本研究对比极小极大博弈中的深度优先算法 AB 和最佳优先算法 SSS，理论证明 SSS 实为 AB 的特例，实证推翻了 SSS 复杂、耗内存且更高效的普遍观点。
摘要：
搜索算法通常根据其节点扩展策略进行分类。一种选择是深度优先策略，这是一种简单的回溯策略，按照生成后继节点的顺序遍历搜索空间。另一种选择是最佳优先策略，其设计旨在使利用特定领域的启发式信息成为可能。通过优先探索搜索空间中有前景的部分，最佳优先算法通常比深度优先算法更高效。在诸如国际象棋和跳棋等极小极大博弈程序中，搜索的效率至关重要。鉴于最佳优先算法在其他领域的成功，人们期望它们也能用于极小极大博弈。然而，所有高性能的博弈程序都基于深度优先算法。本研究深入探讨了深度优先算法 AB 和最佳优先算法 SSS。关于这些算法的普遍观点是，SSS 提供了更高效搜索的潜力，但其复杂的公式表述和指数级的内存需求使其不切实际。这项工作的理论部分表明，这两种算法之间存在着惊人直接的联系——就所有实际目的而言，SSS 是 AB 的一个特例。随后的实证证据证明关于 SSS 的普遍观点是错误的：它并不是一个复杂的算法，它不需要太多内存，而且也不比深度优先搜索更高效。
英文摘要：
Search algorithms are often categorized by their node expansion strategy. One option is the depth-first strategy, a simple backtracking strategy that traverses the search space in the order in which successor nodes are generated. An alternative is the best-first strategy, which was designed to make it possible to use domain-specific heuristic information. By exploring promising parts of the search space first, best-first algorithms are usually more efficient than depth-first algorithms.
In programs that play minimax games such as chess and checkers, the efficiency of the search is of crucial importance. Given the success of best-first algorithms in other domains, one would expect them to be used for minimax games too. However, all high-performance game-playing programs are based on a depth-first algorithm.
This study takes a closer look at a depth-first algorithm, AB, and a best-first algorithm, SSS. The prevailing opinion on these algorithms is that SSS offers the potential for a more efficient search, but that its complicated formulation and exponential memory requirements render it impractical. The theoretical part of this work shows that there is a surprisingly straightforward link between the two algorithms -- for all practical purposes, SSS is a special case of AB. Subsequent empirical evidence proves the prevailing opinion on SSS to be wrong: it is not a complicated algorithm, it does not need too much memory, and it is also not more efficient than depth-first search.
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
Baichuan Inc.（北川公司）；University of Chinese Academy of Sciences（中国科学院大学）；Tongji University（同济大学）；The University of Edinburgh（爱丁堡大学）；Zhejiang University（浙江大学）
ReSearch 提出通过强化学习训练 LLMs 将搜索操作融入推理链，无需监督推理步骤，在 Qwen2.5 系列模型上训练后展现出跨基准的强泛化能力，并自然涌现反思与自我纠错能力。
DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification
School of Computer Science and Software Engineering, Shanghai University（上海大学计算机科学与软件工程学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research
Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee（田纳西大学）
