Beyond Global Replanning: Hierarchical Recovery for Cross-Device Agent Systems

2026-06-18

AI Summary

This work addresses the challenge in multi-device agent systems where runtime failures are difficult to classify as either locally recoverable or requiring global replanning, leading to inefficient recovery. To overcome this, the authors propose H-RePlan, a hierarchical replanning framework that decouples local policy recovery from global task replanning. By introducing cross-layer failure abstractions and locally substitutable execution policies, H-RePlan enables scope-aware, fine-grained recovery. The framework supports a unified API-CLI-GUI execution environment and is accompanied by HeraBench, a fault-injection benchmark for systematic evaluation. Experimental results demonstrate that H-RePlan significantly outperforms baseline methods, improving task completion rate, instruction adherence, and perfect pass rate while reducing the token overhead required for reliable execution.

Abstract

Real-world computer-use tasks often span multiple applications and devices, requiring agents to coordinate heterogeneous environments under dynamic runtime failures. Existing multi-device agent systems support task decomposition and cross-device assignment, but recovery remains largely coarse-grained: when execution fails, they typically retry the same strategy, reassign the subtask, or revise the global plan, without systematically modeling the device-local strategy space. This limits their ability to distinguish failures that can be repaired within the current device from those that require cross-device replanning. We propose \textbf{H-RePlan}, a hierarchical replanning framework for multi-device agents with unified API--CLI--GUI execution. H-RePlan equips each device with interchangeable execution strategies and separates device-local strategy recovery from orchestrator-level global replanning through a compact cross-layer failure abstraction. To evaluate this capability, we introduce \textbf{HeraBench}, a fault-injected benchmark that constructs cross-device workflows over Linux and Android devices and injects strategy- and device-level failures. Experiments show that H-RePlan substantially outperforms single-strategy and coarse-grained multi-device baselines, achieving higher completion, instruction adherence, and perfect-pass rates while reducing the token cost required for reliable end-to-end success. These results demonstrate that scope-aware hierarchical recovery is essential for robust multi-device agent execution.

Problem

• multi-device agent
• runtime failure
• recovery
• cross-device planning
• hierarchical replanning

Innovation

• hierarchical replanning
• multi-device agents
• failure abstraction
• strategy recovery
• cross-device coordination