Stockfish 19
Stockfish 19

原始链接: https://stockfishchess.org/blog/2026/stockfish-19/

Stockfish 19 现已发布,相比前代版本性能提升高达 44 Elo。此次更新引入了 SFNNv16 神经网络架构,在增强对局强度的同时减小了二进制文件体积,并弃用了辅助神经网络,从而提升了在复杂局面下的表现。 主要亮点包括: * **通用二进制文件:** 新的二进制文件可自动检测 CPU 特性,无需手动配置。 * **扩展的兼容性:** 增加了对 RISC-V、LoongArch、WebAssembly 及 1GB Linux 大页内存(huge pages)的原生支持。 * **技术改进:** 引擎现在具有更严格的棋盘局面验证,并重构了共享内存实现,以提高稳定性。 Stockfish 依然是一个免费的、由社区驱动的开源项目。鼓励用户通过 Fishtest 贡献算力、参与 Discord 社区,或在 GitHub 上协助开发。该引擎现可从 stockfishchess.org/download 立即下载。

Hacker News 最新 | 往日 | 评论 | 提问 | 展示 | 工作 | 提交 登录 Stockfish 19 (stockfishchess.org) 13 分 | atiedebee | 1 小时前 | 隐藏 | 往日 | 收藏 | 3 条评论 帮助 piker | 6 分钟前 | [–] 提问:Stockfish 能击败 AlphaZero 这类通用系统吗? 回复 tumdum_ | 1 分钟前 | 父节点 | 下一个 | [–] 我想可以,请看锦标赛结果:https://en.wikipedia.org/wiki/Top_Chess_Engine_Championship#... 回复 mattr03 | 0 分钟前 | 父节点 | 上一个 | [–] 是的,而且已经领先很长一段时间了。在 AlphaZero 展示了神经网络的可行性后,Stockfish 也采用了这种方法,现在的现代国际象棋引擎都是如此。 回复 指南 | 常见问题 | 列表 | API | 安全 | 法律 | 申请 YC | 联系 搜索:
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原文

Today, we have the pleasure of announcing Stockfish 19, a new major release. As always, you can freely download it at stockfishchess.org/download and use it as a drop-in replacement in the GUI of your choice to benefit from stronger play and more accurate analysis.

Whether you can spare hours or days of CPU time, your help matters for the ongoing development of Stockfish. Find out how you can contribute at stockfishchess.org/get-involved. Join our Discord server to get in touch with the community of developers and users of the project!

Quality of Chess Play

In tests against Stockfish 18, this new release brings an Elo gain of up to 44 points, and wins more than three times as many game pairs as it loses.

Stockfish continues to set the standard for engine strength. Against the strongest competition, it consistently secures the top spot in engine championships, continuing to dominate the field.

Update Highlights

Universal Binaries

We have transitioned to universal binaries for our releases, simplifying the download process. These universal binaries automatically detect the features of your CPU and run the optimal code, eliminating the need to manually choose between AVX2, AVX-512, etc.

Upgraded NNUE Architecture and Training

This release introduces the SFNNv16 network architecture, reducing binary size by removing redundant threat features while increasing strength by introducing new pawn-pair features. The secondary neural network, introduced in Stockfish 16.1, has been retired, enhancing strength in positions where the small net previously underperformed.

The training process has been further improved with the introduction of new techniques, such as Quantization-Aware Training (QAT), and further parameter tweaks. These techniques have been applied to hundreds of billions of training positions, all of which have been consistently rescored using a strong Leela net.

Expanded Platform Support

We have added native support for RISC-V (RVV) and LoongArch (LSX/LASX), 1GB Linux huge pages, as well as WebAssembly targets. The shared-memory implementation for Linux, macOS, and BSD was also overhauled.

Strict Position Validation

We have implemented stricter validation for board positions, FEN strings, and UCI commands. The engine will now output an info string CRITICAL ERROR followed by the exact command and the reason it failed, and then immediately terminate the process. A good GUI will ensure you never encounter these errors.

Thank You

The Stockfish project builds on a thriving community of enthusiasts (thanks to everybody!) who contribute their expertise, time, and resources to build a free and open-source chess engine that is robust, widely available, and very strong.

We would like to express our gratitude for the 16.4k stars that light up our GitHub project. Thank you for your support and encouragement – your recognition means a lot to us. Programmers can contribute to the project either directly to Stockfish (C++), to Fishtest (HTML, CSS, JavaScript, and Python), to our trainer nnue-pytorch (C++ and Python), or to our website (HTML, CSS/SCSS, and JavaScript).

The Stockfish team

联系我们 contact @ memedata.com