-2.5-8B        |     | 15.9 | 17.1 |
| Deepseek-R1           | 61.2 | 59.3 |                        |     |      |      |
| Deepseek-V3           | 59.6 | 56.6 |                        |     |      |      |
| Qwen3-235B-A22B       | 58.0 | 58.4 |                        |     |      |      |
Table7.AssessingtheperformanceimpactofCoTacrossdifferent
| Qwen3-32B              | 52.3 | 54.3 | modelsonR-Bench-T.    |     |         |           |
| ---------------------- | ---- | ---- | --------------------- | --- | ------- | --------- |
| Qwen2.5-72B-Instruct   | 53.7 | 52.0 |                       |     |         |           |
| Llama-3.3-70B-Instruct | 49.5 | 47.6 | ModelName             |     | wCoT(%) | w/oCoT(%) |
| Qwen2.5-32B-Instruct   | 50.8 | 49.9 |                       |     |         |           |
|                        |      |      | o1-mini@20240912      |     | 64.0    | 64.0      |
| Gemma-2-27b-it         | 36.0 | 38.9 |                       |     |         |           |
|                        |      |      | GPT-4o-20241120       |     | 53.6    | 51.5      |
| Phi-4-14B              | 55.3 | 47.3 | LLAMA3.3-70B-Instruct |     | 49.5    | 47.4      |
| Phi-3-14B              | 29.5 | 24.4 |                       |     |         |           |
|                        |      |      | Qwen2.5-32B-Instruct  |     | 50.8    | 44.6      |
| Qwen3-8B               | 47.5 | 45.9 |                       |     |         |           |
|                        |      |      | Qwen2.5-7B-Instruct   |     | 43.6    | 42.6      |
| InternLM3-8B-Instruct  | 41.1 | 45.8 |                       |     |         |           |
| Qwen2.5-7B-Instruct    | 43.6 | 44.5 |                       |     |         |           |
GLM-4-9b-chat 25.6 32.4 focused modelssuch as o1-mini. We conjecture thatthis
| Llama-3.1-8B-Instruct | 26.1 | 23.6 |     |     |     |     |
| --------------------- | ---- | ---- | --- | --- | --- | --- |
discrepancyarisesbecausereasoningmodelsinherentlyuti-
| Llama-3.2-3B-Instruct | 24.2 | 24.0 |     |     |     |     |
| --------------------- | ---- | ---- | --- | --- | --- | --- |
lizeCoT-likemechanisms,leadingtotheexplicitaddition
ofCoTredundantandineffective.
3.3.Observationsandfindings
|     |     |     | Consistency | between | English and | Chinese questions. |
| --- | --- | --- | ----------- | ------- | ----------- | ------------------ |
Multimodalreasoningremainschallengingforcurrent AsshowninFig.5,wetestedtheconsistencyofthesame
questionacrossdifferentlanguagesonR-Bench-T.Itcanbe
models. Wecomparedtheperformanceofthesamemodel
onR-Bench-TandR-Bench-M.Forexample,o1achieved
observedthatmostmodels,suchaso1,Doubao1.5pro,and
69.0%onR-Bench-Tbutonly53.2%onR-Bench-M.The GPT-4o,exhibitacertaindegreeofconsistencyacrossdif-
samesituationalsooccursinothermodels,suchasGPT-4o. ferentlanguages.Thissuggeststhatfoundationmodelshave
Itindicatesthatthemodel’scapabilityinlanguagereasoning alreadydemonstratedacertainlevelofintelligence,enabling
significantlysurpassesitsabilityinmultimodalreasoning. themtoperformreasoningonproblemsofthesamediffi-
Therefore,akeyfocusofresearchintherecentfuturewill cultyindifferentlinguisticenvironments. However,these
behowtotransferlinguisticintelligencetothemultimodal modelsarenotperfectandstillrequirefurtherimprovement
domain. inthisaspect. Thisconsistencyreflectstheextenttowhich
|     |     |     | themodeloverfitsdifferentlanguages. |     |     | Therefore,wehope |
| --- | --- | --- | ----------------------------------- | --- | --- | ---------------- |
futuremodelswillfocusmoreonlearninghowtoreason
| TheeffectofCoT. InTab.7,wetestedtheeffectofCoT |     |     |     |     |     |     |
| ---------------------------------------------- | --- | --- | --- | --- | --- | --- |
onfivemodels. Asseeninthetable,mostmodelsbenefit ratherthanmerelyfittingtospecificlanguages.
