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The ARC Prize Foundation has launched SnakeBench, an experimental benchmark that pits 50 large language models (LLMs) against each other in head-to-head snake games. In a total of 2,800 matches, the benchmark evaluates the models' decision-making and adaptability in real-time strategy and spatial reasoning. Notably, models such as o1, o3-mini, sonnet, and Gemini 2.0 performed well, with DeepSeek achieving surprising results. The initiative reflects a growing interest in competitive AI, suggesting a future where AI matches could be as widely viewed as human sports events.
AI Agents are taking the lead in AI. 💡 These autonomous systems plan, act, & adapt, transforming industries from finance to gaming. But there’s a catch… centralized Agents come with risks: bias, data silos, & single points of failure. So, how does Talus improve on this? 🧵👇 https://t.co/YTdmG1as0J
Introducing... Agent Leaderboard! Many devs ask me which LLMs work best for AI agents. The new Agent Leaderboard (by @rungalileo) was built to provide insights and evaluate LLMs on real-world tool-calling tasks—crucial for building AI agents. Let's go over the results: https://t.co/167XXRdBg2
New benchmark from @arcprize - SnakeBench! https://t.co/ff7UBerOKG
