HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents
2026-08-17 • Artificial Intelligence
Artificial IntelligenceRobotics
AI summaryⓘ
The authors study how AI agents handle long tasks by breaking down goals and making plans using language models, but notice that repeatedly asking the model to decide small final actions wastes time and computing power. They introduce HaReCAP, which learns common final actions from past successes and turns them into simple rules that can be applied quickly without calling the model every time. This method saves computing resources while keeping the original planning style intact. They tested HaReCAP in robot and virtual environments and found it significantly reduces token usage compared to the original method.
Long-horizon tasksLLM (Large Language Model)Task decompositionPlan groundingReCAPLast-mile groundingToken consumptionRobotic task planningLeaf-reflex rulesRecursive context management
Authors
Shen Liu, Zhenguo Xu, Shaopu Wang, Yike Gao, Chunlei Wang
Abstract
Long-horizon embodied tasks require LLM agents to iteratively decompose high-level goals, revise plans in response to environmental feedback, and ground leaf-level subgoals into valid executable actions. Recursive context-management methods such as ReCAP improve planning stability through multi-level task decomposition and parent-node refinement, but still repeatedly invoke the LLM at leaf nodes to ground atomic subtasks into exact valid actions. We refer to this final grounding step as last-mile grounding redundancy, which accumulates into substantial LLM-call and token overhead during long-horizon execution. To mitigate this issue, we propose HaReCAP (Habitual-action Grounded ReCAP), a low-intrusion leaf grounding extension for ReCAP. HaReCAP extracts frequent leaf decisions from successful trajectories and compiles them offline into auditable and abstainable one-step leaf-reflex rules. At runtime, it skips the leaf LLM call only when a rule can uniquely determine a legal action in the current valid-action set; otherwise, it falls back to the original ReCAP. This design avoids repeatedly carrying the full recursive context into the LLM for routine leaf action grounding, while preserving the original recursive control flow. We evaluate HaReCAP on Robotouille and ALFWorld with Qwen3.5-27B as the main model. On tasks solved by both ReCAP and HaReCAP, HaReCAP reduces token consumption by 14.67%, 17.93%, and 20.08% on Robotouille synchronous, Robotouille asynchronous, and ALFWorld, respectively. The results show that HaReCAP can serve as a low-intrusion extension to ReCAP-style recursive context-management frameworks, reducing last-mile grounding redundancy across environments and models on commonly successful trajectories.