[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"blog":3},{"title":4,"desc":5,"bannerImg":6,"date":7,"orgImgLinks":8,"bannerLinks":9,"blogCategory":10,"category":11,"weight":12,"externalUrl":13,"links":14,"description":5,"content":15,"tag1":809,"tag2":810,"logosByUrl":812,"resLinks":814},"Scaling Test-Time Compute: How CriticLean Anticipated DeepSeekMath","DeepSeekMath-V2's success confirms 2077AI's vision. Explore how CriticLean pioneered self-verification and the shift from outcome rewards to System 2 reasoning.","https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002FBanner_blog\u002Fbanner_criticleantrend.png","2025-12-15","[]","{}","Research","undefined",0,"","{\"homepage\":\"\",\"github\":\"\",\"huggingface\":\"\",\"x\":\"\",\"discord\":\"\",\"arxiv\":\"\"}",{"data":16,"body":19,"toc":798},{"title":17,"description":18},"Prophetic Validation: DeepSeekMath-V2 Confirms the \"Critic-Centric\" Pioneered by 2077AI's CriticLean","While the industry chased generation, 2077AI planted the flag on the frontier of \"Verification.\" Here is how our roadmap aligns with the latest SOTA breakthroughs.",{"type":20,"children":21},"root",[22,37,54,91,99,114,123,165,174,186,209,218,278,330,342,351,361,384,391,403,412,422,459,466,478,501,511,520,537,560,567,579,588,605,614,621,633,642,659,682,692,701,710,719,732,791],{"type":23,"tag":24,"props":25,"children":29},"element","h1",{"className":26,"id":28},[27],"heading__h1","prophetic-validation-deepseekmath-v2-confirms-the-critic-centric-pioneered-by-2077ais-criticlean",[30],{"type":23,"tag":31,"props":32,"children":34},"span",{"style":33},"white-space: pre-wrap;",[35],{"type":36,"value":17},"text",{"type":23,"tag":38,"props":39,"children":42},"p",{"className":40},[41],"doxhub-editor-paragraph",[43],{"type":23,"tag":44,"props":45,"children":46},"b",{},[47],{"type":23,"tag":48,"props":49,"children":52},"strong",{"className":50,"style":33},[51],"text__bold",[53],{"type":36,"value":18},{"type":23,"tag":38,"props":55,"children":57},{"className":56},[41],[58,63,72,77,86],{"type":23,"tag":31,"props":59,"children":60},{"style":33},[61],{"type":36,"value":62},"The AI research community is currently abuzz with the release of ",{"type":23,"tag":44,"props":64,"children":65},{},[66],{"type":23,"tag":48,"props":67,"children":69},{"className":68,"style":33},[51],[70],{"type":36,"value":71},"DeepSeekMath-V2",{"type":23,"tag":31,"props":73,"children":74},{"style":33},[75],{"type":36,"value":76},". By achieving gold-medal performance in IMO 2025 and near-perfect scores in Putnam 2024, DeepSeek has demonstrated a powerful truth: to achieve super-human reasoning, scaling test-time compute via ",{"type":23,"tag":44,"props":78,"children":79},{},[80],{"type":23,"tag":48,"props":81,"children":83},{"className":82,"style":33},[51],[84],{"type":36,"value":85},"Self-Verification",{"type":23,"tag":31,"props":87,"children":88},{"style":33},[89],{"type":36,"value":90}," is particularly important.",{"type":23,"tag":38,"props":92,"children":94},{"className":93},[41],[95],{"type":23,"tag":96,"props":97,"children":98},"br",{},[],{"type":23,"tag":100,"props":101,"children":102},"figure",{},[103,109],{"type":23,"tag":104,"props":105,"children":108},"img",{"src":106,"alt":107},"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002F20251215\u002F1.webp","DeepSeekMath-V2's Record-Breaking Scores",[],{"type":23,"tag":110,"props":111,"children":112},"figcaption",{},[113],{"type":36,"value":107},{"type":23,"tag":38,"props":115,"children":117},{"className":116},[41],[118],{"type":23,"tag":31,"props":119,"children":120},{"style":33},[121],{"type":36,"value":122},"As shown above, the model achieved an 83.3% score on IMO 2025 and an incredible 98.3% on Putnam 2024. These results serve as definitive proof that self-verification is the key unlock for solving competition-level