Language Model Decoding

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18 papers in the last four weeks, up 200% on the four weeks before. 0.2% of all new papers.

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Fast-dLLM++: Fréchet Profile Decoding for Faster Diffusion LLM Inference
Jun 1, 2026Siva Rajesh Kasa, Yasong Dai, Sumit Negi +1Language Model Decoding Parallel Decoding
Amazon Inc. · School of Computing, Australian National University.
Decoding in Order-Agnostic Language Models: Chain-Rule Deviation and Uniform Spreading
May 31, 2026Lin YaoLanguage Model Decoding Confidence Estimation in Language Models
School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China · Zhongguancun Academy, Beijing, 100097, China
Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
May 30, 2026Hanze Li, Jinhao You, Yichen Guo +3Language Model Decoding LLM Hallucination Mitigation
De Artificial Intelligence Lab
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
Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG
May 29, 2026Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1Retrieval-Augmented Generation Language Model Decoding
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology · Department of Civil, Construction & Environmental Engineering, Iowa State University
MarginGate: Sparse Margin-Triggered Verification for Batch-Invariant LLM Inference
May 28, 2026Kexin Chu, Yang Zhou, Wei ZhangLLM Inference Efficiency Language Model Decoding
University of Connecticut · UC Davis
Projectional Decoding: Towards Semantic-Aware LLM Generation
May 28, 2026Boqi Chen, José Antonio Hernández López, Aren A. BabikianLanguage Model Decoding LLM-Based Program Synthesis
University of Ottawa · University of Murcia · University of Toronto
Decoding Strategies for Diffusion-Based ASR: A Systematic Evaluation of Confidence-Based Thresholding
May 28, 2026Jeong Hun Yeo, Minsu Kim, Hyeongseop Rha +1Language Model Decoding Automatic Speech Recognition
KAIST, Daejeon, Republic of Korea · Google DeepMind, Tokyo, Japan
The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
May 27, 2026Dueun Kim, Albert NoMasked Diffusion Models Language Model Decoding
Department of Artificial Intelligence, Yonsei University
When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models
May 27, 2026Jungwon Park, Jimyeong Kim, Jungmin Ko +2Non-Autoregressive Text Generation Language Model Decoding
1RICS · 2AIIS · 3IPAI +2
The Missing Piece in Pre-trained Model Evaluation: Reward-Guided Decoding Unlocks Task-Oriented Behavior Without Parameter Updates
May 27, 2026Shaobo Wang, Guo Chen, Ziyue Wang +5LLM Evaluation Language Model Decoding
Shanghai Jiao Tong University · Qwen Team, Alibaba Group · The Chinese University of Hong Kong, Shenzhen
SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising
May 27, 2026Akshaj Murhekar, Abhijit MishraLanguage Model Decoding EEG Decoding
School of Information University of Texas at Austin
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
May 26, 2026Samer Awad, Javier Conde, Carlos Arriaga +3LLM Evaluation Language Model Decoding
Information and Processing Telecommunications Center Universidad Politécnica de Madrid Madrid, Spain · Politecnico di Milano Milano, Italy · Banco de España Madrid, Spain
Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
May 25, 2026Yulin Yuan, Hongshuo Zhao, Xiangming MengLanguage Model Decoding Multimodal Large Language Models
Zhejiang University · ZJUI-UIUC Institute
Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
May 23, 2026Weixian Waylon Li, Mengyu Wang, Tiejun MaLanguage Model Decoding Quantitative Finance
University of Edinburgh
When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions
May 20, 2026Wei Xia, Haoqing Wang, Zhi-Hong Deng +1Language Model Decoding CoT Reasoning
Samsung Research, Beijing, China · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University
Verifier-Guided Code Translation via Meta-Step Decoding
May 17, 2026Tianyang Zhou, Somesh Jha, Mihai Christodorescu +2Code Translation Constrained Decoding
University of Illinois Urbana-Champaign · University of Wisconsin–Madison and Google · Google
Selective Safety Steering via Value-Filtered Decoding
May 14, 2026Bat-Sheva Einbinder, Hen Davidov, Yee Whye Teh +2Language Model Decoding Language Model Steering
Department of Electrical and Computer Engineering, Technion IIT, Israel · Department of Statistics, University of Oxford, Oxford, UK · OATML, Department of Computer Science, University of Oxford, Oxford, UK +1
Sampling from Flow Language Models via Marginal-Conditioned Bridges
May 13, 2026Iskander Azangulov, Leo ZhangFlow Matching Language Model Decoding
Department of Statistics, University of Oxford
Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion
May 12, 2026Chien Van Nguyen, Chaitra Hegde, Van Cuong Pham +3Language Model Decoding Diffusion Language Model Inference
University of Oregon · Google DeepMind · Adobe Research
Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
May 12, 2026Yigeng Zhou, Wu Li, Yifan Lu +6Language Model Decoding Knowledge Conflicts in Language Models
Harbin Institute of Technology, Shenzhen, 518055, China · Beijing Academy of Artificial Intelligence, China · Nanyang Technological University, Singapore
The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures
May 12, 2026Bole Ma, Ayesha Afzal, Jan Eitzinger +1LLM Inference Efficiency Language Model Decoding
Erlangen National High Performance Computing Center, Erlangen, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
May 11, 2026Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya +6LLM Evaluation Language Model Decoding
