aiqbench analysis
SyncPlan addresses the critical latency bottleneck in multi-agent LLM systems by introducing explicit synchronization primitives and a lightweight staleness detector to enable efficient long-horizon coordination without sacrificing adaptivity. By optimizing the central coordinator through supervised fine-tuning and reinforcement learning, this framework achieves state-of-the-art success rates on complex benchmarks like Overcooked while reducing wall-clock runtime usage to under 0.05% of existing methods. This work represents a significant advancement in making autonomous agent teams viable for real-time dynamic environments where traditional repeated invocation strategies are too slow or brittle.
SyncPlan: Long-Horizon LLM Coordination with Explicit Synchronization and Adaptive Correction
arXiv:2608.01652v1 Announce Type: cross 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2608.01652 [cs.RO]
(or arXiv:2608.01652v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.01652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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