Inference-Time Scaling

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14 papers in the last four weeks, up 180% on the four weeks before. 0.1% of all new papers.

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VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
Jun 26, 2026Kijung Jeon, Thuy-Duong Vuong, Molei TaoConstrained Generative Modeling Masked Diffusion Models
Georgia Tech · UCSD
Active Inference as the Test-Time Scaling Law for Physical AI Agents
Jun 22, 2026Omar Hashash, Christo Kurisummoottil Thomas, Walid Saad +3Active Inference Inference-Time Scaling
Bradley Department of Electrical and Computer Engineering and the Institute for Advanced Computing, Virginia Tech, Alexandria, VA, USA · Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA · Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates +4
How Inference Compute Shapes Frontier LLM Evaluation
Jun 16, 2026Jessica McFadyen, Ole Jorgensen, Harry Coppock +2LLM Evaluation Language Model Generation Evaluation
AI Security Institute, London, UK · University of Oxford, Oxford, UK · Harvard University, Cambridge, MA, USA
Entropy-Gated Latent Recursion
Jun 15, 2026Soham Bhattacharjee, Dushyant Singh Chauhan, Salem Lahlou +2LLM Inference Inference-Time Scaling
Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Adaptive Inference-Time Scaling via Early-Step Latent Verification for Image Editing
Jun 13, 2026Yue Yu, Yang Jiao, Jiayu Wang +2Inference-Time Scaling Diffusion-Based Image Editing
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China · Microsoft Research Asia, Beijing 100080, China · Institute of Trustworthy Embodied AI, Fudan University, Shanghai 200433, China
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
Jun 11, 2026Jiacheng Chen, Xinyu Zhang, Shunkai Zhang +20Automated Theorem Proving Inference-Time Scaling
MiniMax · The Chinese University of Hong Kong · Fudan University +2
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jun 10, 2026Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7Test-Time Scaling for VLMs Inference-Time Scaling
Stanford University · University of Waterloo · NVIDIA
Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation
Jun 9, 2026Yupu Hao, Zhuoran Jin, Huanxuan Liao +2Knowledge Augmentation for Language Models Inference-Time Scaling
1The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
Jun 7, 2026Giorgio Giannone, Mustafa Eyceoz, Shabana Baig +5Particle Filtering Inference-Time Scaling
AI Innovation, Red Hat · DeCoDE Lab, MIT · Core AI, IBM
Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling
Jun 7, 2026Hong Guo, Nianhui Guo, Christoph Meinel +1Markov Chain Monte Carlo Inference-Time Scaling
Hasso Plattner Institut · German University of Digital Science
Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers
Jun 3, 2026Yacouba Kaloga, Shashi Kumar, Shakeel A. Sheikh +3Inference-Time Scaling Test-Time Scaling
Idiap Research Institute, Switzerland · EPFL, Switzerland · BUT, Czech Republic
Inference-Time Scaling for Joint Audio-Video Generation
Jun 2, 2026Jaemin Jung, Kyeongha Rho, Inkyu Shin +1Audio-Video Generation Inference-Time Scaling
Korea Advanced Institute of Science and Technology · Luma AI
The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs
Jun 2, 2026Xu Wan, Speed Zhu, Jianwei Cai +4Cost-Aware Inference Inference-Time Scaling
1Zhejiang University · 2Tecent HY Team · 3Peking University
Predicting Inference-Time Scaling Gains from Labeled Validation-Set Output Statistics
Jun 2, 2026Luyang Zhang, Jingyan LiLLM Evaluation Inference-Time Scaling
Carnegie Mellon University · Johns Hopkins University
Score $\times$ Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
May 30, 2026Yun-Chen Cheng, Che-Yu Lin, Cheng-Lin YangLanguage Model Decoding LLM Hallucination Mitigation
CyCraft AI Lab, Taiwan
UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling
May 29, 2026Kaiyu Huang, Xingyu Wang, Mingze Kong +6Cost-Aware Inference Inference-Time Scaling
School of Computer Science and Technology, Tongji University · Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen · School of Data Science, The Chinese University of Hong Kong, Shenzhen +2
Scaling Laws for Agent Harnesses via Effective Feedback Compute
May 28, 2026Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2Inference-Time Scaling Agent Harness Optimization
Harbin Institute of Technology
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
