Inference-Time Guidance

Follow this topic Ask about this topic

Momentum

8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 65

All topics

Mask, Sample, Revise: A Revisable CTMC Inference Stack for Guided Discrete Flow Matching Text-to-Speech
Jun 12, 2026Alef Iury Siqueira Ferreira, Lucas Rafael Stefanel Gris, Luiz Fernando de Araújo Vidal +4Flow Matching TTS Synthesis
Federal University of Goi´as, Goiˆania, Brazil · Federal University of Mato Grosso, Cuiab´a, Brazil · Elsa Speak, S˜ao Paulo, Brazil
Towards More General Control of Diffusion Models Using Jeffrey Guidance
Jun 11, 2026Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2Diffusion Model Guidance Diffusion Guidance
Inria, CNRS, I3S, Maasai Université Côte d’Azur, France · Technical University of Denmark, Denmark · Inria, CNRS, LJAD, Maasai Université Côte d’Azur, France
Gradient-Guided Reward Optimization for Inference-time Alignment
Jun 8, 2026Hankun Lin, Ruqi ZhangReward Hacking LLM Alignment
Department of Computer Science, Purdue University
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
ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
Jun 3, 2026Ruiqing Sun, Sen Yang, Dawei Feng +3Diffusion Guidance Multi-Objective Optimization
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China · Bioinformatics Center of AMMS, Beijing 100080, China
Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
May 30, 2026Jiakang Li, Guanyu Zhu, Can Jin +8Reward Modeling Language Model Steering
Rutgers University · South China Agricultural University · Columbia University +4
TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation
May 25, 2026Sizhe Zhao, Shengping Zhang, Shuo Yang +3Robotic Manipulation Inference-Time Guidance
Harbin Institute of Technology, China · Harbin Institute of Technology (Weihai) Qingdao Research Institute, China · Iray Technology co., Ltd., Shandong, China +1
Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory
May 25, 2026Xifeng Zhang, Jin ZhaoFlow Matching PDE Solving
School of Mathematical Science Capital Normal University Beijing, 100048, China · Academy for Multidisciplinary Studies Capital Normal University2026 Beijing, 100048, China
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
May 24, 2026Andrew Corbett, Archit Sood, Anna Tzatzopoulou +2Inference-Time Search Structured Reasoning
digiLab, UK · University of Bristol, UK
Smoother Action Chunking Flow Policy via Prior-Corrected Orthogonal Trust-Region Guidance
May 23, 2026Kai Fang, Hailong Pei, Xuemin ChiRobot Policy Learning Action Chunking
South China University of Technology · Guangzhou, China · Zhejiang University +1
Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
May 22, 2026Jaihoon Kim, Taehoon Yoon, Prin Phunyaphibarn +3Markov Chain Monte Carlo Amortized Inference
1KAIST · University of Michigan · 3NVIDIA
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
May 21, 2026Lingyu Jiang, Zirui Li, Shuo Xing +6Efficient Language Model Reasoning Efficient Language Model Inference
1Tohoku University · 2Texas A&M University · 3Worcester Polytechnic Institute
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
May 21, 2026Ziyuan Zhu, Keyu Hu, Zhifei Chen +10Physics-Informed Generative Modeling Physics-Informed ML
School of Advanced Manufacturing Engineering, Nanjing University, Suzhou, 215163, China. · National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, China. · Suzhou Acoustics Industry Technology Research Institute Co., Ltd., Suzhou, 215513, China. +8
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
May 20, 2026Xuehui Yu, Fucheng Cai, Meiyi Wang +2Constrained Generative Modeling Diffusion Model Guidance
Smart Systems Institute, National University of Singapore, Singapore · Faculty of Computing, Harbin Institute of Technology, Harbin, China · School of Computing, National University of Singapore, Singapore
Harnesses for Inference-Time Alignment over Execution Trajectories
May 15, 2026Boyuan Wang, Bochao Li, Minghan Wang +2LLM Planning Agent Harness Optimization
Southern University of Science and Technology
Feedback World Model Enables Precise Guidance of Diffusion Policy
May 15, 2026Tuo An, Jindou Jia, Gen Li +8World Model Learning World Models for Robotics
1MARS Lab, Nanyang Technological University
Compositional Video Generation via Inference-Time Guidance
May 14, 2026Ariel Shaulov, Eitan Shaar, Amit Edenzon +2Diffusion Model Guidance Video Diffusion Models
1Tel-Aviv University · 2Independent Researcher · 3Bar Ilan University +1
Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
May 13, 2026Ye Wang, Jing Liu, Toshiaki Koike-AkinoReward Hacking LLM Alignment
Mitsubishi Electric Research Laboratories (MERL) · Broadway, Cambridge, MA 02139, USA
Aligning Flow Map Policies with Optimal Q-Guidance
May 12, 2026Christos Ziakas, Alessandra Russo, Avishek Joey BoseFlow Matching Policy Optimization
Imperial College London · Mila
LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
May 12, 2026Jeongchan Kim, Yunkyung Ko, Jong Chul YeInference-Time Search Generative Modeling
KAIST AI
Guided Streaming Stochastic Interpolant Policy
May 11, 2026Puming Jiang, Meiyi Wang, Kelvin Lin +2Robot Policy Learning Robot Policy Adaptation
School of Computing, National University of Singapore
Geometry Guided Self-Consistency for Physical AI
May 9, 2026Yinwei Dai, Zhuofu Chen, Lijie Yang +1Diffusion Policy Vision-Language-Action Models
Princeton University
Inference-Time Attribute Distribution Alignment for Unconditional Diffusion
May 8, 2026Hao Luan, See-Kiong Ng, Chun Kai LingControllable Image Generation Inference-Time Guidance
School of Computing, National University of Singapore · Institute of Data Science, National University of Singapore
Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
May 8, 2026Kim Yong Tan, Yueming Lyu, Ivor Tsang +1Diffusion Model Guidance Black-Box Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement
Apr 28, 2026Yixiao Zhou, Dongzhou Cheng, zhiliang wu +3LLM Alignment RL for Language Model Reasoning
Zhejiang University · Shanghai Innovation Institute · 1Zhejiang University, 2Shanghai Innovation Institute +9
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
Apr 27, 2026Yimin Deng, Yejing Wang, Zhenxi Lin +8Temporal Reasoning Temporal QA
Xi’an Jiaotong University · City University of Hong Kong · Tencent Jarvis Lab +1
To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending
Apr 22, 2026Jin Gan, Xin Li, Jun LuoLLM Alignment Inference-Time Language Model Alignment
College of Computing and Data Science, Nanyang Technological University, Singapore
Prompt-Guided Image Editing with Masked Logit Nudging in Visual Autoregressive Models
Apr 16, 2026Amir El-Ghoussani, Marc Hölle, Gustavo Carneiro +1Text-Guided Image Editing Visual Autoregressive Models
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · University of Surrey, United Kingdom
Dissecting Failure Dynamics in Large Language Model Reasoning
Apr 16, 2026Wei Zhu, Jian Zhang, Lixing Yu +2Inference-Time Guidance LLM Reasoning
School of Information Science and Engineering, Yunnan University, Kunming, China · Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming, China
Calibrated Test-Time Guidance for Bayesian Inference
Feb 25, 2026Daniel Geyfman, Felix Draxler, Jan Groeneveld +3Bayesian Inverse Problems Bayesian Inference
Department of Computer Science, University of California, Irvine, CA, USA · Seoul National University, Seoul, South Korea · Chan-Zuckerberg Initiative, Redwood City, CA, USA

