Meta 发布 AIRA：双智能体自主搜索神经网络架构，超越 Llama 3.2
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 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📊 37 ⚡
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Meta, AIRA, 神经网络架构搜索, 智能体.
Source attribution: curated from elvis (@omarsar0).