| fromCoT,butithasnoimpactono1-mini. |     | Theresultsindi- |     |     |     |     |
| ---------------------------------- | --- | --------------- | --- | --- | --- | --- |
catethatCoTenhancestheperformanceofchatmodelslike Modelsshowsignificantperformancevariationacross
GPT-4o. However,ithasnonotableimpactonreasoning- disciplines. Fig.6showstheperformanceofGPT-4oin
7

R-Bench
leveragedacrossdiversefieldssuchaswriting,coding,edu-
cation,healthcare,finance,andmore,servingasasource
Consistency of English and Chinese questions with the same difficulty
86.7%
|     |          |     |     |     |     | forprovidingintelligence. |     |     | Now, | foundationmodelshave |     |     |     |
| --- | -------- | --- | --- | --- | --- | ------------------------- | --- | --- | ---- | -------------------- | --- | --- | --- |
|     | 80 78.5% |     |     |     |     |                           |     |     |      |                      |     |     |     |
75.2% 73.5% 73.4% 73.3% becomeanessentialpartofourdailyworkandlife.
|     |     |     | 72.4% 70.5% | 69.3% 69.2% |                   |                                                   |     |     |     |     |     |     |     |
| --- | --- | --- | ----------- | ----------- | ----------------- | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
|     |     |     |             |             | 68.3% 68.0% 66.9% |                                                   |     |     |     |     |     |     |     |
|     | 60  |     |             |             |                   | Tobuildahigh-qualityfoundationmodel,webelievethat |     |     |     |     |     |     |     |
ycnetsisnoC fivekeyaspectsareessential: pre-training(Vaswanietal.,
2017;Raffeletal.,2020;Sunetal.,2023;Radfordetal.,
40
2021;2018),supervisedfine-tuning(Ouyangetal.,2022;
|     |     |     |     |     |     | Liu et al., | 2024b; | Pareja | et  | al., 2024; | Li  | et al., | 2024; |
| --- | --- | --- | --- | --- | --- | ----------- | ------ | ------ | --- | ---------- | --- | ------- | ----- |
20
|     |     |     |     |     |     | Zhu et al., | 2023; | Taori | et al., | 2023), | preference | optimiza- |     |
| --- | --- | --- | --- | --- | --- | ----------- | ----- | ----- | ------- | ------ | ---------- | --------- | --- |
tion(Rafailovetal.,2024;Schulmanetal.,2017;Lightman
|     | 0 7 5 | 2 c t 0 | 0 c t i t | c t uc t -8B-Instruct | B .5-7B-Instruct |     |     |     |     |     |     |     |     |
| --- | ----- | ------- | --------- | --------------------- | ---------------- | --- | --- | --- | --- | --- | --- | --- | --- |
4 1 2 1 4 1 2 1 4 0 9 1 s t r u onnet@ 0 6 2 4 1 1 2 -32B-Inst ru -2 7 b - s tr u s tr 4 -1 4 et al., 2023; Pal et al., 2024; Azar et al., 2024),test-time
|     | 20 2 -2 0 2 @2 | 0 2 B -I n | 20 2 a -2 70B - In | B - In  | Ph i- |     |     |     |     |     |     |     |     |
| --- | -------------- | ---------- | ------------------ | ------- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
|     | o1- -p ro w    | -7 2 s 4o- | m m -              | .1 -8 3 |       |     |     |     |     |     |     |     |     |
a o e v ie n 2 . 5 3 . 5 - G P T - n 2 . 5 G e a- 3 . 3 a - 3 rn L M e n 2 enhancement(Brownetal.,2020;Weietal.,2022;OpenAI,
|     | D o u b 1 -p r | Q w e au d e | Q w e la m L la m | nt e | Q w |                                                  |     |         |         |       |            |       |      |
| --- | -------------- | ------------ | ----------------- | ---- | --- | ------------------------------------------------ | --- | ------- | ------- | ----- | ---------- | ----- | ---- |
|     | o              | C l          | L                 | I    |     | 2024b;DeepSeek,2024;Dongetal.,2022),andtrustwor- |     |         |         |       |            |       |      |
|     |                |              |                   |      |     | thy evaluation                                   |     | (Chiang | et al., | 2024; | Li et al., | 2023; | Chen |
Figure5.Theperformanceofdifferentmodelsonquestionsofthe
|     |     |     |     |     |     | et al., 2021; | Jain | et al., | 2024; | Yu et | al., 2023). | Reliable |     |
| --- | --- | --- | --- | --- | --- | ------------- | ---- | ------- | ----- | ----- | ----------- | -------- | --- |
samedifficultyinChineseandEnglish.
evaluationplaysacrucialroleinrevealingmodels’weak-
nessesandshortcomings,guidingfurtheroptimizationand
improvement,whichisalsothefocusofthispaper.