♠University of Southern California · ♣Capital One
Task-Aware Calibration: Provably Optimal Decoding in LLMs
May 11, 2026Tim Tomov, Dominik Fuchsgruber, Rajeev Verma +1Language Model Decoding MBR Decoding
School of Computation, Information & Technology, Technical University of Munich · Munich Data Science Institute · Munich Center for Machine Learning +1
Entropy-informed Decoding: Adaptive Information-Driven Branching
May 10, 2026Benjamin Patrick Evans, Sumitra Ganesh, Leo ArdonLanguage Model Decoding LLM Inference Acceleration
JP Morgan AI Research, London, UK · JP Morgan AI Research, New York, USA
APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation
May 10, 2026Tianyu Zheng, Hong Wu, Jiaji ZhongLanguage Model Decoding LLM Reliability
University of Electronic Science and Technology of China
A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability
May 9, 2026Hamed Omidvar, Vahideh AkhlaghiLLM Inference Efficiency AI Agent Reliability
INTELLERCE LLC San Jose, CA, USA
The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
May 4, 2026Tu Nguyen, Matthieu Zimmer, Rasul Tutunov +2Language Model Decoding LLM Inference Acceleration
1Huawei Heisenberg Research Center · 2Huawei Noah’s Ark Lab · 3UCL Centre for Artificial Intelligence
Decoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended Generation
May 4, 2026Muneeb Ur Raheem KhanDebiased ML Language Model Decoding
Lahore University of Management Sciences (LUMS)
Large Language Models Explore by Latent Distilling
Apr 27, 2026Yuanhao Zeng, Ao Lu, Lufei Li +3Language Model Decoding Test-Time Training
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China.
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Apr 25, 2026Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2Language Model Decoding Generative Retrieval
University of Amsterdam Amsterdam, The Netherlands
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding
Apr 24, 2026Weixu Zhang, Fanghua Ye, Qiang Gao +7Contextual Faithfulness Language Model Decoding
Hunyuan AI Digital Human, Tencent · McGill University · Mila - Quebec AI Institute +4
Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees
Apr 22, 2026Xueyan Li, Johannes Zenn, Ekaterina Fadeeva +3Language Model Decoding Inference-Time Search
Max Planck Institute for Intelligent Systems · ETH Zurich · AI Center Tübingen +3
LatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesis
Apr 22, 2026Faruk Alpay, Bugra KilictasConstrained Decoding Grounded Text Generation
Department of Computer Engineering, Bahcesehir University
DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion
Apr 20, 2026Rahul Mehta, Kavin R, Indrajit Pal +3Language Model Decoding
Microsoft Corporation Hyderabad, India · Indian Institute of Technology Kharagpur, India · Independent Bengaluru, India
No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation
Apr 17, 2026Yufei Tao, Ameeta AgrawalLanguage Model Decoding LLM Reliability
Department of Computer Science, Portland State University, USA
Embarrassingly Simple Self-Distillation Improves Code Generation
Apr 1, 2026Ruixiang Zhang, Richard He Bai, Huangjie Zheng +3Language Model Decoding Language Model Distillation
Apple
The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices
Mar 19, 2026Esteban Garces Arias, Nurzhan Sapargali, Christian Heumann +1AI-Generated Text Detection Language Model Decoding
Department of Statistics, LMU Munich · Munich Center for Machine Learning (MCML)
Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding
Mar 6, 2026Hoseong Ahn, Jeongyun Chae, Yoonji Park +1Language Model Decoding Automatic Speech Recognition
Sungkyunkwan University, Republic of Korea
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
Mar 3, 2026Anum Afzal, Yuki Saito, Hiroya Takamura +5Language Model Decoding Multimodal Large Language Models
School of CIT, Technical University of Munich · National Institute of Advanced Industrial Science and Technology (AIST) · The University of Tokyo +3
Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model
Feb 6, 2026Jacqueline He, Jonathan Hayase, Wen-tau Yih +3Constrained Decoding Language Model Decoding
University of Washington · Allen Institute for Artificial Intelligence
Attention-Mass Condensation for Sparse Decoding
Feb 6, 2026Jorge L. Ruiz WilliamsLanguage Model Decoding Sparse Attention
NaNZeta LLC
LEAD: Layer-wise Expert-aligned Decoding for Faithful Radiology Report Generation
Feb 4, 2026Ruixiao Yang, Yuanhe Tian, Di Dong +3Radiology Report Generation Language Model Decoding
Beijing Institute of Technology · Zhongguancun Academy · Zhongguancun Institute of Artificial Intelligence +1
Strategies for Span Labeling with Large Language Models
Jan 23, 2026Danil Semin, Ondřej Dušek, Zdeněk KasnerConstrained Decoding Language Model Decoding
Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics, Charles University
LOGIC: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration
Jan 21, 2026Peidong Wang, Jian Xue, Jinyu LiLanguage Model Decoding Speech Processing
Microsoft, USA
AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
Jan 9, 2026Chengming Cui, Tianxin Wei, Ziyi Chen +6Language Model Decoding Test-Time Scaling
University of Illinois Urbana-Champaign
ScalingAR: Scaling Confidence for Autoregressive Image Generation
Sep 30, 2025Harold Haodong Chen, Xianfeng Wu, Wen-Jie Shu +4Language Model Decoding Visual Autoregressive Models
HKUST(GZ) · UNC-Chapel Hill · ZODA +3
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
Mar 4, 2025Zhenhua Liu, Lijun Li, Ruizhe Chen +5LLM Alignment Language Model Decoding
Shanghai Artificial Intelligence Laboratory · Soochow University · Zhejiang University +1

