Latest Test-Time Compute Research Papers

The newest Test-Time Compute papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Test-Time Compute so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• Kronecker Delta Rule for Long Context Linear Attention
  Anamaria-Roberta Hartl, Levente Zólyomi, Michael List, Günter Klambauer et al. · arXiv · Oct 19, 2026

  We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories. KronLSTM extends the xLSTM family with a memory-revision mechanism that corrects targeted associati…

• recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models
  Saurav Jha, Qianli Liao, Robert Vernon, Sarath Chandar et al. · arXiv · Oct 19, 2026

  Protein language models (pLMs) are powerful tools for protein annotation and mutation-effect prediction, but progress has largely depended on scaling model size and data, making them expensive and prone to memorization. Test-time scaling of…

• Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
  Anikait Singh, Teresa Zhang, Yoonho Lee, Roshen Sanjay Nair et al. · arXiv · Oct 15, 2026

  Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottlenecking agents with complex, cross-document reasoning at inference time, while precomputed…

• Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling
  Weixin Wang, Wei Deng, Anderson Schneider, Yuriy Nevmyvaka et al. · arXiv · Oct 14, 2026

  We study how to improve LLM reasoning at inference time without updating model weights or using external reward models. Recent reward-free power-sampling methods define the sequence-level power target $π_α(y\mid x)\propto p_0(y\mid x)^α$, w…

• Decoupling Exploration from Optimization in RLVR
  Saif Punjwani, Micah Goldblum · arXiv · Oct 7, 2026

  Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas abs…

• CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling
  Yifan Zhang, Yutong Dai, Viraj Prabhu, Zhiyuan Hu et al. · arXiv · Oct 5, 2026

  Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak supervision: binary task success is too sparse for credit assignment, while frontier-langua…

• IdeaLens: Detecting AI Ideas in Long-form Writing
  Rishanth Rajendhran, Minjoon Choi, Jenna Russell, Ramya Namuduri et al. · arXiv · Oct 5, 2026

  While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came fr…

• Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
  Sohail, Sarkar, Shakuntala Baichoo · arXiv · Sep 30, 2026

  Sampling several answers and keeping the one a verifier scores highest is one of the simplest ways to buy accuracy at test time. Its effect is reported as a scaling curve: accuracy against the number $k$ of sampled answers. The curve is che…

• Improving Test-Time Scaling with Adaptive Looped Transformers
  Yichen You, Tianyu Fu, Aosong Feng, Xingtai Lv et al. · arXiv · Sep 28, 2026

  Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched parameters or per-token FLOPs. However, to the best of our knowledg…

• hyperbolix: Hyperbolic Deep Learning in JAX
  Timo Klein, Thomas Lang, Yllka Velaj, Sebastian Tschiatschek · arXiv · Sep 23, 2026

  We present hyperbolix, an open-source library for hyperbolic deep learning in JAX, built on Flax NNX. To our knowledge, it is the first comprehensive, general-purpose hyperbolic deep learning library in JAX. It includes six manifolds with a…

• Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning
  Kevin Du, Alexander Hoyle, Laura Ruis, Acyr Locatelli · arXiv · Sep 3, 2026

  Reasoning traces from chain-of-thought models appear to offer a legible window into how a model arrives at its answer. A growing body of work treats them as such, using LLM judges to diagnose errors, evaluate faithfulness, and provide step-…

• Discriminative World Models for Web Agents
  Kelvin Li, Dhruv Pendharkar, Anish Pahilajani, Chuyi Shang et al. · arXiv · Sep 2, 2026

  Recent web agents use world models for test-time action selection by sampling candidate actions, predicting the resulting web states, and ranking them with a ranker model or a Process Reward Model (PRM). These world models are typically tra…

• Cliff: Learning Process Rewards from the First Mistake
  Peixuan Han, Runhui Wang, Ketan Ramaneti, Jie Hao et al. · arXiv · Sep 2, 2026

  Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards leads to limited guidance on intermediate reasoning processes.…

• LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
  Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026

  Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…

• DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
  Jadelynn Dao, Milan Ganai, Yasmina Abukhadra, Ajay Sridhar et al. · X-Reason Poster · Aug 23, 2026

  Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency, token usa…

• DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
  RSS26-W: FM4RoboPlan Oral · Jul 8, 2026

  Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency, token usa…

• DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
  Jadelynn Dao, Milan Ganai, Yasmina Abukhadra, Ajay Sridhar et al. · RSS SemRob 2026 Poster · Jun 30, 2026

  Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency, token usa…

• Allocation, Not Volume: Test-Time Compute for Agentic Forecasting
  Atin Aboutorabi, Gaetan de Rassenfosse, Nicolas Flammarion, Maksym Andriushchenko · Forecast@ICML26 Oral · Jun 11, 2026

  Test-time compute scaling has been studied extensively in verifiable domains such as math and code; how to spend an inference budget for forecasting future events, where no test-time verifier exists, is far less studied. We compare three mu…

• ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
  Hao Wen, Yifan Su, Feifei Zhang, Yun-Xin Liu et al. · arXiv.org · Aug 30, 2025

  Recent advances in Large Language Models (LLMs) have been driven by test-time compute scaling - a strategy that improves reasoning by generating longer, sequential thought processes. While effective, this approach encounters a significant b…

• Inverse Scaling in Test-Time Compute
  Aryo Pradipta Gema, Alexander Hägele, Runjin Chen, Andy Arditi et al. · Trans. Mach. Learn. Res. · Jul 19, 2025

  We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between test-time compute and accuracy. Our evaluation tasks span four …

• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
  Mi Chen, Aonian Li, Bangwei Gong, Binyan Jiang et al. · arXiv.org · Jun 16, 2025

  We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is develo…

• Scaling Test-time Compute for LLM Agents
  King Zhu, Hanhao Li, Siwei Wu, Tianshun Xing et al. · arXiv.org · Jun 15, 2025

  Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic exploration of applying test-time scaling methods to language agents …

• e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
  Amrith Rajagopal Setlur, Matthew Y. R. Yang, C. Snell, Jeremy Greer et al. · arXiv.org · Jun 10, 2025

  Test-time scaling offers a promising path to improve LLM reasoning by utilizing more compute at inference time; however, the true promise of this paradigm lies in extrapolation (i.e., improvement in performance on hard problems as LLMs keep…

• GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning
  Jian Zhao, Runze Liu, Kaiyan Zhang, Zhi-Mu Zhou et al. · AAAI Conference on Artificial Intelligence · Apr 1, 2025

  Recent advancements in Large Language Models (LLMs) have shown that it is promising to utilize Process Reward Models (PRMs) as verifiers to enhance the performance of LLMs. However, current PRMs face three key challenges: (1) limited proces…

• Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
  Yuxiao Qu, Matthew Y. R. Yang, Amrith Rajagopal Setlur, Lewis Tunstall et al. · International Conference on Machine Learning · Mar 10, 2025

  Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or running RL with 0/1 outcome reward, but do these approaches ef…

• Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers
  Shalev Lifshitz, Sheila A. McIlraith, Yi-Lun Du · arXiv.org · Feb 27, 2025

  By utilizing more computational resources at test-time, large language models (LLMs) can improve without additional training. One common strategy uses verifiers to evaluate candidate outputs. In this work, we propose a novel scaling dimensi…

• Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
  Wenkai Yang, Shu-Ming Ma, Yankai Lin, F. Wei · Neural Information Processing Systems · Feb 25, 2025

  Recent studies have shown that making a model spend more time thinking through longer Chain of Thoughts (CoTs) enables it to gain significant improvements in complex reasoning tasks. While current researches continue to explore the benefits…

• Rank1: Test-Time Compute for Reranking in Information Retrieval
  Orion Weller, Kathryn Ricci, Eugene Yang, Andrew Yates et al. · arXiv.org · Feb 25, 2025

  We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for distillation…

• Scaling Test-Time Compute Without Verification or RL is Suboptimal
  Amrith Rajagopal Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar · International Conference on Machine Learning · Feb 17, 2025

  Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued and efficient improvements with scaling. There are largely two approaches: first, distilling suc…