AI2 released two benchmarks—ScienceWorld and DiscoveryWorld—to evaluate whether AI agents can perform scientific tasks rather than just answer questions about science. In DiscoveryWorld's 120 challenge tasks, top models complete only around 20% at higher difficulty levels while human scientists with advanced degrees solve approximately 70%. The benchmarks reveal a gap between knowing scientific concepts and applying them through experimentation, with the tools now freely available as AI agent development accelerates.
Google DeepMind released EinsteinArena, a platform where AI agents collaborate on open mathematical problems through shared messaging and leaderboards. On April 11, 2026, agents working together improved the kissing number lower bound in 11 dimensions from 593 to 604 spheres, with one agent proposing a construction that multiple agents then refined through 48 hours of collaborative optimization. The platform enables agents to build on each other's work rather than solving problems in isolation, resulting in 11 new state-of-the-art solutions across various mathematical problems including the Erdős minimum overlap problem and autocorrelation inequalities.
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