May 27, 2026Kunhao Zheng, Pierre Chambon, Juliette Decugis +4Multi-Objective Reinforcement Learning RL for Code Generation
Meta Superintelligence Labs - FAIR · Université Paris-Dauphine PSL
Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
May 24, 2026Dao Tran, Duc Anh Le, Ngoc Luu +3Inference-Time Scaling Efficient Language Model Reasoning
Qualcomm AI Research†
RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
May 21, 2026Minseok Jung, Abhas Ricky, Muhammad Rameez ChatniExecution-Guided Code Generation Inference-Time Scaling
Cloudera 6220 America Center Dr. San Jose, CA 95002
Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
May 19, 2026Taegu Kang, Jaesik Yoon, Sungjin AhnInference-Time Scaling Diffusion Models
KAIST · KAIST & SAP · KAIST & NYU
Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
May 18, 2026Chenyang Wang, Weizhong Wang, Yinuo Ren +2Diffusion Model Guidance Importance Sampling
School of Mathematical Sciences, Peking University, Beijing, China · School of Mathematical Sciences, Fudan University, Shanghai, China · Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States +2
Drift Flow Matching
May 17, 2026Chenrui Ma, Xi Xiao, Lin Zhao +3Flow Matching Inference-Time Scaling
University of California, Irvine · University of Virginia · University of Alabama at Birmingham +1
Effort as Ceiling, Not Dial: Reasoning Budget Does Not Modulate Cognitive Cost Alignment Between Humans and Large Reasoning Models
May 16, 2026Yueqing Hu, Tianhong WangComputational Cognitive Modeling Inference-Time Scaling
Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China · School of Philosophy, Anhui University, Hefei, China
Dual-Dimensional Consistency: Balancing Budget and Quality in Adaptive Inference-Time Scaling
May 14, 2026Rongman Xu, Yifei Li, Tianzhe Zhao +3Inference-Time Scaling LLM Inference Acceleration
Xi’an Jiaotong University
Inference-Time Amplification of Weak Reasoning Models
May 13, 2026Varun Sunkaraneni, Pierfrancesco Beneventano, Riccardo Neumarker +2Inference-Time Scaling LLM Reasoning
MIT
TabPFN-3: Technical Report
May 13, 2026Léo Grinsztajn, Klemens Flöge, Oscar Key +38Tabular Foundation Models Efficient Neural Network Inference
Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
May 10, 2026Kewei Chen, Yayu Long, Mingsheng ShangRobotic Control Inference-Time Scaling
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences · Chongqing School, University of Chinese Academy of Sciences
Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching
May 9, 2026Shan Jiang, Zijian Yi, Chenguang ZhuLLM Inference Efficiency Test-Time Optimization
The University of Texas at Austin
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection
May 8, 2026James Petullo, Sonny George, Dylan Cashman +1LLM Inference Efficiency Inference-Time Scaling
Computer Science Department, Brandeis University

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VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

Kijung Jeon, Thuy-Duong Vuong, Molei Tao

Georgia Tech · UCSD

Constrained Generative Modeling Masked Diffusion Models Controllable Text Generation Diffusion Sampling Inference-Time Scaling

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Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider Masked Diffusion Model (MDM) and introduce MDM-VGB, a discrete diffusion sampler that augments unmasking generation with theoretically principled reward-guided remasking. Inspired by the recent success of the classical Jerrum-Sinclair backtracking Markov chain in reward-tilted generation, MDM-VGB extends the backtracking random walk from a fixed prefix tree to a masked-state graph, allowing tokens to be unmasked and remasked at arbitrary positions. The resulting sampler favors unmasking and remasking moves that lead to higher-value partial configurations, enabling both effective high-reward generation and efficient repair of low-reward samples. We prove that MDM-VGB is robust to process-verifier noise and achieves quadratic complexity, while popular test-time heuristics such as best-of-

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can incur exponential complexity due to error accumulation. Our theoretical findings are corroborated by strong empirical performance, particularly on popular constraint-satisfaction and scientific benchmarks such as Sudoku and QM9.

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Latest papers 77