elvis@omarsar0 · XMeta提出AIRA系统，通过分离策略与实现的双代理架构，实现神经架构的自主发现。AIRA-Compose负责宏观架构搜索，AIRA-Design专注低级机制实现。该系统在24小时计算预算内，于350M、1B和3B规模上找到超越Llama 3.2的架构。其核心方法论表明，在复杂任务中分离规划代理与实现代理能提升效能，此思路同样适用于流水线组装、查询规划等其他AI代理场景。
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.