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.

FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation
May 6, 2026Guangsheng Bao, Hongbo Zhang, Han Cui +4Efficient Neural Network Inference Test-Time Adaptation
Zhejiang University · Westlake University · Shanghai Polytechnic University +1
From Weights to Activations: Is Steering the Next Frontier of Adaptation?
Apr 15, 2026Simon Ostermann, Daniil Gurgurov, Tanja Baeumel +4Online Language Model Adaptation Language Model Steering
Saarland University · German Research Center for Artificial Intelligence (DFKI) · Centre for European Research in Trusted AI (CERTAIN) +6
Self-Calibrating Language Models via Test-Time Discriminative Distillation
Mar 18, 2026Mohamed Rissal Hedna, Jan Strich, Martin Semmann +1Inference-Time Adaptation of Language Models Language Model Calibration
Hub of Computing and Data Science (HCDS), University of Hamburg, Germany
Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation
Mar 14, 2026Hanwen Shen, Ting Ying, Jiajie Lu +1Narrative Generation Language Model Debiasing
Laboratory for Artificial Intelligence in Mathematics Education, Stevens Institute of Technology · Independent Researcher · NLP2CT Lab, Department of Computer and Information Science, University of Macau
Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLMs via Global Attention Reallocation
Mar 10, 2026Jingtao Wang, Yucong Wang, Jun Ding +2LLM Inference Inference-Time Adaptation of Language Models
Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada · Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada · The College of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, China +4
Learning Adaptive LLM Decoding
Mar 10, 2026Chloe H. Su, Zhe Ye, Samuel Tenka +3Language Model Decoding Inference-Time Adaptation of Language Models
Department of Computer Science, Harvard University, Boston, USA · Kempner Institute, Harvard University, Cambridge, MA, USA · UC Berkeley, CA, USA +1
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Mar 6, 2026Shubhangi Upasani, Chen Wu, Jay Rainton +4Inference-Time Adaptation of Language Models LLM Prompting
SambaNova Systems, Inc · Stanford University
N-gram Injection into Transformers for Dynamic Language Model Adaptation in Handwritten Text Recognition
Mar 4, 2026Florent Meyer, Laurent Guichard, Yann Soullard +3Inference-Time Adaptation of Language Models Language Modeling
ANTAI, Rennes, France · Univ Rennes, CNRS, IRISA - UMR 6074, Rennes, France
Test-Time Meta-Adaptation with Self-Synthesis
Mar 3, 2026Zeyneb N. Kaya, Nick RuiInference-Time Adaptation of Language Models Test-Time Adaptation
Stanford University
PonderLM-3: Adaptive Token-Wise Pondering with Differentiable Masking
Mar 2, 2026He Li, Feichen Song, Boyi Zeng +4Adaptive Computation Inference-Time Adaptation of Language Models
LUMIA Lab, School of Artificial Intelligence, Shanghai Jiao Tong University · Shanghai AI Laboratory · Shanghai Jiao Tong University +1
AdaPonderLM: Gated Pondering Language Models with Token-Wise Adaptive Depth
Mar 2, 2026Shixiang Song, He Li, Zitong Wang +6Recurrent Language Models Inference-Time Adaptation of Language Models
LUMIA Lab, School of Artificial Intelligence, Shanghai Jiao Tong University · Shanghai Innovation Institute · Sun Yat-sen University +2
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Mar 2, 2026Chenxing Wei, Hong Wang, Ying He +4Inference-Time Adaptation of Language Models Inference-Time Language Model Alignment
Shenzhen University · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Bytedance +4
Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs
Sep 27, 2025Chenxing Wei, Hong Wang, Ying He +2Inference-Time Adaptation of Language Models Multi-Turn Dialogue Management
School of Computing and Data Science, The University of Hong Kong · Shenzhen Loop Area Institute · University of Science and Technology of China +2

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FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation

Guangsheng Bao, Hongbo Zhang, Han Cui, Ke Sun+3

Zhejiang University · Westlake University · Shanghai Polytechnic University+1

Efficient Neural Network Inference Test-Time Adaptation Fast Weights Inference-Time Adaptation of Language Models

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Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memory-based or in-context learning. We propose FAAST, a forward-only associative adaptation method that analytically compiles labeled examples into fast weights in a single pass. By eliminating memory or context dependence, FAAST achieves constant-time inference and decouples task adaptation from pretrained representation. Across image classification and language modeling benchmarks, FAAST matches or exceeds backprop-based adaptation while reducing adaptation time by over 90% and is competitive to memory/context-based adaptation while saving memory usage by up to 95%. These results demonstrate FAAST as a highly efficient, scalable solution for supervised task adaptation, particularly for resource-constrained models. We release the code and models at https://github.com/baoguangsheng/faast.