mathematics.",{"type":23,"tag":38,"props":124,"children":126},{"className":125},[41],[127,132,141,146,160],{"type":23,"tag":31,"props":128,"children":129},{"style":33},[130],{"type":36,"value":131},"For us at ",{"type":23,"tag":44,"props":133,"children":134},{},[135],{"type":23,"tag":48,"props":136,"children":138},{"className":137,"style":33},[51],[139],{"type":36,"value":140},"2077AI",{"type":23,"tag":31,"props":142,"children":143},{"style":33},[144],{"type":36,"value":145},", this moment is one of profound validation. Back in July 2025, when we released our paper ",{"type":23,"tag":147,"props":148,"children":149},"i",{},[150],{"type":23,"tag":44,"props":151,"children":152},{},[153],{"type":23,"tag":48,"props":154,"children":157},{"className":155,"style":33},[51,156],"text__italic",[158],{"type":36,"value":159},"CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization",{"type":23,"tag":31,"props":161,"children":162},{"style":33},[163],{"type":36,"value":164},", we explicitly identified the \"Critic\" phase as the missing link in automated reasoning. DeepSeekMath-V2’s architecture, released four months later, follows a strikingly similar philosophical trajectory to the one we pioneered.",{"type":23,"tag":38,"props":166,"children":168},{"className":167},[41],[169],{"type":23,"tag":31,"props":170,"children":171},{"style":33},[172],{"type":36,"value":173},"Today, we unpack this convergent evolution and explain why CriticLean remains a critical piece of infrastructure for the future of formal reasoning.",{"type":23,"tag":175,"props":176,"children":180},"h2",{"className":177,"id":179},[178],"heading__h2","the-shared-insight-the-outcome-reward-fallacy",[181],{"type":23,"tag":31,"props":182,"children":183},{"style":33},[184],{"type":36,"value":185},"The Shared Insight: The 'Outcome Reward' Fallacy",{"type":23,"tag":38,"props":187,"children":189},{"className":188},[41],[190,195,204],{"type":23,"tag":31,"props":191,"children":192},{"style":33},[193],{"type":36,"value":194},"For a long time, the dominant paradigm in LLM reasoning (RLHF) relied heavily on ",{"type":23,"tag":44,"props":196,"children":197},{},[198],{"type":23,"tag":48,"props":199,"children":201},{"className":200,"style":33},[51],[202],{"type":36,"value":203},"Outcome-Based Rewards",{"type":23,"tag":31,"props":205,"children":206},{"style":33},[207],{"type":36,"value":208},": did the model get the final answer right?",{"type":23,"tag":38,"props":210,"children":212},{"className":211},[41],[213],{"type":23,"tag":31,"props":214,"children":215},{"style":33},[216],{"type":36,"value":217},"However, as we argued in the CriticLean paper, this approach is fundamentally flawed for high-level mathematics.",{"type":23,"tag":219,"props":220,"children":223},"ul",{"className":221},[222],"doxhub-editor-ul",[224,245],{"type":23,"tag":225,"props":226,"children":230},"li",{"value":227,"className":228},"1",[229],"doxhub-editor-list-item",[231,240],{"type":23,"tag":44,"props":232,"children":233},{},[234],{"type":23,"tag":48,"props":235,"children":237},{"className":236,"style":33},[51],[238],{"type":36,"value":239},"💡 DeepSeekMath’s View:",{"type":23,"tag":31,"props":241,"children":242},{"style":33},[243],{"type":36,"value":244}," \"Correct answers don’t guarantee correct reasoning.\" A model can hallucinate its way to a correct number.",{"type":23,"tag":225,"props":246,"children":249},{"value":247,"className":248},"2",[229],[250,259,264,273],{"type":23,"tag":44,"props":251,"children":252},{},[253],{"type":23,"tag":48,"props":254,"children":256},{"className":255,"style":33},[51],[257],{"type":36,"value":258},"💡 CriticLean’s View:",{"type":23,"tag":31,"props":260,"children":261},{"style":33},[262],{"type":36,"value":263}," In formal theorem proving (Lean 4), auto-formalization 'requires not only syntactical accuracy but also a deep understanding of the problem’s