ContextFlow: Hierarchical Task-State Alignment for Long-Horizon Embodied Agents

2026-05-18

AI Summary

This work addresses decision failures in long-horizon embodied agents caused by task-state inconsistencies—such as phase locking and actuator-context mismatches—by introducing an auditable task-state alignment framework. The approach models task phases as explicit contracts and generates runtime evidence bundles through hierarchical task representations. Alignment of the task frontier is achieved via scoped, local update operations: continue, refine, shift, elevate, and repair. This is the first framework to integrate contract-based phase modeling with evidence-driven mechanisms, ensuring task consistency under multi-actuator coordination. Experimental results demonstrate that the method effectively diagnoses and mitigates state misalignments, substantially reducing unnecessary replanning while enhancing system robustness and interpretability.

Abstract

Long-horizon embodied agents increasingly delegate navigation, search, approach, and manipulation to specialist executors. As these executors become stronger, the main bottleneck shifts from local skill execution to maintaining a coherent task frontier across planning, monitoring, memory, and execution. We study task-state misalignment, a task-level consistency failure in which the planner's active stage, runtime evidence, remembered context, and delegated executor no longer justify the same next-step decision. This failure can lead to unsupported handoffs, stage lock, executor-context mismatch, and unnecessary replanning. We propose ContextFlow, an inspectable alignment framework that represents stages as explicit contracts, converts runtime observations into evidence packets, and applies scoped updates including continue, refine, transfer, promote, and repair. ContextFlow keeps specialist executors responsible for local closed-loop control while making task-frontier alignment explicit and auditable. Experiments and demonstration traces on long-horizon embodied tasks illustrate how evidence-grounded scoped updates diagnose and mitigate recurring task-state failures.

Problem

• task-state misalignment
• long-horizon embodied agents
• task frontier
• executor-context mismatch
• coherent task execution

Innovation

• ContextFlow
• task-state alignment
• evidence packets
• scoped updates
• embodied agents