Adaptive Computation

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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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Latest papers 59

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Forget, Anticipate and Adapt: Test Time Training for Long Videos
Jun 25, 2026Rajat Modi, Sebastian Noel, Xin Liang +1Continual Test-Time Adaptation Test-Time Training
Institute of Artificial Intelligence, University of Central Florida
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Jun 24, 2026Aneesh Muppidi, Firas Darwish, Dylan Cope +2Reinforcement Learning Policy Learning
British Open-ended Learning and Discovery Lab (BOLD), University of Oxford · Department of Statistics, University of Oxford · VGG, University of Oxford
ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving
Jun 24, 2026Sining Ang, Yuan Chen, Liu Haiyan +5LLM Inference Acceleration Autonomous Driving Planning
Institute for AI Industry Research (AIR), Tsinghua University · Department of Automation, University of Science and Technology of China · Beijing University of Aeronautics and Astronautics +1
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Jun 16, 2026Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin +5Recurrent Transformers Algorithmic Reasoning
ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center · ETH Zurich · Swiss Institute of Bioinformatics +2
Learning to Refine Hidden States for Reliable LLM Reasoning
Jun 16, 2026Chia-Hsuan Hsu, Jui-Ming YaoRL for Language Model Reasoning Efficient Language Model Reasoning
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
Jun 15, 2026Hanyu Lin, Min Cai, Jiawei Wen +1Language Model Decoding Efficient Language Model Reasoning
1Shenzhen University · University of Alberta
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
Jun 5, 2026Tengyao Tu, Yulin Li, Hui-Ling Zhen +6Overthinking in Language Models Efficient Language Model Reasoning
Harbin Institute of Technology, Shenzhen · Zhongguancun Academy · Huawei Noah’s Ark Lab +2
A Dual-Path Architecture for Scaling Compute and Capacity in LLMs
May 28, 2026Markus Frey, Behzad Shomali, Joachim Koehler +1Recurrent Transformers Language Model Scaling
Lamarr Institute · Fraunhofer IAIS · University of Bonn
ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models
May 28, 2026Ye Li, Huanan Liu, Kangye Ji +7Efficient VLM Inference Efficient VLA Model Inference
Tsinghua University · University of Illinois at Urbana-Champaign
LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
May 27, 2026Jintao Li, Yong-Yi Wang, Zheng-An Wang +1Efficient Inference Inference-Time Optimization
Beijing Key Laboratory of Fault-Tolerant Quantum Computing, Beijing Academy of Quantum Information Sciences, Beijing, China · Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, CAS, Beijing, China · Beijing Key Laboratory of Advanced Quantum Technology, Beijing, China +1
CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models
May 27, 2026Venkat Akhil LakkapragadaSmall Language Models LLM Reasoning
Mistyoz AI Hyderabad, India
Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning
May 27, 2026Zhenyu Cui, Xiangzhong LuoLLM Interpretability Mechanistic Interpretability
Growing a Neural Network in Breadth, Depth, and Time
May 24, 2026Eivinas Butkus, Kedar Garzón Gupta, Nikolaus KriegeskorteConvolutional RNNs Dynamic Neural Networks
1Columbia University · 2NSF AI Institute for Artificial and Natural Intelligence
Process Rewards with Learned Reliability
May 15, 2026Jinyuan Li, Langlin Huang, Chengsong Huang +5Reward Modeling Process Reward Models
Washington University in St. Louis · Singapore University of Technology and Design
GridProbe: Posterior-Probing for Adaptive Test-Time Compute in Long-Video VLMs
May 11, 2026Mohamed Eltahir, Lama Ayash, Ali Habibullah +2Efficient VLM Inference Long-Video Understanding
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Department of Computer Science, Edge Hill University, Ormskirk, England
Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning
May 11, 2026Debashis GuhaAdaptive Learning Adaptive Computation
S P Jain School of Global Management
Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models
May 10, 2026Lin Zheng, Vasilisa Bashlovkina, Timothy Dozat +3Byte-Level Language Model Language Modeling
1Google DeepMind · 2The University of Hong Kong
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
May 10, 2026Taekhyun Park, Yongjae Lee, Dohee Kim +1Recurrent Transformers LLM Post-Training
Department of Data Science Pusan National University Busan, Republic of Korea · Department of Industrial Engineering Pusan National University Busan, Republic of Korea · Department of Artificial Intelligence Engineering Changwon National University Changwon, Republic of Korea
Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking
May 7, 2026Shiao Wang, Xiao Wang, Duoqing Yang +5Event-Based Vision Mixture of Experts
School of Computer Science and Technology, Anhui University, Hefei 230601, China · Beijing Institute of Technology, Beijing, China · Peng Cheng Laboratory, Shenzhen, China +2
Measuring AI Reasoning: A Guide for Researchers
May 4, 2026Munachiso Samuel Nwadike, Zangir Iklassov, Kareem Ali +2Reasoning Evaluation LLM Reasoning
MBZUAI, Department of Natural Language Processing · MBZUAI, Department of Machine Learning
HALO: Hybrid Adaptive Latent Reasoning for Language Models
May 3, 2026Micah ZhangLLM Inference Acceleration Adaptive Computation
Lockheed Martin
Don't Pause! Every prediction matters in a streaming video
Apr 27, 2026Dibyadip Chatterjee, Zhanzhong Pang, Fadime Sener +2Streaming Video Understanding Efficient VLM Inference
National University of Singapore · Google Inc.
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
Apr 27, 2026Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos TserpesMulti-Armed Bandits Dynamic Neural Networks
Department of Electrical and Computer Engineering National Technical University of Athens, Greece
Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement
Apr 23, 2026Mahdi Kallel, Johannes Tölle, Ahmed Hendawy +1Reinforcement Learning Classification
Center for Artificial Intelligence and Data Science, University of Würzburg · Department of Computer Science, Technical University of Darmstadt
Universal Transformers Need Memory: Depth-State Trade-offs in Adaptive Recursive Reasoning
Apr 23, 2026Grigory SapunovMemory-Augmented Neural Networks Recurrent Transformers
Intento
Depth Adaptive Efficient Visual Autoregressive Modeling
Apr 19, 2026Chunliang Li, Tianze Cao, Sanyuan ZhaoEfficient Transformer Inference Visual Autoregressive Models
Beijing Institute of Technology · Beijing Institute of Technology, Zhuhai
Adaptive Computation Depth via Learned Token Routing in Transformers
Apr 18, 2026Ahmed Abdelmuniem Abdalla MohammedEfficient Transformer Inference Transformer
Independent Researcher
Scaling Latent Reasoning via Looped Language Models
Oct 29, 2025Rui-Jie Zhu, Zixuan Wang, Kai Hua +30Language Model Pretraining Implicit Reasoning in Language Models
1ByteDance Seed · 2UC Santa Cruz · 3Princeton University +8
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
Apr 21, 2023Shuyin Xia, Guoyin Wang, Xinbo Gao +2Interpretable ML Adaptive Computation
Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, and Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China · College of Computer and Information Science, the National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China