semantics'.",{"type":23,"tag":44,"props":265,"children":266},{},[267],{"type":23,"tag":48,"props":268,"children":270},{"className":269,"style":33},[51],[271],{"type":36,"value":272}," ",{"type":23,"tag":31,"props":274,"children":275},{"style":33},[276],{"type":36,"value":277},"Compilation success does not equate to semantic correctness.",{"type":23,"tag":38,"props":279,"children":281},{"className":280},[41],[282,287,296,301,310,315,325],{"type":23,"tag":31,"props":283,"children":284},{"style":33},[285],{"type":36,"value":286},"Both labs reached the same conclusion independently: To advance from \"Solver\" to \"Reasoner,\" we must shift from ",{"type":23,"tag":44,"props":288,"children":289},{},[290],{"type":23,"tag":48,"props":291,"children":293},{"className":292,"style":33},[51],[294],{"type":36,"value":295},"Outcome Rewards",{"type":23,"tag":31,"props":297,"children":298},{"style":33},[299],{"type":36,"value":300}," to ",{"type":23,"tag":44,"props":302,"children":303},{},[304],{"type":23,"tag":48,"props":305,"children":307},{"className":306,"style":33},[51],[308],{"type":36,"value":309},"Process Verification",{"type":23,"tag":31,"props":311,"children":312},{"style":33},[313],{"type":36,"value":314},". We need models that don't just generate, but actively ",{"type":23,"tag":147,"props":316,"children":317},{},[318],{"type":23,"tag":319,"props":320,"children":322},"em",{"className":321,"style":33},[156],[323],{"type":36,"value":324},"critique",{"type":23,"tag":31,"props":326,"children":327},{"style":33},[328],{"type":36,"value":329},".",{"type":23,"tag":331,"props":332,"children":336},"h3",{"className":333,"id":335},[334],"heading__h3","divergent-paths-same-destination",[337],{"type":23,"tag":31,"props":338,"children":339},{"style":33},[340],{"type":36,"value":341},"Divergent Paths, Same Destination",{"type":23,"tag":38,"props":343,"children":345},{"className":344},[41],[346],{"type":23,"tag":31,"props":347,"children":348},{"style":33},[349],{"type":36,"value":350},"While the underlying logic is identical, 2077AI and DeepSeek applied this philosophy to two different, complementary domains.",{"type":23,"tag":331,"props":352,"children":355},{"className":353,"id":354},[334],"deepseek-the-informal-reasoning-verifier",[356],{"type":23,"tag":31,"props":357,"children":358},{"style":33},[359],{"type":36,"value":360},"DeepSeek: The Informal Reasoning Verifier",{"type":23,"tag":38,"props":362,"children":364},{"className":363},[41],[365,370,379],{"type":23,"tag":31,"props":366,"children":367},{"style":33},[368],{"type":36,"value":369},"DeepSeekMath-V2 focuses on ",{"type":23,"tag":44,"props":371,"children":372},{},[373],{"type":23,"tag":48,"props":374,"children":376},{"className":375,"style":33},[51],[377],{"type":36,"value":378},"Natural Language",{"type":23,"tag":31,"props":380,"children":381},{"style":33},[382],{"type":36,"value":383},". It trains a verifier to detect logical gaps in Natural-Language Theorem Proving. By scaling verification compute, it allows the model to self-correct before finalizing an answer.",{"type":23,"tag":38,"props":385,"children":387},{"className":386},[41],[388],{"type":23,"tag":96,"props":389,"children":390},{},[],{"type":23,"tag":100,"props":392,"children":393},{},[394,399],{"type":23,"tag":104,"props":395,"children":398},{"src":396,"alt":397},"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002F20251215\u002F2.webp","SOTA Performance on IMO-ProofBench",[],{"type":23,"tag":110,"props":400,"children":401},{},[402],{"type":36,"value":397},{"type":23,"tag":38,"props":404,"children":406},{"className":405},[41],[407],{"type":23,"tag":31,"props":408,"children":409},{"style":33},[410],{"type":36,"value":411},"The impact of this approach is evident in the chart above. Human evaluations on IMO-ProofBench show that DeepSeekMath-V2 (Heavy), driven by self-verification, significantly outperforms previous SOTA models like GPT-5 and Gemini in generating valid informal proofs.",{"type":23,"tag":331,"props":413,"children":416},{"className":414,"id":415},[334],"criticlean-the-formal-reasoning-judge",[417],{"type":23,"tag":31,"props":418,"children":419},{"style":33},[420],{"type":36,"value":421},"CriticLean: The Formal Reasoning Judge",{"type":23,"tag":38,"props":423,"children":425},{"className":424},[41],[426,431,440,445,454],{"type":23,"tag":31,"props":427,"children":428},{"style":33},[429],{"type":36,"value":430},"2077AI took on the \"Last Mile\" problem of mathematics: ",{"type":23,"tag":44,"props":432,"children":433},{},[434],{"type":23,"tag":48,"props":435,"children":437},{"className":436,"style":33},[51],[438],{"type":36,"value":439},"Formalization",{"type":23,"tag":31,"props":441,"children":442},{"style":33},[443],{"type":36,"value":444},". We focused on translating natural language into ",{"type":23,"tag":44,"props":446,"children":447},{},[448],{"type":23,"tag":48,"props":449,"children":451},{"className":450,"style":33},[51],[452],{"type":36,"value":453},"Lean 4",{"type":23,"tag":31,"props":455,"children":456},{"style":33},[457],{"type":36,"value":458},"—executable, machine-verifiable code. This is arguably a stricter challenge, as it requires bridging the gap between human intent and machine rigidity.",{"type":23,"tag":38,"props":460,"children":462},{"className":461},[41],[463],{"type":23,"tag":96,"props":464,"children":465},{},[],{"type":23,"tag":100,"props":467,"children":468},{},[469,474],{"type":23,"tag":104,"props":470,"children":473},{"src":471,"alt":472},"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002F20251215\u002F3.webp","The CriticLean Framework (Published in July 2025)",[],{"type":23,"tag":110,"props":475,"children":476},{},[477],{"type":36,"value":472},{"type":23,"tag":38,"props":479,"children":481},{"className":480},[41],[482,487,496],{"type":23,"tag":31,"props":483,"children":484},{"style":33},[485],{"type":36,"value":486},"In our framework, we elevated the critic from a passive observer to an active ",{"type":23,"tag":44,"props":488,"children":489},{},[490],{"type":23,"tag":48,"props":491,"children":493},{"className":492,"style":33},[51],[494],{"type":36,"value":495},"CriticLeanGPT",{"type":23,"tag":31,"props":497,"children":498},{"style":33},[499],{"type":36,"value":500},". As illustrated in the diagram, we created a closed feedback loop between the AutoFormalizer, the Lean Compiler, and our semantic CriticLeanGPT. This ensures that the generated code isn't just syntactically valid, but mathematically faithful to the original statement.",{"type":23,"tag":175,"props":502,"children":505},{"className":503,"id":504},[178],"core-contributions-of-criticlean-infrastructure-for-the-future",[506],{"type":23,"tag":31,"props":507,"children":508},{"style":33},[509],{"type":36,"value":510},"Core Contributions of CriticLean: Infrastructure for the Future",{"type":23,"tag":38,"props":512,"children":514},{"className":513},[41],[515],{"type":23,"tag":31,"props":516,"children":517},{"style":33},[518],{"type":36,"value":519},"As the industry pivots toward this \"Critic-Guided\" paradigm, the assets and methodologies introduced by 2077AI in July are more relevant than ever. We established three key pillars for this new era:",{"type":23,"tag":219,"props":521,"children":523},{"className":522},[222],[524],{"type":23,"tag":225,"props":525,"children":527},{"value":227,"className":526},[229],[528],{"type":23,"tag":44,"props":529,"children":530},{},[531],{"type":23,"tag":48,"props":532,"children":534},{"className":533,"style":33},[51],[535],{"type":36,"value":536},"CriticLeanBench: Quantifying the Critic",{"type":23,"tag":38,"props":538,"children":540},{"className":539},[41],[541,546,555],{"type":23,"tag":31,"props":542,"children":543},{"style":33},[544],{"type":36,"value":545},"While others measured generation rates, we built the first benchmark to measure ",{"type":23,"tag":44,"props":547,"children":548},{},[549],{"type":23,"tag":48,"props":550,"children":552},{"className":551,"style":33},[51],[553],{"type":36,"value":554},"discrimination capability",{"type":23,"tag":31,"props":556,"children":557},{"style":33},[558],{"type":36,"value":559},". If a model cannot distinguish a subtle semantic error from a correct proof, it cannot reason.",{"type":23,"tag":38,"props":561,"children":563},{"className":562},[41],[564],{"type":23,"tag":96,"props":565,"children":566},{},[],{"type":23,"tag":100,"props":568,"children":569},{},[570,575],{"type":23,"tag":104,"props":571,"children":574},{"src":572,"alt":573},"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002F20251215\u002F4.webp","CriticLeanBench Data Overview",[],{"type":23,"tag":110,"props":576,"children":577},{},[578],{"type":36,"value":573},{"type":23,"tag":38,"props":580,"children":582},{"className":581},[41],[583],{"type":23,"tag":31,"props":584,"children":585},{"style":33},[586],{"type":36,"value":587},"This dataset uses a rigorous pipeline, visualized above, which combines automatic filtering with manual expert review. This results in high-quality pairs of correct and incorrect formalizations, specifically designed to test a model's ability to critique semantic errors across various domains.",{"type":23,"tag":219,"props":589,"children":591},{"className":590},[222],[592],{"type":23,"tag":225,"props":593,"children":595},{"value":227,"className":594},[229],[596],{"type":23,"tag":44,"props":597,"children":598},{},[599],{"type":23,"tag":48,"props":600,"children":602},{"className":601,"style":33},[51],[603],{"type":36,"value":604},"Scaling Law in Criticism",{"type":23,"tag":38,"props":606,"children":608},{"className":607},[41],[609],{"type":23,"tag":31,"props":610,"children":611},{"style":33},[612],{"type":36,"value":613},"Crucially, our research proved that the ability to critique is not static. It is a learnable capability that improves with scale.",{"type":23,"tag":38,"props":615,"children":617},{"className":616},[41],[618],{"type":23,"tag":96,"props":619,"children":620},{},[],{"type":23,"tag":100,"props":622,"children":623},{},[624,629],{"type":23,"tag":104,"props":625,"children":628},{"src":626,"alt":627},"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002F2077ai\u002F20251215\u002F5.webp","Figure 5: Scaling Laws in Critique Capability",[],{"type":23,"tag":110,"props":630,"children":631},{},[632],{"type":36,"value":627},{"type":23,"tag":38,"props":634,"children":636},{"className":635},[41],[637],{"type":23,"tag":31,"props":638,"children":639},{"style":33},[640],{"type":36,"value":641},"Our experiments, plotted above, reveal that as model parameters increase (from 7B to 32B) and instruction tuning is applied, the model's scoring accuracy on the critique benchmark improves consistently. This mirrors the findings in generation models, confirming that \"Criticism\" follows scaling laws.",{"type":23,"tag":219,"props":643,"children":645},{"className":644},[222],[646],{"type":23,"tag":225,"props":647,"children":649},{"value":227,"className":648},[229],[650],{"type":23,"tag":44,"props":651,"children":652},{},[653],{"type":23,"tag":48,"props":654,"children":656},{"className":655,"style":33},[51],[657],{"type":36,"value":658},"FineLeanCorpus",{"type":23,"tag":38,"props":660,"children":662},{"className":661},[41],[663,668,677],{"type":23,"tag":31,"props":664,"children":665},{"style":33},[666],{"type":36,"value":667},"We proved that high-quality data is the bedrock of reasoning. By using our critic model to filter and curate over ",{"type":23,"tag":44,"props":669,"children":670},{},[671],{"type":23,"tag":48,"props":672,"children":674},{"className":673,"style":33},[51],[675],{"type":36,"value":676},"285,000",{"type":23,"tag":31,"props":678,"children":679},{"style":33},[680],{"type":36,"value":681}," problems, we created a dataset that emphasizes domain diversity and