Jun 1, 2026, cs.CLJ/K move · Enter open · S save

Fast-dLLM++: Fréchet Profile Decoding for Faster Diffusion LLM Inference

Siva Rajesh Kasa, Yasong Dai, Sumit Negi, Hongdong Li

Amazon Inc. · School of Computing, Australian National University.

Language Model Decoding Parallel Decoding Diffusion Language Model Decoding Diffusion Language Models Diffusion Model Inference Acceleration

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Diffusion large language models promise parallel token generation, yet inference remains bottlenecked by deciding which masked tokens can be safely committed together. Fast-dLLM addressed this with KV caching and confidence-guided parallel decoding, but its decoding theory uses a homogeneous high-confidence assumption that effectively reduces each candidate set to its weakest selected token. We argue that this leaves speed on the table because real decoding steps exhibit heterogeneous confidence profiles. We propose \textbf{Fast-dLLM++}, a training-free extension that introduces \emph{Fréchet profile decoding}: selecting parallel commit sets from the full sorted confidence profile rather than a single worst-case confidence. The resulting rule is a heterogeneous-confidence generalization of Fast-dLLM's factor selector and it recovers the previous rule exactly in the equal-confidence case and adds a provable \emph{heterogeneity bonus} when the selected tokens have uneven confidences. Fast-dLLM++ leaves the model, diffusion process, and cache implementation entirely unchanged, making it a drop-in replacement for existing Fast-dLLM decoding. Experiments on GSM8K, MATH, HumanEval, and MBPP with the LLaDA-8B model show that the theoretical improvement translates directly into empirical gains: profile-aware selection improves the accuracy--throughput frontier by exploiting safe parallelism that weakest-token rules miss, achieving up to 37% higher throughput at comparable accuracy. Our code release is at https://github.com/Ringo-Star/FastdLLM_plusplus.

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