这篇把4个Claude Code用AgentRadio串起来，SWE-Atlas上干到62.1%，比单跑Opus 4.8还高。搞多智能体协作的值得看看。
AgentRadio提出线程、消息、等待提及三种原语，用异步消息传递取代阶段式交接。在SWE-Atlas QnA基准上，单个Claude Code agent（Opus 4.6）解决32.3%任务，四个通过AgentRadio协作的agent解决62.1%，超过单个Opus 4.8的57.2%。按难度分层分析显示增益随任务难度增长，说明收益主要来自中途修正而非并行。论文见arxiv.org/abs/2607.28430。
Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard mul...
Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard multi-agent work. The usual design lets them exchange findings only at phase boundaries, through staged handoffs or synchronized rounds, so communication and work stay mutually exclusive. AgentRadio breaks that constraint with an asynchronous message-passing layer. Three primitives, threads, messages, and waiting for mentions. The last one runs as a background task, surfacing a teammate's discovery without interrupting foreground work. On SWE-Atlas QnA, a single Claude Code agent on Opus 4.6 resolves 32.3% of tasks. Four agents wired through AgentRadio resolve 62.1%. That also beats a single Claude Code agent running the newer Opus 4.8 at 57.2%. Rubric-level analysis shows the gain growing with task difficulty, which points at mid-course correction as the mechanism rather than raw parallelism. Paper: arxiv.org/abs/2607.28430 Track more trending AI papers in our academy: academy.dair.ai 💬 5 🔄 4 ❤️ 4 👀 1587 📊 7 ⚡