AIRA
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Meta's AIRA is an agent system that autonomously discovers neural architectures, producing models that beat Llama 3.2 at 350M, 1B, and 3B scales under a 24-hour compute budget. The search is split across two specialized agents: AIRA-Compose searches macro architecture, and AIRA-Design implements the low-level mechanisms. The split outperforms a single end-to-end agent on this non-toy search problem.
Two-agent decomposition: A planner picks structure; an implementer fills in mechanisms. This pattern generalizes well beyond neural architecture search to pipeline assembly, query planning, prompt scaffolding, and tool-use programs.
Beats Llama 3.2 at three scales under budget: Discovered architectures match or exceed Llama 3.2 at 350M, 1B, and 3B parameter scales within a 24-hour compute budget for the search itself. That is competitive with months of human-led ablation studies.
Search not synthesis: The discovered models are not LLM-written code patches grafted into a framework. They are full architectures discovered through structured search guided by the two-agent loop.
Why it matters: If agentic search can produce competitive architectures end to end, then NAS and large parts of the ML research workflow become candidates for automation by agent systems rather than by hand-engineered search algorithms.
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