Jun 25, 2026, cs.CVJ/K move · Enter open · S save

Forget, Anticipate and Adapt: Test Time Training for Long Videos

Rajat Modi, Sebastian Noel, Xin Liang, Yogesh Singh Rawat

Institute of Artificial Intelligence, University of Central Florida

Continual Test-Time Adaptation Test-Time Training Long-Video Understanding Adaptive Computation

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Test Time Training (TTT) is a mechanism in which a model adapts to an incoming test-sample by performing some self-supervised (SSL) task and updating its weights even during inference. This procedure does not require labels at test-time. This paper focuses on TTT for long-videos. A major concern with existing approaches is: 1) they perform TTT updates using a sliding window containing frames in the past, whose compute increases linearly with the size of window. This becomes computationally intractable when the videos are hours long. 2) TTT is performed even when temporally close frames look similar, thereby consuming a lot of compute. We present the Frame Forgetting Network (FFN) that: 1) operates on only three frames within the sliding window, namely the frame that exits, the current frame and the frame after that. The model still manages to retain temporal context and work for hours long-videos; 2) mathematically define a surprise metric: how much new information the incoming frame contains with respect to the past seen frame. This facilitates determining how to modify the effective window size during TTT and constitutes the core mechanism of an adaptive windowing algorithm. Additionally, we curate a dataset EpicTours containing up to 3 hour long videos of walking city-tours, whereas earlier datasets on this problem were only 5 min long. We demonstrate FFNs empirical effectiveness on dense-segmentation, video classification tasks, generalization to depth-estimation, and multi-hour long videos.

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