AgentRoom：基于CRDT的共享工作区中的并发多智能体编码

摘要

并发多智能体编码有望在模块间实现分工、通过冗余提升鲁棒性，并在多文件项目的自然粒度上进行并行探索。实时协作编辑协议通过无冲突复制数据类型（CRDTs）解决了人类团队的协调问题，但底层的大语言模型逐词元生成输出，现有的大语言模型多智能体编码系统继承了这一串行限制：它们要么通过阶段交接来序列化智能体，要么无协调地汇聚独立样本，而单个智能体在面对高达一半的困难任务时会放弃，仅生成一个文件的存根后退出。AgentRoom是一种面向并发编码智能体的实时协作编辑协议。其运行时层将文件级声明、状态和广播作为MCP工具暴露在CRDT合并的共享文件系统上。五个前沿编码CLI模型在四个后端编码任务上进行了测试，并在Python DevBench和Rust+axum上进行了跨语言检查。对于CLI稳定的模型，使用2个智能体的AgentRoom比 Solo 放弃的任务更少，且运行间变异更小。在算力匹配的条件下，一个正向的LLM评测均值对比显示AgentRoom优于并行合并。另一项对比（捆绑探针）表明，完整的AgentRoom优于每种部分配置：这是一个排序问题，而非百分比分配问题。承担主要作用的是协调，而非并行性或CRDT合并。

English

Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols solve this coordination problem for human teams via Conflict-free Replicated Data Types (CRDTs), but the LLMs underneath generate one token at a time and existing multi-agent coding systems inherit this serial limit: they either sequence agents through phase handoffs or pool independent samples without coordination, and a single agent abandons up to half of hard tasks with a one-file stub-and-exit. AgentRoom is a realtime collaborative editing protocol for concurrent coding agents. Its runtime layer exposes file-level claim, status, and broadcast as MCP tools on a CRDT-merged shared filesystem. Five frontier coding-CLI models ran four backend coding tasks, with cross-language checks in Python DevBench and Rust+axum. For CLI-stable models, AgentRoom with 2 agents abandons fewer tasks than Solo and has less run-to-run variation. At matched-compute, one positive mean LLM-judge contrast puts AgentRoom over parallel-merge. The other contrast, a bundle probe, puts full AgentRoom above each partial case: an ordering rather than a percentage split. Coordination, not parallelism or CRDT-merge, bears the load.