Accuracy of Different Departments
70 68.3%68.0% Average: 53.6% 4.2.Evaluationforfoundationmodels
62.5%
|     |     | 61.0% |     |     |     | Evaluatingtheintelligenceoffoundationmodelsisamul- |     |     |     |     |     |     |     |
| --- | --- | ----- | --- | --- | --- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
|     | 60  | 58.4% |     |     |     |                                                    |     |     |     |     |     |     |     |
55.9%55.8%55.3%54.5%54.2% tifacetedandcomplexchallenge,akintoassessinghuman
53.6%
)%( ycaruccA 51.0% intelligence. Researchers have introduced various evalu-
|     | 50  |     | 48.0% |     |     |                   |     |         |             |     |      |               |     |
| --- | --- | --- | ----- | --- | --- | ----------------- | --- | ------- | ----------- | --- | ---- | ------------- | --- |
|     |     |     |       |     |     | ation benchmarks, |     | broadly | categorized |     | into | fast-thinking |     |
44.9%
|     |     |     |     |            |     | assessments  | (   | a.k.a., system-I |       | evaluation |            | (Chiang | et al., |
| --- | --- | --- | --- | ---------- | --- | ------------ | --- | ---------------- | ----- | ---------- | ---------- | ------- | ------- |
|     | 40  |     |     | 39.0%38.9% |     |              |     |                  |       |            |            |         |         |
|     |     |     |     |            |     | 2024; Dubois |     | et al., 2024;    | Zheng | et         | al., 2023; | Lin     | et al., |
34.4%33.3%
2021;2024;Luetal.,2022;Yuetal.,2023;Lietal.,2023),
30.4%
30
whichrequiresthefoundationmodelstomemorizeexten-
siveknowledgeandretrieveefficiently,andslow-thinking
|     | Biolog y s  E . tronic E | . erospac e hemistry Ma th utomation | s ic s on ic s hanical E. Statist ic s emica l E . | puter S. Materials Civil E | . e  E . ironme nt conomics |     |     |     |     |     |     |     |     |
| --- | ------------------------ | ------------------------------------ | -------------------------------------------------- | -------------------------- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
hysic Ph y lectr ehicl assessments(a.k.a.,system-IIevaluation(Hendrycksetal.,
|     | P E l ec | A C A | o e e c C h C om |     | V E n v E |     |     |     |     |     |     |     |     |
| --- | -------- | ----- | ---------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
|     |          |       | M i cr M         |     |           |     |     |     |     |     |     |     |     |
2021;Yueetal.,2024a;Luetal.,2023;Wangetal.,2024a;
Liuetal.,2024c;Chenetal.,2021;Jimenezetal.,2023),
R-Bench-T
| Figure6.GPT-4o |     | on  | across | different | departments, |     |     |     |     |     |     |     |     |
| -------------- | --- | --- | ------ | --------- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- |
whichemphasizesthecomplexreasoningskillsoffounda-
whichshowslargevariationamongdifferentdisciplines.. R-Benchfocusesonthelatter.
tionmodels.
MMLU(Hendrycksetal.,2021)isthepioneerinreasoning
different areas of the R-Bench-T benchmark. From the evaluation,whichproposesamulti-disciplineunderstanding
test. Afterthat,lotsofmulti-disciplinebenchmarks(Rein
figure,itcanbeobservedthattheperformancevariessig-
nificantlyacrossdifferentdomains,witharangereaching etal.,2023;Wangetal.,2024b;Yueetal.,2024b)atthe
37.9%. This suggests that if we want to improve the rea- undergraduateorgraduatelevelareproposedforreasoning
|     |     |     |     |     |     | assessment. | However,withtherapiddevelopmentofintelli- |     |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | ----------- | ----------------------------------------- | --- | --- | --- | --- | --- | --- |
soningabilityofmodels,weshouldtakeacomprehensive
approachratherthanfocusingsolelyonimprovementsina gentmodels(OpenAI,2024b),thesebenchmarksareclose
|     |     |     |     |     |     | tosaturation. | Besides,severalstudies(Glazeretal.,2024; |     |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | ------------- | ---------------------------------------- | --- | --- | --- | --- | --- | --- |
singlesubject,suchasmathematics.
Gaoetal.,2024;Luetal.,2023;Wangetal.,2024a)assess
reasoningabilitythroughcomplexmathematicalproblems
4.RelatedWork
|     |     |     |     |     |     | such as | mathematical |     | olympiad | challenges, |     | which | bring |
| --- | --- | --- | --- | --- | --- | ------- | ------------ | --- | -------- | ----------- | --- | ----- | ----- |
4.1.Foundationmodels challengesandguidancetocurrentfoundationmodels.How-
ever,onlyguidingthemodeltoimproveitsmathematical
WiththeemergenceofChatGPT(OpenAI,2022),founda-
|     |     |     |     |     |     | reasoning | skills | appears | to be | limited. | In this | paper, | our |
| --- | --- | --- | --- | --- | --- | --------- | ------ | ------- | ----- | -------- | ------- | ------ | --- |
tionmodels(Ouyangetal.,2022;Touvronetal.,2023;Yang
targetistobuildareliablebenchmarkforLLMandMLLM
etal.,2024;Jiangetal.,2023;Teametal.,2024;Zengetal., reasoning evaluation, which matches the comprehensive-
| 2022; | Bi et | al., 2024; | Liu et al., 2024a; | Cai | et al., 2024; |     |     |     |     |     |     |     |     |
| ----- | ----- | ---------- | ------------------ | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
nessofMMLU(Hendrycksetal.,2021)whileachieving
Anthropic,2024a;Wuetal.,2024)areincreasinglybeing
8

R-Bench
thedifficultyofmathematicalolympiadquestions. Z., Wei, X., Weng, Q., Wu, F., Xiong, Y., Xu, C., Xu,
R.,Yan,H.,Yan,Y.,Yang,X.,Ye,H.,Ying,H.,Yu,J.,
| 5.Conclusion |     |     |     |     |     | Yu,J.,Zang,Y.,Zhang,C.,Zhang,L.,Zhang,P.,Zhang, |     |     |     |     |     |     |
| ------------ | --- | --- | --- | --- | --- | ----------------------------------------------- | --- | --- | --- | --- | --- | --- |
P.,Zhang,R.,Zhang,S.,Zhang,S.,Zhang,W.,Zhang,
Inthispaper,weproposedR-Bench,agraduate-levelmulti- W.,Zhang,X.,Zhang,X.,Zhao,H.,Zhao,Q.,Zhao,X.,
disciplinary, multilingual benchmark for both LLM and Zhou,F.,Zhou,Z.,Zhuo,J.,Zou,Y.,Qiu,X.,Qiao,Y.,
MLLMreasoningevaluation,whichhascoveragesimilar andLin,D. Internlm2technicalreport,2024.
| to MMLU | and MMMU | while reaching |     | the difficulty | of  |     |     |     |     |     |     |     |
| ------- | -------- | -------------- | --- | -------------- | --- | --- | --- | --- | --- | --- | --- | --- |
Chen,M.,Tworek,J.,Jun,H.,Yuan,Q.,Pinto,H.P.D.O.,
| mathematicalcompetitionssuchasAIME@2024. |     |     |     | Weeval- |     |     |     |     |     |     |     |     |
| ---------------------------------------- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
Kaplan,J.,Edwards,H.,Burda,Y.,Joseph,N.,Brockman,
uatedmultipleclosed-sourceandopen-sourcemodelssuch
|           |             |              |     | R-Bench |     | G., etal. | Evaluatinglargelanguagemodelstrainedon |     |     |     |     |     |
| --------- | ----------- | ------------ | --- | ------- | --- | --------- | -------------------------------------- | --- | --- | --- | --- | --- |
| as OpenAI | o1, GPT-4o, | DeepSekk-R1, |     | etc, on |     |           |                                        |     |     |     |     |     |
code. arXivpreprintarXiv:2107.03374,2021.
andobservedboththeprogressandlimitationsofcurrent
modelsinreasoning. Later,wewillmakethedataandcode Chen, Z., Wang, W., Cao, Y., Liu, Y., Gao, Z., Cui, E.,
| available, | hoping to provide | guidance | and | insight | for the |          |         |       |          |           |               |     |
| ---------- | ----------------- | -------- | --- | ------- | ------- | -------- | ------- | ----- | -------- | --------- | ------------- | --- |
|            |                   |          |     |         |         | Zhu, J., | Ye, S., | Tian, | H., Liu, | Z., etal. | Expandingper- |     |
developmentoffoundationmodels.
formanceboundariesofopen-sourcemultimodalmodels
|            |     |     |     |     |     | withmodel,data,andtest-timescaling. |     |     |     |     | arXivpreprint |     |
| ---------- | --- | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | --- | ------------- | --- |
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| --------------------------------------------- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- |
9

R-Bench
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11

R-Bench
| Zheng,L.,Chiang,W.-L.,Sheng,Y.,Zhuang,S.,Wu,Z., |     |     |         | A.Appendix. |     |     |     |     |     |     |
| ----------------------------------------------- | --- | --- | ------- | ----------- | --- | --- | --- | --- | --- | --- |
| Zhuang,Y.,Lin,Z.,Li,Z.,Li,D.,Xing,E.,etal.      |     |     | Judging |             |     |     |     |     |     |     |
A.1.ResponseExample
| llm-as-a-judge | with mt-bench | and chatbot | arena. Ad- |     |     |     |     |     |     |     |
| -------------- | ------------- | ----------- | ---------- | --- | --- | --- | --- | --- | --- | --- |
vances in Neural Information Processing Systems, 36: Here, wepresentourquestionandtheoutputoftheOpe-
46595–46623,2023.
