Inference-Time Adaptation of Language Models

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43 papers17 papers in the last four weeks, up 325% on the four weeks before. 0.1% of all new papers.

Where Should Learning Live? Regret Analysis for Text, Parameter, and Joint Adaptation
Nov 6, 2026Jungtaek Kim, Jaden Park, Jongwon Jeong +2Inference-Time Adaptation of Language Models Online Learning
University of Arkansas · University of Wisconsin–Madison · KRAFTON +1
WeightLoop: Weight-Space Programming with Experimental Feedback
Nov 5, 2026Mengran Li, Bo Li, Weijing Zhao +6Language Model Self-Improvement Weight-Space Learning
Hong Kong Institute of Science and Innovation · University of Macau · Sichuan University +3
Evolving Memory, Learning to Meta-Control: Test-Time Adaptation for Experience-augmented LLM Agents
Nov 1, 2026Zeyuan Zhao, Yuanyuan Shi, Peng Yu +3LLM Agents Experience Learning for LLM Agents
Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory
Latent Harness: Compiling Agent Behaviors into Post-Block Activation Shifts
Nov 1, 2026Ran Li, Shimin Di, Zeyu Ren +3LLM Agent Harnesses Inference-Time Adaptation of Language Models
Hong Kong University of Science and Technology · Southeast University · Rensselaer Polytechnic Institute +2
Precision--Recall Trade-off on the Inference Level for LLMs in Grammatical Error Correction
Oct 30, 2026Ryszard Staruch, Filip GralińskiGrammatical Error Correction Inference-Time Adaptation of Language Models
Adam Mickiewicz University
LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction
Oct 30, 2026Kwan Yan, Jialun Zhong, Yanzeng LiLLM Output Length Prediction LLM Inference Scheduling
Beijing Normal University · Peking University
Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer
Oct 30, 2026Minju Gwak, Alan RitterLanguage Model Self-Improvement RL for Language Model Reasoning
Georgia Institute of Technology
LatentWeave: Test-Time Latent Optimization for Multimodal Reasoning
Oct 29, 2026Fanci Meng, Kun Wang, Guibin Zhang +4MLLM Reasoning Inference-Time Adaptation of Language Models
University of Science and Technology of China · Nanyang Technological University · National University of Singapore +2
Compute Has Shape: Self-Morphing the Internal Computation of Frozen LLMs
Oct 28, 2026Junxi Wu, Yiwen Guo, Shu-Tao XiaInference-Time Adaptation of Language Models Test-Time Scaling
School of Statistics and Data Science, Nankai University · Tsinghua Shenzhen International Graduate School, Tsinghua University · Independent Researcher
Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training
Oct 27, 2026Yue Wu, Yang Zhang, Weinan Gan +4Test-Time Training Inference-Time Adaptation of Language Models
University of Science and Technology of China China · National University of Singapore Singapore · Huawei Noah’s Ark Lab China
ADTGuard: Adaptive Depth-Time Safeguard for Streaming Inference in Large Language Models
Oct 27, 2026Chuming Wei, Churui Yang, Zihan Zhang +4LLM Safety Monitoring Inference-Time Adaptation of Language Models
Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University · Faculty of Science and Engineering, University of Nottingham Ningbo China · Department of Computer Sciences, University of Wisconsin–Madison +1
Beyond Update Counts: A Control Map for Evaluating Test-Time Model Updates
Oct 26, 2026Zehao Zhao, Shangjian Yin, Rui Liu +6LLM Evaluation Inference-Time Adaptation of Language Models
Meta AI
Test-Time Confidence Adaptation under RL-Induced Trajectory Shift
Oct 26, 2026Zhenzhen Ren, Xiaoqing Zhang, GuoBiao Li +4Confidence Estimation in Language Models Inference-Time Adaptation of Language Models
Fudan University · Zhongguancun Academy · Renmin University of China +4
EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems
Oct 25, 2026Yalun Wu, Qiwei Ma, Bingbing Xu +10Multi-Agent Systems Self-Evolving Agents
National University of Singapore · Hunan University · Institute of Computing Technology, Chinese Academy of Sciences +4
Counterfactual Test-Time Augmentation for Shortcut Removal in Language Models
Oct 24, 2026Katie Matton, Rosalind W. Picard, John Guttag +1Shortcut Learning in Language Models Test-Time Augmentation
MIT · University of Michigan
Delta N-gram: Forward-Only Continual Writing into a Language Model's Native N-gram Memory
Oct 22, 2026Hao WuPersistent Memory for Language Models Memory-Augmented Language Models
Illinois Institute of Technology
Harnessing Language Models as Optimization Modeling Agents
Oct 22, 2026Yunxiang Luo, Xin Xu, Tianhao Chen +7Large Language Model-Guided Optimization Natural Language to Optimization Modeling
The Hong Kong University of Science and Technology · Huawei
AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
Oct 19, 2026Rui Wang, Zishen Lai, Jiayu Liu +15LLM Agent Evaluation AI Agent Benchmarks
HKUST · HKUST-UGAIL · HKUST(GZ) +3
TIPS: A Tiny Inference-time Policy for Scaling in Diffusion Language Models
Oct 15, 2026Ya-Shi Zhang, Jian Tang, Pierre-André Noël +1Diffusion Language Model Decoding Inference-Time Adaptation of Language Models
ServiceNow AI Research · Mila - Quebec AI Institute · HEC Montréal
Self-Generated Feedback Destabilizes Test-Time Training: A Causal Decomposition of Long-Horizon Adaptation
Oct 4, 2026Cheng Luo, Bing Li, Bernard GhanemTest-Time Adaptation Continual Test-Time Adaptation
King Abdullah University of Science and Technology (KAUST)
Dynamic Routing as a New Dimension for Test-time Versatility of LLMs
Oct 3, 2026Michal Štefánik, Marek Kadlčík, Josef Kuchař +1Inference-Time Adaptation of Language Models LLM Routing
TransformersClub, Faculty of Informatics, Masaryk University · R&D Centre for Large Language Models, National Institute of Informatics, Japan
Test-time Calibration Learning for Large Language Model Reasoning
Oct 2, 2026Zizhuo Zhang, Xiong Peng, Jingwei Sun +4Inference-Time Adaptation of Language Models Language Model Calibration
TMLR Group, Department of Computer Science, Hong Kong Baptist University · Huawei Noah’s Ark Lab.
Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning
Sep 10, 2026Bincheng Gu, Min Gao, Zongwei Wang +3Test-Time Adaptation LLM Reliability
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, China · King’s College London, London, UK · Griffith University, Brisbane, Australia
Safin-1: Safety from Within through Memory-Native State Evolution
Aug 31, 2026Ming Zhang, Kaisen Yang, Shu Yu +15Memory-Augmented Language Models LLM Safety Alignment
Shanghai AI Laboratory
Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching
Aug 10, 2026Kristian Schwethelm, Daniel Rueckert, Georgios KaissisLLM Inference LLM Inference Scheduling
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM), Germany · Department of Computing, Imperial College London, UK · Munich Center for Machine Learning (MCML), Germany +1
Strengthening Target-Language Features: SAE-Based Steering for Multilingual Inference
Aug 5, 2026Hongsheng Wang, Philipp KoehnMultilingual Language Models Language Model Steering
Johns Hopkins University, Baltimore / USA
SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute
Jul 30, 2026Hongyu Chen, Liang Lin, Guangrun WangLanguage Model Self-Verification RL for Language Model Reasoning
1Sun Yat-sen University · 2Guangdong Key Laboratory of Big Data Analysis and Processing · 3X-Era AI Lab
Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias
Jul 24, 2026Ofek I. Cohen, Lior Shani, Aviv Rosenberg +3Black-Box Optimization Domain Adaptation for LLMs
Tel Aviv University · Google Research · Columbia University
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
Jul 7, 2026Heting MaoLLM Alignment Memory-Augmented Language Models
Shanghai Lixin University of Accounting and Finance
Test-Time Training with Next-Token Prediction
Jun 19, 2026Xuan Ouyang, Zefan Cai, Junjie HuLong-Context Language Modeling Next-Token Prediction
University of Wisconsin–Madison

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Where Should Learning Live? Regret Analysis for Text, Parameter, and Joint Adaptation

Jungtaek Kim, Jaden Park, Jongwon Jeong, Emmanouil-Vasileios Vlatakis-Gkaragkounis+1

University of Arkansas · University of Wisconsin–Madison · KRAFTON+1

Inference-Time Adaptation of Language Models Online Learning

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Language-model systems can select prompts and parameter adapters repeatedly during deployment, using feedback to guide later choices. Earlier choices may change the context or environment inherited by later configurations. We study selection among preconstructed candidates and compare cumulative loss with the best fixed prompt--parameter pair used from the beginning. This excess loss separates into three terms: selection regret within the available family, the gap from restricting that family, and the benefit or harm of the actual deployment history. Selection is evaluated using the history each candidate would have produced if used throughout, which separates candidate quality from the effects of switching. When loss functions are fixed independently of selector randomness, past choices affect loss for only a bounded number of rounds, and every candidate's own-history loss is revealed, limiting switches yields sublinear expected regret within the selected family. We also quantify the costs and additional assumptions of independent probing and deployment-only feedback. Controlled experiments show that fewer switches can either lower or raise deployed loss, depending on whether inherited histories are harmful or helpful. With Qwen3.5-9B on Spider, text-only selection has lower within-family regret than joint selection, yet produces more failures because its candidate family excludes stronger configurations.