斯坦福论文 DeLM 提出去中心化多智能体框架，复杂编码任务速度最高达 2.49 倍
斯坦福一篇新论文提出去中心化多智能体框架 DeLM，用共享上下文和任务队列取代中央协调智能体，智能体异步认领任务并实时共享发现。
Rohan Paul· @rohanpaul_ai · X·2026-10-08 14:48· 4 小时前AI 评分55
AI 导读
斯坦福论文提出 DeLM，用共享上下文和任务队列取代中央协调智能体，智能体异步认领任务并实时共享发现。
正文
A new Stanford paper just proved that decentralized multi-agent systems can beat Claude Code and Codex on complex coding tasks, running up to 2.49× faster!
DeLM tackles a key bottleneck in multi-agent systems: time wasted repeating work and waiting on other agents.
DeLM replaces the central coordinating agent with a shared context and task queue. Agents pick up tasks independently, share discoveries as they happen, and build on each other’s progress. When one agent finds a solution or hits a dead end, the others can use that information immediately.
DeLM beats state-of-the-art in both speed and accuracy on long-horizon tasks from Terminal-Bench 4.0, DeepSWE v1.1, and ProgramBench:
- Up to 2.49× faster execution than the vanilla Claude Code and Codex baselines
- Up to +19.2 percentage points in accuracy over the vanilla Claude Code and Codex baselines
- Up to +19.9 points in ProgramBench test pass rate within the same 120-minute budget
Built on Codex and Claude Code. Code and 720 trajectories are available so you can explore how the agents collaborate! An open-source plugin lets you try DeLM directly in Codex and Claude Code.
来源：Rohan Paul · x.com
来源：RadarAI · aihot.news