|     |     |     |     | nAI’so1modelintheboxbelow. |     |     |     | Themodelcarriedouta |     |     |
| --- | --- | --- | --- | -------------------------- | --- | --- | --- | ------------------- | --- | --- |
step-by-stepanddetailedreasoningbasedonthequestion
| Zhu, D., | Chen, J., Shen, | X., Li, X., | and Elhoseiny, M. |     |     |     |     |     |     |     |
| -------- | --------------- | ----------- | ----------------- | --- | --- | --- | --- | --- | --- | --- |
andfinallyarrivedatthecorrectanswer.
| Minigpt-4:    | Enhancing      | vision-language | understanding  |     |     |     |     |     |     |     |
| ------------- | -------------- | --------------- | -------------- | --- | --- | --- | --- | --- | --- | --- |
| with advanced | large language | models.         | arXiv preprint |     |     |     |     |     |     |     |
Questionandtheresponsebyo1
arXiv:2304.10592,2023.
|     |     |     |     | Answer | the | following | single choice | question. |     | The |
| --- | --- | --- | --- | ------ | --- | --------- | ------------- | --------- | --- | --- |
lastlineofyourresponseshouldbeofthefollowing
|     |     |     |     | format:                                   | ’ANSWER:    |            | $LETTER’        | (without | quotes)    |        |
| --- | --- | --- | --- | ----------------------------------------- | ----------- | ---------- | --------------- | -------- | ---------- | ------ |
|     |     |     |     | where                                     | LETTER      | is one     | of Options(e.g. |          |            | one of |
|     |     |     |     | ABCDEF).                                  |             | Think step | by step         | before   | answering. |        |
|     |     |     |     | Question:                                 |             | A sudden   | environmental   | change   |            | causes |
|     |     |     |     | arecessivealleletobecomecompletelylethal. |             |            |                 |          |            | How    |
|     |     |     |     | many                                      | generations | will       | it take         | for its  | frequency  | to     |
|     |     |     |     | decrease                                  | from        | 10−2 to    | 10−3?           |          |            |        |
A. 950
B. 850
|     |     |     |     | C.  | All other | answers are | incorrect |     |     |     |
| --- | --- | --- | --- | --- | --------- | ----------- | --------- | --- | --- | --- |
D. 900
E. 800
F. 920
Answer:
|     |     |     |     | ###                                | Response    | by o1   |           |                |           |     |
| --- | --- | --- | --- | ---------------------------------- | ----------- | ------- | --------- | -------------- | --------- | --- |
|     |     |     |     | To                                 | solve this, | use the | fact that | for a fully    | recessive |     |
|     |     |     |     | lethalallele(withinitialfrequencyq |             |         |           | ),itsfrequency |           |     |
0
|     |     |     |     | in the | next | generation | is given | by: |     |     |
| --- | --- | --- | --- | ------ | ---- | ---------- | -------- | --- | --- | --- |
q
|     |     |     |     |     |     | q   | = n |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
n+1
1+q n
1
|     |     |     |     | Define | R n | = . Then | from the | recurrence |     | above: |
| --- | --- | --- | --- | ------ | --- | -------- | -------- | ---------- | --- | ------ |
qn
1
|     |     |     |     |     |     | R   | =   |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|     |     |     |     |     |     | n+1 | q   |     |     |     |
n+1
1+q n
=
q
n
1
|     |     |     |     |     |     |     | = +1 |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- |
q n
|     |     |     |     |     |     |     | =R +1 |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- |
n
|     |     |     |     | Thus,R      | =R      | +n. SinceR  |           | = 1 andwewantq |     |     |
| --- | --- | --- | --- | ----------- | ------- | ----------- | --------- | -------------- | --- | --- |
|     |     |     |     |             | n       | 0           | 0         | q0             |     | n   |
|     |     |     |     | to decrease |         | from 0.01   | to 0.001: |                |     |     |
|     |     |     |     | •           | R =     | 1 =100.     |           |                |     |     |
|     |     |     |     |             | 0       | 0.01        |           |                |     |     |
|     |     |     |     | •           | We need | 1 =1000,    | so R      | =1000.         |     |     |
|     |     |     |     |             |         | qn          |           | n              |     |     |
|     |     |     |     | •           | Hence   | 1000=100+n, | giving    | n=900.         |     |     |
|     |     |     |     | ANSWER:     |         | D           |           |                |     |     |
A.2.CoTPrompt
Here,wepresenttheformatofourCoTpromptinfollowing
box. Aswecansee,ourCoTpromptmainlyuses“Think
stepbystep”,whichshowsthatevenasimplepromptstill
12

R-Bench
hasapositiveeffectonmostchatmodels.
SystemPromptforR-Bench
Answer the following single-choice question. The
lastlineofyourresponseshouldbeofthefollowing
format: ’ANSWER: $LETTER’ (without quotes)
where LETTER is one of the Options (e.g. one of
ABCDEF). Think step by step before answering.
Question: {Question Input}
A. {OptionA}
B. {OptionB}
C. {OptionC}
D. {OptionD}
E. {OptionE}
F. {OptionF}
Answer:
### Example:
Answer the following single choice question. The
lastlineofyourresponseshouldbeofthefollowing
format: ‘ANSWER: $LETTER’ (without quotes)
where LETTER is one of Options(e.g. one of
ABCDEF). Think step by step before answering.
Question: ConsideraCMOSinverterdrivingawire
of length L. In the initial design, the on-resistance
oftheinverterisequaltothetotalresistanceofthe
wire, the source-drain capacitance of the inverter
is equal to the total capacitance of the wire, and
the total delay of the inverter and wire is tp. Now,
the devices are scaled down using Constant Field
Scaling, while the wire is ideally scaled down.
Assumingthewirecanbemodeledusingalumped
parameter model, answer the following questions
in a first-order approximation:
(1) Assuming the wire is a local wire, and the
scaling factors for both process and supply voltage
are 2, express the total delay after scaling in terms
of tp.
(2) Now assume the wire is global, and the length
of the wire increases inversely with the process
scaling, with scaling factors for both process and
supply voltage being 2, express the total delay
after scaling in terms of tp.
A. (1) 1/3tp (2) 35/6tp
B. (1) 1/2tp (2) 38/6tp
C. All other answers are incorrect
D. (1) 3/4tp (2) 36/6tp
E. (1) 5/6tp (2) 39/6tp
F. (1) 2/3tp (2) 37/6tp
Answer:
A.3.Specificsubjectdistribution
WepresentthespecificsubjectdistributionsofR-Bench-T
andR-Bench-MinTable8andTable8,respectively. Itcan
beobservedthatR-Benchhasabroadcoverage,makingit
difficulttoimproveperformanceonR-Benchbyoverfitting
tospecificsubjects.
13

R-Bench
DistributionofCoursesbyDisciplineinR-Bench-T
Table8:
| Discipline          | SpecificSubject                 | Count |
| ------------------- | ------------------------------- | ----- |
|                     | FluidMechanics                  | 33    |
| CivilEngineering    | StructuralMechanics             | 2     |
|                     | Surveying                       | 1     |
|                     | PrinciplesofProcessTransport    | 3     |
| ChemicalEngineering | PrinciplesofChemicalEngineering | 15    |
|                     | AnalyticalChemistry             | 7     |
|                     | AdvancedMathematicalEconomics   | 1     |
|                     | Econometrics                    | 3     |
|                     | IntermediateFinancialTheory     | 1     |
| Economics           | FinancialEngineering            | 1     |
|                     | GameTheoryandMechanismDesign    | 5     |
|                     | IntermediateMicroeconomics      | 2     |
|                     | PrinciplesofAccounting          | 2     |
|                     | TimeSeriesAnalysis              | 8     |
|                     | SoilScience                     | 1     |
|                     | Genetics                        | 16    |
Biology
|         | Physiology                          | 1   |
| ------- | ----------------------------------- | --- |
|         | Biochemistry                        | 15  |
|         | Heredity                            | 8   |
|         | MathematicalMethodsinPhysics        | 10  |
|         | Electromagnetics                    | 11  |
|         | Optics                              | 23  |
|         | QuantumMechanics                    | 8   |
| Physics | AnalyticalMechanics                 | 6   |
|         | Electrodynamics                     | 5   |
|         | ThermodynamicsandStatisticalPhysics | 6   |
|         | GeneralRelativity                   | 2   |
|         | BasicPhysics                        | 29  |
|         | GroupTheory                         | 3   |
|         | MechanicsofMaterials                | 1   |
|         | StructuralMechanicsofAircraft       | 3   |
|         | TheoreticalMechanics                | 2   |
Aerospace
|     | FluidMechanicsandAerodynamics             | 15  |
| --- | ----------------------------------------- | --- |
|     | OptimalControl                            | 29  |
|     | PropulsionPrinciplesandThermalFluidBasics | 9   |
|     | DigitalLarge-ScaleIntegratedCircuits      | 20  |
Microelectronics
|            | AnalogCircuits                  | 2   |
| ---------- | ------------------------------- | --- |
|            | SignalsandSystems               | 20  |
| Automation | OperationsResearch              | 15  |
|            | AutomaticControlTheory          | 17  |
|            | CommunicationandNetwork         | 15  |
|            | PrinciplesofAnalogCircuits      | 4   |
|            | ElectromagneticFieldsandWaves   | 8   |
|            | FundamentalsofSolidStatePhysics | 12  |
ElectronicEngineering
|     | DigitalSignalProcessing | 8   |
| --- | ----------------------- | --- |
|     | StochasticProcesses     | 3   |
Continuedonnextpage
14

R-Bench
Table8–Continuedfrompreviouspage
| Discipline | SpecificSubject                                            | Count |
| ---------- | ---------------------------------------------------------- | ----- |
|            | SolidStatePhysics                                          | 4     |
