Test-time scaling (TTS) enhances LLM reasoning capabilities by sampling and aggregating diverse solution trajectories. However, existing approaches often rely on external verifiers and one-shot independent sampling, which results in inefficient budget allocation and underutilizes interim high-quality trajectories. We propose ConMA, a training-free, verifier-free TTS framework that reallocates a fixed inference budget into iterative sample–filter–diversify–select cycles: it filters answer groups based on intrinsic token-probability confidence, enriches candidates through diversity-aware expansion, and employs repeated single-choice selection for multi-stage refinement. Across multiple benchmarks, ConMA consistently improves accuracy under fixed budgets. With a maximum budget of N=64, ConMA boosts Qwen3-4B to 80% accuracy on AIME25, significantly outperforming strong baselines while converging early with only 18 samples on average, substantially reducing inference cost.

Recent advancements in the application of Large Language Models (LLMs) to formal theorem proving in Lean have unveiled new prospects in complex logical reasoning scenarios. Acknowledging that the difficulty across reasoning steps in a formal theorem proof is not uniform, prior proof search algorithms have not systematically investigated which proof steps are more crucial to explore. This paper introduces Entropy-Based Best-First Search (EBFS), an entropy-inspired heuristic tree search algorithm that enhances the efficiency of proof generation models to explore proof trees, thereby increasing the probability of finding the correct proof. We also introduce a reinforcement fine-tuning approach specifically for proof tree search. Using EBFS during the generation stage further boosts the model’s performance in proof generation. Evaluations demonstrate that our fine-tuned EBFS model achieves consistent improvements over the baseline models. Under the same sample budget, it outperforms whole proof generation by 3.2% on the ProofNet testset, with further gains of 1.9% on MiniF2F and 1.5% on ProverBench.

Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, committing to specific answers even when the queried facts are inherently volatile. To capture this challenge, we introduce ChronoRegime, a future-oriented temporal reasoning task that requires models to determine whether historical temporal evidence justifies commitment to a future answer. Furthermore, we propose Confidence-Aware Temporal Reasoning (CTR), a framework that estimates query predictability from temporal dynamics and regulates answer commitment accordingly. Rather than performing unconditional forecasting, CTR commits only when the reconstructed evidence supports the target future state. Across multiple LLMs, CTR improves strict overall accuracy by 6.7 percentage points and reduces hallucination rates by 18.4 percentage points on average over the plain QA baseline. These results demonstrate that explicit predictability assessment is an effective mechanism for safer and more reliable temporal reasoning under future uncertainty.

Work on personality detection has tended to incorporate psychological features from different personality models, such as BigFive and MBTI. There are more than 900 psychological features, each of which is helpful for personality detection. However, when used in combination, the application of different calculation standards among these features may result in interference between features calculated using distinct systems, thereby introducing noise and reducing performance. This paper adapts different psychological models in the proposed PsyAttention for personality detection, which can effectively encode psychological features, reducing their number by 85%. In experiments on the BigFive and MBTI models, PysAttention achieved average accuracy of 65.66% and 86.30%, respectively, outperforming state-of-the-art methods, indicating that it is effective at encoding psychological features.