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A Survey of Test-Time Compute

From Intuitive Inference to Deliberate Reasoning

Bibliographic Data

ID21580989
AuthorsYixin Ji (Soochow University. [email protected]), Juntao Li (0000-0001-5377-7866, Soochow University. [email protected]), Yang Xiang (0000-0003-3016-0323, Soochow University), Hai Ye (National University of Singapore), Kaixin Wu (Ant Group (China)), Kai Yao (0000-0002-1841-1508, Ant Group (China)), YAO Kai, Jia Xu (0000-0003-1238-1317, Ant Group (China)), Linjian Mo (Ant Group (China)), Min Zhang (0000-0003-4273-6836, Soochow University. [email protected])
Year2026
Pages1-51
Publication date2026-05-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueComputational Linguistics (JOURNAL)
Journal identifiersISSN: 0891-2017 • E-ISSN: 1530-9312
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/coli.a.629
OpenAlexW7162681532
LanguageEN

The remarkable performance of the o1 model in complex reasoning demonstrates that test-time compute scaling can further unlock the model’s potential, enabling powerful System-2 thinking. However, there is still a lack of comprehensive surveys for test-time compute scaling. We trace the concept of test-time compute back to System-1 models. In System-1 models, test-time compute addresses distribution shifts and improves robustness and generalization through parameter updating, input modification, representation editing, and output calibration. In System-2 models, it enhances the model’s reasoning ability to solve complex problems through repeated sampling, self-correction, and tree search. We organize this survey according to the trend of System-1 to System-2 thinking, highlighting the key role of test-time compute in the transition from System-1 models to weak System-2 models, and then to strong System-2 models.We also point out advanced topics and future directions

Generalization · Inference · Scaling · Constraint Satisfaction and Optimization · Formal Methods in Verification · Topic Modeling

Citation velocityhistorical
Highly citedNo

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