Simulating Human Cognition: Heartbeat-Driven Autonomous Thinking Activity Scheduling for LLM-based AI systems

arXiv cs.AI / 4/17/2026

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Key Points

The paper argues that current LLM-agent frameworks often use rigid, reactive control flows, leading to inefficient or impulsive behavior and late correction.
It proposes Heartbeat-Driven Autonomous Thinking Activity Scheduling, a periodic “heartbeat” mechanism that proactively orchestrates cognitive modules such as Planner, Critic, Recaller, and Dreamer.
The scheduler learns when to trigger specific thinking activities (e.g., memory recall, experience summarization, strategic planning) using temporal patterns and historical context rather than hard-coded rules.
The approach supports dynamically adding or removing cognitive modules without restructuring the system, and it includes a meta-learning strategy to continually adapt the scheduling policy from interaction logs.
Experiments reportedly show the method can learn scheduling policies from historical data and autonomously integrate newly introduced thinking modules.

Abstract

Large Language Model (LLM) agents have demonstrated remarkable capabilities in reasoning and tool use, yet they often suffer from rigid, reactive control flows that limit their adaptability and efficiency. Most existing frameworks rely on fixed pipelines or failure-triggered reflection, causing agents to act impulsively or correct errors only after they occur. In this paper, we introduce Heartbeat-Driven Autonomous Thinking Activity Scheduling, a mechanism that enables proactive, adaptive, and continuous self-regulation. Mirroring the natural rhythm of human cognition, our system employs a periodic ``heartbeat'' mechanism to orchestrate a dynamic repertoire of cognitive modules (e.g., Planner, Critic, Recaller, Dreamer). Unlike traditional approaches that rely on hard-coded symbolic rules or immediate reactive triggers, our scheduler learns to determine when to engage specific thinking activities -- such as recalling memories, summarizing experiences, or strategic planning -- based on temporal patterns and historical context. This functional approach allows cognitive modules to be dynamically added or removed without structural reengineering. Meanwhile, we propose a meta-learning strategy for continual policy adaptation, where the scheduler optimizes its cognitive strategy over time using historical interaction logs. Evaluation results demonstrate that our approach effectively learns to schedule cognitive activities based on historical data and can autonomously integrate new thinking modules.