Test-Time Scaling

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30 papers in the last four weeks, up 58% on the four weeks before. 0.2% of all new papers.

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Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling
Apr 28, 2026Ocean Monjur, Shahriar Kabir Nahin, Anshuman ChhabraLLM Pruning Test-Time Scaling
Bellini College of AI, Cybersecurity, and Computing University of South Florida Tampa, FL, USA
Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
Apr 22, 2026Bowen Zuo, Dongruo Zhou, Yinglun ZhuEfficient Inference Test-Time Optimization
University of California, Riverside · 2Indiana University Bloomington
Bridging the Reasoning Gap in Vietnamese with Small Language Models via Test-Time Scaling
Apr 20, 2026Bui The Trung, Do Minh Duc, Nguyen Van Vinh +1Supervised Fine-Tuning Mathematical Reasoning Benchmarks
Computer Science and Engineering VNU Vietnam Japan University Hanoi, Vietnam · Information Science JAIST Ishikawa, Japan · Faculty of Information VNU University of Engineering and Technology Hanoi, Vietnam +1
Efficient Test-Time Scaling via Temporal Reasoning Aggregation
Apr 19, 2026Jiakun Li, Xingwei He, Kefan Li +3Test-Time Scaling Efficient Language Model Inference
School of Computer Science and Engineering, Beihang University · 3Hangzhou Innovation Institute, Beihang University · 2Qingdao Research Institute, Beihang University
SCATR: Simple Calibrated Test-Time Ranking
Apr 16, 2026Divya Shyamal, Marta Knežević, Lan Tran +3Test-Time Scaling Best-of-N Sampling
EPFL · MIT
(1D) Ordered Tokens Enable Efficient Test-Time Search
Apr 16, 2026Zhitong Gao, Parham Rezaei, Ali Cy +7Image Tokenization Test-Time Scaling
1Swiss Federal Institute of Technology Lausanne (EPFL) · 2Apple
Scaling Test-Time Compute for Agentic Coding
Apr 16, 2026Joongwon Kim, Wannan Yang, Kelvin Niu +13AI Coding Agents Test-Time Scaling
1Meta Superintelligence Labs · University of Washington · 3New York University +4
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Apr 16, 2026Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2Test-Time Scaling Constrained Optimization
Fudan University · ETH Zurich · Guangming Lab
C-voting: Confidence-Based Test-Time Voting without Explicit Energy Functions
Apr 15, 2026Kenji Kubo, Shunsuke Kamiya, Masanori Koyama +3Test-Time Scaling Neural Algorithmic Reasoning
Graduate School of Engineering, The University of Tokyo
Parcae: Scaling Laws For Stable Looped Language Models
Apr 14, 2026Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick +1Language Model Scaling Laws Recursive Language Models
University of California, San Diego · Together AI
Adaptive Test-Time Scaling for Zero-Shot Respiratory Audio Classification
Apr 14, 2026Tsai-Ning Wang, Herman Teun den Dekker, Lin-Lin Chen +2Respiratory Sound Classification Test-Time Scaling
Eindhoven Artificial Intelligence Systems Institute, The Netherlands
CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades
Apr 14, 2026Raeyoung Chang, Dongwook Kwon, Jisoo Lee +1LLM Routing Multi-Agent Debate
Sogang University · Kwangwoon University · Seoul National University +1
Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks
Apr 13, 2026Yoonsang Lee, Howard Yen, Xi Ye +1Test-Time Scaling Long-Horizon Agent Tasks
Princeton Language and Intelligence, Princeton University
When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling
Apr 12, 2026Shu Zhou, Rui Ling, Junan Chen +3Overthinking in Language Models Test-Time Scaling
Nanjing University · Baidu · Nanjing University of Finance & Economics
MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought
Apr 9, 2026Haodong Lei, Junming Liu, Yirong Chen +2Memory-Augmented Language Models Test-Time Scaling
Southeast University Nanjing, Jiangsu, China · Shanghai Artificial Intelligence Laboratory Shanghai, China
$S^3$ : Stratified Scaling Search for Test-Time in Diffusion Language Models
Apr 7, 2026Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +6Test-Time Scaling Diffusion Language Model Decoding
University of Oklahoma · Stanford University · University of Göttingen +2
Decocted Experience Improves Test-Time Inference in LLM Agents
Apr 6, 2026Maohao Shen, Kaiwen Zha, Zexue He +6LLM Context Management Memory-Augmented Language Model Agents
Massachusetts Institute of Technology · Stanford University · MIT-IBM Watson AI Lab +2
