连接符号控制与神经推理的LLM代理——结构认知循环
Bridging Symbolic Control and Neural Reasoning in LLM Agents -- The Structured Cognitive Loop
- JEI University（JEI大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出结构认知循环(SCL)架构，通过分离检索、认知、控制、行动和记忆模块，解决LLM代理的推理与执行纠缠、记忆波动和行动序列失控问题，实现零策略违规、防止冗余调用并保持决策可追溯性。
AI中文摘要：
大型语言模型代理存在架构脆弱性，如纠缠的推理与执行、记忆波动和不受控的动作序列。我们引入结构认知循环(SCL)，一种模块化代理架构，将认知分为检索、认知、控制、行动和记忆(R-CCAM)。SCL通过引入监管层，通过软符号控制对概率推断施加符号约束，同时控制保持为一个独立的确定性运行引擎，用于防止重复调用、错误限制和终止判断。通过多步条件推理实验，我们证明SCL实现了零策略违规，防止了冗余工具调用，并保持了完整的决策可追溯性。我们将在混合智能中定位SCL，区分它与基于提示、仅记忆和神经符号方法，并推导出三个设计原则：模块分解、适应性符号治理和透明状态管理。通过开源实现和一个实时GPT-4o驱动的旅行规划代理，本文为可靠、可解释和可治理的LLM代理提供了实用路径。
英文摘要：
Large language model agents suffer from architectural fragilities such as entangled reasoning and execution, memory volatility, and uncontrolled action sequences. We introduce Structured Cognitive Loop (SCL), a modular agent architecture that separates cognition into Retrieval, Cognition, Control, Action, and Memory (R-CCAM). SCL introduces Regulation as a dedicated governance layer through which Soft Symbolic Control applies symbolic constraints to probabilistic inference, while Control remains a distinct deterministic runtime engine for duplicate-call prevention, error limits, and termination judgment. Through multi-step conditional reasoning experiments, we show that SCL achieves zero policy violations, prevents redundant tool calls, and maintains complete decision traceability. We position SCL within hybrid intelligence, distinguish it from prompt-centric, memory-only, and neuro-symbolic approaches, and derive three design principles for trustworthy agents: modular decomposition, adaptive symbolic governance, and transparent state management. With an open-source implementation and a live GPT-4o-powered travel planning agent, this work offers a practical path toward reliable, explainable, and governable LLM agents.