Jun 12, 2026, cs.SDJ/K move · Enter open · S save

Mask, Sample, Revise: A Revisable CTMC Inference Stack for Guided Discrete Flow Matching Text-to-Speech

Alef Iury Siqueira Ferreira, Lucas Rafael Stefanel Gris, Luiz Fernando de Araújo Vidal, Frederico Santos de Oliveira+3

Federal University of Goi´as, Goiˆania, Brazil · Federal University of Mato Grosso, Cuiab´a, Brazil · Elsa Speak, S˜ao Paulo, Brazil

Flow Matching TTS Synthesis Flow-Based Generative Modeling Inference-Time Guidance Discrete Flow Matching

Open paper AskPDF arXiv

Recent alignment-free non-autoregressive (NAR) text-to-speech (TTS) models formulate synthesis as a conditional infilling task, bypassing explicit duration predictors and external aligners. When speech is represented with neural codec tokens, the infilling problem becomes discrete, making Discrete Flow Matching (DFM), a Continuous-Time Markov Chain (CTMC) framework for discrete generation, a natural fit. However, inference-time control for stable low-step conditional infilling remains underexplored. We propose Mask, Sample, Revise, an inference-time CTMC stack for alignment-free DFM-TTS. The stack combines predictor-free guidance to strengthen text conditioning, prompt-matched conditional coupling to align the probability path with the acoustic prompt, and SC-ReMask, a schedule-constrained remasking mechanism that introduces token-to-mask transitions so de-masking decisions can be revised. These components require no post-hoc fine-tuning and operate in a single tau-leaping sampler. Controlled ablations show that this stack improves intelligibility and robustness in the low-NFE prompted setting, outperforming unguided and guidance-only samplers with substantially more steps.

Momentum

Related topics

Latest papers 65