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On Thursday, the Laude Institute announced its first batch of Slingshots grants<\/a>, aimed at \u201cadvancing the science and practice of artificial intelligence.\u201d<\/p>\n Designed as an accelerator for researchers, the Slingshots program is meant to provide resources that would be unavailable in most academic settings, whether it\u2019s funding, compute power, or product and engineering support. In exchange, the recipients pledge to produce some final work product, whether it\u2019s a startup, an open source codebase, or another type of artifact.<\/p>\n 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<\/a> and the latest version of the long-running ARC-AGI project<\/a>.<\/p>\n 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\u2019 ability to optimize existing code, while the Columbia-based BizBench proposes a comprehensive benchmark for \u201cwhite-collar AI agents.\u201d Other grants explore new structures for reinforcement learning or model compression.<\/p>\n