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models
Apr 3, 2026Kehan Jiang, Haonan Dong, Zhaolu Kang +2Efficient Language Model Reasoning Large Reasoning Models
School of Software and Microelectronics, Peking University · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Apr 2, 2026Dat Tran, Douwe KielaMulti-Agent LLM Systems Multi-Agent Systems
Stanford University
Test-Time Scaling Makes Overtraining Compute-Optimal
Apr 1, 2026Nicholas Roberts, Sungjun Cho, Zhiqi Gao +7Scaling Laws Language Model Scaling Laws
University of Wisconsin-Madison · Stanford University
Scaling Reasoning Tokens via RL and Parallel Thinking: Evidence From Competitive Programming
Apr 1, 2026Qianfan Zhang, Tianyu Guo, Xuandi Ren +4Test-Time Scaling Large Reasoning Models
Princeton University · UC Berkeley · Stanford University +1
Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
Mar 30, 2026Bin Zhu, Qianghuai Jia, Tian Lan +6Deep Research Agents LLM Agent Training
Alibaba International Digital Commerce
Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3
Mar 29, 2026Natapong NitarachLLM Reasoning LLM Prompting
Caterpillar of Thoughts: The Optimal Test-Time Algorithm for Large Language Models
Mar 24, 2026Amir Azarmehr, Soheil Behnezhad, Alma GhafariInference-Time Optimization Efficient Language Model Reasoning
Northeastern University
Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
Mar 19, 2026Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim +18LLM Mathematical Reasoning Mathematical Reasoning Benchmarks
FAIR at Meta · CMU · UCLA
Zoom to Essence: Trainless GUI Grounding by Inferring upon Interface Elements
Mar 15, 2026Ziwei Liu, Tao Feng, Borui Kang +2GUI Agents GUI Grounding
Department of Computer Science and Technology, Sichuan University, China · Department of Computer Science and Technology, Tsinghua University, China · School of Computer Science, Nanjing University, China +1
Ranking Reasoning LLMs under Test-Time Scaling
Mar 11, 2026Mohsen Hariri, Michael Hinczewski, Jing Ma +1LLM Evaluation Test-Time Scaling
Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA · Department of Physics, Case Western Reserve University, Cleveland, OH, USA
$V_1$ : Unifying Generation and Self-Verification for Parallel Reasoners
Mar 4, 2026Harman Singh, Xiuyu Li, Kusha Sareen +14Test-Time Scaling RL for Language Model Reasoning
UC Berkeley · Mila · Together AI +1
Parallel Test-Time Scaling with Multi-Sequence Verifiers
Mar 3, 2026Yegon Kim, Seungyoo Lee, Chaeyun Jang +2LLM Answer Verification Test-Time Scaling
Graduate School of AI, KAIST, Seoul, South Korea
Form Follows Function: Recursive Stem Model
Mar 3, 2026Navid HakimiNeural Algorithmic Reasoning Algorithmic Reasoning

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Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

Ocean Monjur, Shahriar Kabir Nahin, Anshuman Chhabra

Bellini College of AI, Cybersecurity, and Computing University of South Florida Tampa, FL, USA

LLM Pruning Test-Time Scaling Neural Network Pruning

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Large Language Models (LLMs) now exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), with impressive performance across math and coding benchmarks. In parallel, research in model compression has developed pruning methods that seek to remove redundant/detrimental parameters without sacrificing task performance. The intersection of these two research advancements lays the foundation for our work. Specific to reasoning LLMs, prior work has shown that structured pruning (methods which remove entire set of layer blocks), significantly degrades TTS reasoning performance. However, in this work, we revisit this assumption and investigate whether unstructured pruning (methods that carefully remove only certain redundant/detrimental weights) exhibits similar limitations. Surprisingly, our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs. Furthermore, we also empirically study the impact of different layer-wise sparsity allocation strategies, which are an important parametric choice for instantiating these unstructured methods. These findings challenge the conventional notion that pruning always reduces TTS performance and in fact, suggest that carefully undertaken pruning can retain TTS effectiveness.