Meta FAIR researchers release paper on AIRA-Compose and AIRA-Design, a two-agent system that autonomously discovers neural architectures beyond Transformers and surpasses Llama 3.2 at 350M, 1B, and 3B scales

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://arxiv.org/abs/2605.15871 Learn to build effective AI agents in our academy: https://academy.dair.ai/