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.

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.