difficulty—resources that are now open to the community.",{"type":23,"tag":175,"props":683,"children":686},{"className":684,"id":685},[178],"defining-the-next-step",[687],{"type":23,"tag":31,"props":688,"children":689},{"style":33},[690],{"type":36,"value":691},"Defining the Next Step",{"type":23,"tag":38,"props":693,"children":695},{"className":694},[41],[696],{"type":23,"tag":31,"props":697,"children":698},{"style":33},[699],{"type":36,"value":700},"The success of DeepSeekMath-V2 marks a milestone for AI reasoning, signaling that System 2 thinking (slow, verified reasoning) has officially arrived.",{"type":23,"tag":38,"props":702,"children":704},{"className":703},[41],[705],{"type":23,"tag":31,"props":706,"children":707},{"style":33},[708],{"type":36,"value":709},"For 2077AI, this confirms that our strategic focus on Evaluation, Verification, and High-Fidelity Data is on the right track. We didn't just predict the trend; we built the infrastructure for it.",{"type":23,"tag":38,"props":711,"children":713},{"className":712},[41],[714],{"type":23,"tag":31,"props":715,"children":716},{"style":33},[717],{"type":36,"value":718},"As we continue to explore the frontiers of AGI, we invite researchers to build upon the CriticLean framework. The future of AI isn't just about writing answers, it's about knowing why they are true.",{"type":23,"tag":38,"props":720,"children":722},{"className":721},[41],[723],{"type":23,"tag":44,"props":724,"children":725},{},[726],{"type":23,"tag":48,"props":727,"children":729},{"className":728,"style":33},[51],[730],{"type":36,"value":731},"🔗 Explore the Work:",{"type":23,"tag":219,"props":733,"children":735},{"className":734},[222],[736,764],{"type":23,"tag":225,"props":737,"children":739},{"value":227,"className":738},[229],[740,749,753],{"type":23,"tag":44,"props":741,"children":742},{},[743],{"type":23,"tag":48,"props":744,"children":746},{"className":745,"style":33},[51],[747],{"type":36,"value":748},"CriticLean Paper:",{"type":23,"tag":31,"props":750,"children":751},{"style":33},[752],{"type":36,"value":272},{"type":23,"tag":754,"props":755,"children":759},"a",{"href":756,"className":757},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.06181",[758],"text__link",[760],{"type":23,"tag":31,"props":761,"children":762},{"style":33},[763],{"type":36,"value":159},{"type":23,"tag":225,"props":765,"children":767},{"value":247,"className":766},[229],[768,777,781],{"type":23,"tag":44,"props":769,"children":770},{},[771],{"type":23,"tag":48,"props":772,"children":774},{"className":773,"style":33},[51],[775],{"type":36,"value":776},"DeepSeekMath-V2 Paper:",{"type":23,"tag":31,"props":778,"children":779},{"style":33},[780],{"type":36,"value":272},{"type":23,"tag":754,"props":782,"children":785},{"href":783,"className":784},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.22570",[758],[786],{"type":23,"tag":31,"props":787,"children":788},{"style":33},[789],{"type":36,"value":790},"DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning",{"type":23,"tag":38,"props":792,"children":794},{"className":793},[41],[795],{"type":23,"tag":96,"props":796,"children":797},{},[],{"title":13,"searchDepth":799,"depth":799,"links":800},2,[801,807,808],{"id":179,"depth":799,"text":185,"children":802},[803,805,806],{"id":335,"depth":804,"text":341},3,{"id":354,"depth":804,"text":360},{"id":415,"depth":804,"text":421},{"id":504,"depth":799,"text":510},{"id":685,"depth":799,"text":691},"model",[811],"llm",[813],"https:\u002F\u002Fdoxhub.s3.us-east-1.amazonaws.com\u002Fdocs-hub\u002F2077ai\u002Forg-logo\u002Fbytedance-seedream.png",{"homepage":815,"arxiv":756,"github":816,"huggingface":817},"https:\u002F\u002Fwww.2077ai.com\u002Fdatasets\u002Fdataset-criticlean","https:\u002F\u002Fgithub.com\u002Fmultimodal-art-projection\u002FCriticLean","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fm-a-p\u002FCriticLeanInstruct"]