做架构搜索或智能体系统的开发者值得一看——AIRA 用双智能体分工策略解决了搜索效率问题，而且思路可以迁移到管道组装、查询规划等场景，直接参考论文实现。
Meta 提出 AIRA 智能体系统，通过两个分工明确的智能体（AIRA-Compose 负责宏观架构搜索，AIRA-Design 负责底层机制实现），在 24 小时计算预算内自主发现了超越 Llama 3.2 的神经网络架构（350M、1B、3B 规模）。该方法将策略制定与实现分离，相比单端到端智能体在真实搜索任务上表现更优。论文已公开，对架构搜索和智能体设计有重要参考价值。
NEW paper from Meta. (bookmark it) It's an agent system that autonomously discovers neural archite...
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: arxiv.org/abs/2605.15871 Learn to build effective AI agents in our academy: academy.dair.ai 💬 11 🔄 20 ❤️ 96 👀 10073 📊 37 ⚡