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文章背景与核心概要

并发多智能体编程旨在通过分工、冗余和跨多个文件的并行探索来改进软件生成。然而，传统系统受到大语言模型（LLM）逐词（token-by-token）生成限制的瓶颈制约，迫使智能体要么通过阶段交接串行运行，要么在缺乏适当协调的孤立池中运行。因此，单个智能体往往会失败或放弃复杂的任务。

本文介绍了 AgentRoom，这是一种专门为并发编程智能体设计的实时协作编辑协议。通过在基于冲突无关复制数据类型（CRDT）合并的共享文件系统之上，将文件级别的声明、状态和广播公开为模型上下文协议（MCP）工具，AgentRoom成功协调了多智能体工作流。在Python DevBench和Rust+axum的任务中，对五个前沿编程CLI模型进行的评估表明，协调——而不是单纯的并行性或CRDT合并——才是提高任务完成率和可靠性的关键驱动力。

AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace

Authors: Seonglae Cho, Donghyun Lee
Submitted on: 24 August 2026
Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv: 2608.23740 [cs.AI] | DOI: 10.48550/arXiv.2608.23740

Authors: Seonglae Cho, Donghyun Lee
Submitted on: 24 August 2026
Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv: 2608.23740 [cs.AI] | DOI: 10.48550/arXiv.2608.23740

📌 Summary

Concurrent multi-agent coding aims to improve software generation through division of labor, redundancy, and parallel exploration across multiple files. However, traditional systems are bottlenecked by the token-by-token generation limits of Large Language Models (LLMs), forcing agents to run either sequentially through phase handoffs or in isolated pools without proper coordination. As a result, single agents often fail or abandon complex tasks.

This paper introduces AgentRoom, a real-time collaborative editing protocol designed specifically for concurrent coding agents. By exposing file-level claims, statuses, and broadcasting as Model Context Protocol (MCP) tools on top of a Conflict-free Replicated Data Type (CRDT)–merged shared filesystem, AgentRoom successfully coordinates multi-agent workflows. Evaluations across five frontier coding-CLI models on tasks using Python DevBench and Rust+axum demonstrate that coordination—rather than raw parallelism or CRDT-merging alone—is the key driver of improved task completion and reliability.

📌 Summary

Concurrent multi-agent coding aims to improve software generation through division of labor, redundancy, and parallel exploration across multiple files. However, traditional systems are bottlenecked by the token-by-token generation limits of Large Language Models (LLMs), forcing agents to run either sequentially through phase handoffs or in isolated pools without proper coordination. As a result, single agents often fail or abandon complex tasks.

This paper introduces AgentRoom, a real-time collaborative editing protocol designed specifically for concurrent coding agents. By exposing file-level claims, statuses, and broadcasting as Model Context Protocol (MCP) tools on top of a Conflict-free Replicated Data Type (CRDT)–merged shared filesystem, AgentRoom successfully coordinates multi-agent workflows. Evaluations across five frontier coding-CLI models on tasks using Python DevBench and Rust+axum demonstrate that coordination—rather than raw parallelism or CRDT-merging alone—is the key driver of improved task completion and reliability.

🧭 Abstract

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.

🧭 Abstract

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.