内容来源：https://techcrunch.com/2025/11/06/laude-institute-announces-first-batch-of-slingshots-ai-grants/

内容总结：

近日，劳德研究所正式启动首批"弹射计划"资助项目，聚焦人工智能领域的科研创新与实践应用。该计划旨在为研究人员提供学术环境中难以获取的专项支持，包括资金投入、算力资源及产品工程辅助，被资助者需承诺产出具体成果，如初创企业、开源代码库或其他技术成果。

首期入选的15个项目中，人工智能评估技术成为重点攻关方向。除业界熟知的终端命令行编码基准测试平台Terminal Bench和长期研发的ARC-AGI项目升级版外，多个创新方案引人注目：加州理工学院与德克萨斯大学联合开发的Formula Code致力于评估AI代理的代码优化能力，哥伦比亚大学的BizBench项目则着力构建"白领AI代理"综合评估体系。另有项目专注强化学习新框架与模型压缩技术的研究。

值得关注的是，SWE-Bench联合创始人约翰·博达·杨领衔的CodeClash项目，借鉴SWE-Bench成功经验，开创性地采用动态竞技评估模式测试代码质量。杨表示："坚持使用第三方核心基准测试确实推动着行业进步，我担心未来评估标准会沦为企业的私有工具。"这一观点折射出学术界对AI评估体系标准化建设的共同关切。

中文翻译：

周四，劳德研究所公布了首批"弹射计划"资助项目，该计划旨在"推动人工智能科学与实践的发展"。作为研究加速器，该计划致力于提供学术机构难以获取的资源，包括资金支持、算力资源及产品工程支持。作为回报，受资助者需承诺产出具体成果，无论是初创企业、开源代码库还是其他形式的成果。

首期资助的15个项目重点关注人工智能评估这一难题。其中部分项目已为TechCrunch读者所熟知，包括终端命令行编码基准测试Terminal Bench，以及长期开展的ARC-AGI项目最新版本。另有项目对经典评估课题提出创新解法：由加州理工学院和德克萨斯大学奥斯汀分校研究人员开发的Formula Code旨在评估AI代理优化现有代码的能力，哥伦比亚大学的BizBench则提出了针对"白领AI代理"的综合评估体系。其他资助项目还涉及强化学习新框架与模型压缩等方向。

SWE-Bench联合创始人John Boda Yang也入选本期名单，他领导的新项目CodeClash受到SWE-Bench成功启发，将通过动态竞技框架评估代码能力。杨向TechCrunch表示："我坚信持续基于第三方核心基准开展评估能推动进步。但有些担忧未来基准测试会完全被企业定制标准所主导。"

英文来源：

On Thursday, the Laude Institute announced its first batch of Slingshots grants, aimed at “advancing the science and practice of artificial intelligence.”
Designed as an accelerator for researchers, the Slingshots program is meant to provide resources that would be unavailable in most academic settings, whether it’s funding, compute power, or product and engineering support. In exchange, the recipients pledge to produce some final work product, whether it’s a startup, an open source codebase, or another type of artifact.
The initial cohort is 15 projects, with a particular focus on the difficult problem of AI evaluation. Some of those projects will be familiar to TechCrunch readers, including the command-line coding benchmark Terminal Bench and the latest version of the long-running ARC-AGI project.
Others take a fresh approach to a long-established evaluation problem. Formula Code, built by researchers at Caltech and UT Austin, aims to produce an evaluation of AI agents’ ability to optimize existing code, while the Columbia-based BizBench proposes a comprehensive benchmark for “white-collar AI agents.” Other grants explore new structures for reinforcement learning or model compression.
SWE-Bench co-founder John Boda Yang is also part of the cohort, as leader of the new CodeClash project. Inspired by the success of SWE-Bench, CodeClash will assess code through a dynamic competition-based framework, which Yang hopes will
“I do think people continuing to evaluate on core third-party benchmarks drives progress,” Yang told TechCrunch. “I’m a little bit worried about a future where benchmarks just become specific to companies.”