SyncPlan: Long-Horizon LLM Coordination with Explicit Synchronization and Adaptive Correction
2026-08-03 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors address the challenge of coordinating multiple agents using large language models (LLMs) in changing environments. They note that existing methods are either slow because they repeatedly update plans or fast but inflexible because they don't adapt during execution. Their solution, SyncPlan, makes detailed plans all at once and then carefully monitors progress, allowing agents to wait or replan as needed when situations change. This approach improves success rates while being much faster than previous methods.
Large Language ModelsMulti-Agent CoordinationPlan-Execute-Correct FrameworkSynchronizationDeadlock DetectionReplanningOvercooked BenchmarkReinforcement LearningState Staleness Detection
Authors
Shen You, Xiaoming Zhu, Weining Weng, Hefei Mei, Weixuan Wang, Zhongshen Li, Zeji LI, Ye-Wen Wang, Zijun Liao, Juchao Zhuo, Yang Wei, Fuhao Qiu, Siqin Li, Zhenjie Lian, Danei Gong, Junkai Ji, Xiangtao Li, Qiuzhen Lin, Liang Wang, Ka-Chun Wong
Abstract
LLM-based multi-agent coordination faces a fundamental trade-off between efficiency and adaptivity in dynamic environments. Existing approaches typically rely on repeated LLM invocations or multi-round communication to adapt decisions during execution, introducing substantial latency and making coordination vulnerable to asynchronous progress and environmental changes. Conversely, one-shot planning reduces coordination overhead but produces open-loop plans that can quickly become stale or fail when actions depend on other agents and the environment. We introduce SyncPlan, a plan-execute-correct framework for long-horizon coordination through explicit synchronization and adaptive correction. Given the state and team-level task, a centralized LLM coordinator generates per-agent action chains in a single planning call. During execution, explicit wait primitives and deadlock detection enforce inter-agent and agent-environment dependencies, while a lightweight Plan Staleness Detector continuously assesses the remaining plan and triggers replanning when environmental changes invalidate its assumptions. We further optimize the coordinator through SFT and planning-oriented RL with dense task progress and outcome-level execution feedback. Experiments on the public Overcooked benchmark and the complex Honor of Kings environment show that SyncPlan achieves state-of-the-art task success rates while using less than 0.05% of the wall-clock runtime compared with existing LLM-based coordinators. Code and datasets will be made publicly available.