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class="article-meta-label">发表于</span><time datetime="2026-03-08T07:42:28.000Z" title="发表于 2026-03-08 15:42:28">2026-03-08</time></span></div><div class="content">系统拆解 AlphaGeometry DSL 的问题格式、defs.txt action 定义、predicate 语义、rules.txt 推理规则与构造流程，适合做几何求解器、数据生成与 AlphaGeometry 复现。</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" target="_blank" rel="noopener" href="https://luhuidev.com/zh-cn/essays/alphageometry2-google-ai-solve-imo-geometry-problems" title="AlphaGeometry2 深度解析：Google AI 如何解决 IMO 几何题？">AlphaGeometry2 深度解析：Google AI 如何解决 IMO 几何题？</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2026-03-06T12:19:12.000Z" title="发表于 2026-03-06 20:19:12">2026-03-06</time></span></div><div class="content">拆解 AlphaGeometry2 如何把几何推理、辅助线搜索与符号证明结合起来解决 IMO 几何题，并总结对数学 AI 工程的启发。</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2026/03/03/202602/" title="2026/02 Review"><img class="post-bg" src="https://liluhui.oss-cn-hangzhou.aliyuncs.com/assets/imgs/2026/03/03/20260303174321_672_425.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="2026/02 Review"></a></div><div class="recent-post-info"><a class="article-title" href="/2026/03/03/202602/" title="2026/02 Review">2026/02 Review</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2026-03-03T13:17:41.000Z" title="发表于 2026-03-03 21:17:41">2026-03-03</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%94%9F%E6%B4%BB/">生活</a></span></div><div class="content">Demo, Don't Memo</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" target="_blank" rel="noopener" href="https://luhuidev.com/zh-cn/essays/google-deepmind-aletheia-autonomous-math-research-agent" title="Google DeepMind Aletheia：完全自主研究的数学 Agent 解读">Google DeepMind Aletheia：完全自主研究的数学 Agent 解读</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2026-02-25T10:01:14.000Z" title="发表于 2026-02-25 18:01:14">2026-02-25</time></span></div><div class="content">前言Google DeepMind Aletheia 在 IMO-ProofBench Advanced 数据集中以 ~91.9% 成绩遥遥领先。 针对美国数学奥林匹克 2025 难题表现也远超基线系统。在内部更难的 benchmark 上表现超过旧版推理模型，虽仍有差异但已领先过去基线。 最近关于 Aletheia 的讨论，有点熟悉的味道。 标题里写着“AI 数学家”，评论区在问“是不是要取代数学家了？是不是已经能自动搞科研了？” 我认真研究了下 Aletheia 的论文和数据集，把我学习到的关键架构和落地价值做了梳理，也正是本篇文章的内容。 一、DeepMind Aletheia 的来时路把时间线拉长看，会发现 Google DeepMind 在这个方向上已经蓄力很久了。 在 2016 年推出 AlphaGo，就已经开始研究一个问题：如何在一个规则完备、评价函数明确的系统里，优化决策路径？ 棋盘是离散的，胜负可判定，搜索空间巨大但结构清晰，那是一种理想的策略优化环境。 DeepMind 那套“神经网络 +...</div></div></div><div class="recent-post-item"><div 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