|            | AppliedStochasticProcesses                                 | 26    |
|            | FluidMechanics                                             | 11    |
|            | PrinciplesandInterfaceTechnologyofSingle-ChipMicrocomputer | 4     |
|            | MechanicalDesign                                           | 1     |
|            | TheoryofMachines                                           | 4     |
|            | ElectromechanicalTransmissionandControl                    | 4     |
MechanicalEngineering
|                 | ElectricalandelectronicTechnology    | 2   |
| --------------- | ------------------------------------ | --- |
|                 | MechanicalVibration                  | 3   |
|                 | HydraulicandPneumaticTransmission    | 1   |
|                 | EngineeringThermodynamics            | 8   |
|                 | MechanicsofMaterials                 | 9   |
|                 | TheoreticalMechanics                 | 1   |
|                 | DataStructure                        | 24  |
|                 | CombinatorialMathematics             | 3   |
|                 | NumericalAnalysis                    | 5   |
|                 | Cryptography                         | 17  |
| ComputerScience | Automata                             | 8   |
|                 | PrinciplesofComputerOrganization     | 2   |
|                 | CompilationPrinciples                | 5   |
|                 | ComputerNetwork                      | 11  |
|                 | OperatingSystem                      | 11  |
|                 | ComputerArchitecture                 | 3   |
|                 | InorganicChemistry                   | 50  |
|                 | ChemicalThermodynamics               | 48  |
|                 | ChemicalReactionKinetics             | 20  |
|                 | IntroductiontoComputationalChemistry | 11  |
Chemistry
|     | QuantumChemistry             | 5   |
| --- | ---------------------------- | --- |
|     | PhysicalChemistry            | 22  |
|     | OrganicChemistry             | 5   |
|     | ComplexAnalysis              | 26  |
|     | AnalyticGeometry             | 26  |
|     | AdvancedCalculus             | 9   |
|     | NumberTheory                 | 22  |
|     | MatrixAnalysis               | 36  |
|     | PartialDifferentialEquations | 29  |
Mathematics
|     | MathematicalAnalysis                | 9   |
| --- | ----------------------------------- | --- |
|     | StochasticDifferentialEquations     | 8   |
|     | FunctionalAnalysis                  | 14  |
|     | OrdinaryDifferentialEquations       | 3   |
|     | DifferentialGeometry                | 3   |
|     | Topology                            | 3   |
|     | ThermodynamicsandStatisticalPhysics | 5   |
PhysicsEngineering
|     | NuclearRadiationPhysicsandDetection | 20  |
| --- | ----------------------------------- | --- |
|     | QuantumandStatistics                | 14  |
|     | PhysicalPropertiesofMaterials       | 1   |
Materials
|     | FundamentalsofMaterialsScience | 23  |
| --- | ------------------------------ | --- |
Continuedonnextpage
15

R-Bench
Table8–Continuedfrompreviouspage
| Discipline         | SpecificSubject                      | Count |
| ------------------ | ------------------------------------ | ----- |
|                    | MaterialsAnalysisandCharacterization | 3     |
|                    | FiniteElementAnalysisBasics          | 1     |
|                    | PrinciplesofAutomotivePowerSystem    | 11    |
|                    | AutomotiveElectronicsandControl      | 1     |
| VehicleEngineering | FundamentalsofControlEngineering     | 3     |
|                    | TheoryofAutomobile                   | 3     |
|                    | AutomobileConstruction               | 1     |
|                    | DiscreteMathematics                  | 12    |
|                    | ProbabilityTheory                    | 42    |
|                    | IntroductiontoBayesianStatistics     | 6     |
Statistics
|             | ReliabilityDataandSurvivalAnalysis                        | 1   |
| ----------- | --------------------------------------------------------- | --- |
|             | StatisticalInference                                      | 2   |
|             | PrinciplesofEnvironmentalEngineeringScienceandEngineering | 5   |
| Environment | WaterTreatmentEngineering                                 | 12  |
|             | EnvironmentalChemistry                                    | 1   |
16

R-Bench
DistributionofCoursesbyDisciplineinR-Bench-M
Table9:
| Discipline | SpecificSubject               | Count |
| ---------- | ----------------------------- | ----- |
|            | MaterialsMechanics            | 26    |
|            | FluidMechanicsandAerodynamics | 2     |
|            | TheoreticalMechanics          | 16    |
Aerospace
|     | AircraftStructuralMechanics          | 2   |
| --- | ------------------------------------ | --- |
|     | PropulsionPrinciplesandThermalFluids | 2   |
|     | OptimalControl                       | 2   |
|     | DigitalVLSI                          | 11  |
|     | AnalogCircuits                       | 19  |
IntegratedCircuits
|                     | DigitalElectronicsFundamentals | 8   |
| ------------------- | ------------------------------ | --- |
|                     | AnalogElectronicsFundamentals  | 6   |
|                     | TransportProcessPrinciples     | 2   |
| ChemicalEngineering | ChemicalPrinciples             | 11  |
|                     | PhysicalChemistry              | 12  |
|                     | ChemicalThermodynamics         | 1   |
|                     | ChemicalReactionKinetics       | 10  |
| Chemistry           | InorganicChemistry             | 1   |
|                     | OrganicChemistry               | 17  |
|                     | PhysicalChemistry              | 2   |
|                     | DataStructures                 | 4   |
|                     | Combinatorics                  | 1   |
|                     | DiscreteMathematics            | 12  |
|                     | TheoryofAutomata               | 7   |
| ComputerScience     | OperatingSystems               | 6   |
|                     | Compilers                      | 4   |
|                     | ComputerArchitecture           | 3   |
|                     | Cryptography                   | 1   |
|                     | ComputerNetworks               | 1   |
|                     | AnalyticalMechanics            | 25  |
|                     | Optics                         | 6   |
| Physics             | Electrodynamics                | 9   |
|                     | Electromagnetism               | 10  |
|                     | BasicPhysics                   | 10  |
|                     | AnalogCircuitPrinciples        | 17  |
|                     | SignalsandSystems              | 3   |
|                     | DigitalSignalProcessing        | 1   |
ElectricalEngineering
|             | CommunicationandNetworks      | 4   |
| ----------- | ----------------------------- | --- |
|             | ElectromagneticFieldsandWaves | 1   |
|             | SolidStatePhysics             | 1   |
|             | ComplexAnalysis               | 8   |
|             | AnalyticGeometry              | 2   |
|             | ProbabilityTheory             | 5   |
|             | StochasticProcesses           | 3   |
| Mathematics | Analysis                      | 1   |
|             | ProbabilityandStatistics      | 1   |
|             | MathematicalAnalysis          | 5   |
|             | Statistics                    | 6   |
Continuedonnextpage
17

R-Bench
Table9–Continuedfrompreviouspage
| Discipline | SpecificSubject                              | Count |
| ---------- | -------------------------------------------- | ----- |
|            | Topology                                     | 1     |
|            | WaterTreatmentEngineering                    | 4     |
|            | EnvironmentalScienceandEngineeringPrinciples | 9     |
|            | EnvironmentalMonitoring                      | 6     |
EnvironmentalEngineering
|     | WaterPollutionControlProject   | 4   |
| --- | ------------------------------ | --- |
|     | EnvironmentalChemistry         | 1   |
|     | SolidWasteTreatmentandDisposal | 2   |
|     | Genetics                       | 13  |
Biology
|     | Biochemistry                   | 4   |
| --- | ------------------------------ | --- |
|     | MaterialsMechanics             | 37  |
|     | QuantumandStatisticalMechanics | 5   |
MaterialScience
|           | MaterialAnalysisandCharacterization     | 1   |
| --------- | --------------------------------------- | --- |
|           | BasicMaterialScience                    | 18  |
|           | OperationsResearch                      | 2   |
|           | PrinciplesofAccounting                  | 3   |
| Economics | FinancialEngineering                    | 2   |
|           | IntermediateFinancialTheories           | 3   |
|           | GameTheoryandMechanismDesign            | 8   |
|           | ElectricalandElectronicsTechnology      | 5   |
|           | MechanicalDesign                        | 24  |
|           | ElectromechanicalTransmissionandControl | 1   |
|           | HydraulicandPneumaticTransmission       | 9   |
MechanicalEngineering
|                    | MechanicalVibrations            | 7   |
| ------------------ | ------------------------------- | --- |
|                    | FluidMechanics                  | 7   |
|                    | PrinciplesofMechanics           | 1   |
|                    | TheoreticalMechanics            | 11  |
| CivilEngineering   | StructuralMechanics             | 33  |
| EngineeringPhysics | EngineeringMechanics            | 23  |
| Automation         | AutomaticControlTheory          | 25  |
|                    | FluidMechanics                  | 34  |
|                    | EngineeringControlBasics        | 8   |
|                    | FiniteElementAnalysisBasics     | 5   |
| VehicleEngineering | AutomotiveElectronicsandControl | 5   |
|                    | AutomobileConstruction          | 5   |
|                    | AdvancedHeatTransfer            | 9   |
|                    | AutomotivePowerSystemPrinciples | 2   |
| Architecture       | StructuralEngineering           | 21  |
18