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@omarsar0

NEW paper from Meta. (bookmark it) It's an agent system that autonomously discovers neural architectures that beat Llama 3.2 at 350M, 1B, and 3B scales, all under a 24-hour compute budget. They get this work by splitting the search into two agents: > AIRA-Compose searches the macro architecture. > AIRA-Design implements the low-level mechanisms. For devs: If one agent in your stack is doing both strategy and implementation, split it. Run a planner that picks the structure and an implementer that fills in the mechanisms. AIRA shows this beats a single end-to-end agent on a real, non-toy search problem. The same split is useful for pipeline assembly, query planning, prompt scaffolding, and tool-use programs. Paper: https://t.co/CYALI6CFjJ Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

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