The paper introduces Uncertainty-Aware Budget Allocation (UAB), a concave integer optimization framework that reallocates a fixed sampling budget for language model reasoning based on uncertainty estimated from initial samples. In Phase‑1, each question receives a small number of generations, and the disagreement among these answers (vote entropy) signals difficulty. Phase‑2 uses a marginal‑greedy algorithm to allocate the remaining budget, focusing on questions with high initial disagreement, leading to a 2.3% average accuracy improvement over uniform allocation across five open‑weight models and five reasoning benchmarks, with up to 5.5% gains on individual benchmarks, especially in low‑resource settings.
By Manh Nguyen, Sunil Gupta, Hung Le
arXiv:2602. 09574v2 Announce Type: replace-cross Abstract: Tree-search decoding is an effective form of test-time scaling for large language models (LLMs), but real-world deployment often imposes a fixed per-query token budget that varies across settings.
By Sora Miyamoto, Daisuke Oba, Naoaki Okazaki
The paper introduces LB-MCTS, a trajectory-structured optimization framework that combines Monte Carlo Tree Search, Bayesian Optimization, and Large Language Models to address the CASH problem in AutoML. It uses a shared tree state for algorithm selection, hyperparameter refinement, and synergy between BO and LLM proposals, with a reliability-aware policy that shifts from LLM-driven to BO-driven suggestions as the surrogate model improves. Experiments on 104 AMLB datasets show that LB-MCTS consistently outperforms existing BO-based, LLM-based, and hybrid baselines.
By Beicheng Xu, Weitong Qian, Lingching Tung, Yupeng Lu, Bin Cui
The paper introduces ExTS, a tree‑search policy designed for budget‑constrained agentic search where evaluation and generation costs are high. ExTS treats expansion as a value‑of‑information decision, combining discriminative reward shaping, a stochastic virtual child, and quality‑conditioned branching to allocate budget more effectively. Experiments on prompt optimization, code generation, molecular structure elucidation, and agentic workflow optimization show ExTS matching or surpassing task‑specific baselines with an average gain of +5.5% using a single configuration, and the authors also present pilot‑run diagnostics to guide adaptation to different problem structures.
By Haoyang Fang, Bernie Wang
arXiv:2604. 05164v3 Announce Type: replace-cross Abstract: As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries.
By Neharika Jali, Anupam Nayak, Gauri Joshi
arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.
By Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi