CogEvo-Edu: 基于认知进化教育的多智能体协作系统
CogEvo-Edu: Cognitive Evolution Educational Multi-Agent Collaborative System
AI总结：
CogEvo-Edu通过认知进化机制实现教育多智能体协作，提升数字信号处理教学效果。
摘要：
大型语言模型（LLMs）正越来越多地被用作STEM教育中的对话导师，但大多数系统仍然依赖于单一LLM，并采用静态的检索增强生成（RAG）管道来处理课程材料。这种设计在复杂领域如数字信号处理（DSP）中遇到了困难，因为导师必须维护连贯的长期学生模型，管理异构的知识库，并在长时间的交互中调整教学策略。我们主张检索、记忆和控制应被视为一个耦合的认知进化过程。我们在此观点下构建了CogEvo-Edu，一个分层的教育多智能体系统，包括认知感知层（CPL）、知识进化层（KEL）和元控制层（MCL）。CPL维护双记忆并执行基于置信度的巩固，以在有限上下文中构建结构化的、自我纠正的学生档案。KEL为每个知识片段分配时空价值，驱动激活、语义压缩和遗忘。MCL将教学制定为分层的顺序决策过程，协调专门的智能体，并通过双内-外循环共同调整CPL/KEL的超参数。为了评估CogEvo-Edu，我们构建了DSP-EduBench，一个针对DSP教学的垂直基准，具有异构资源、模拟学生档案和长周期交互脚本。使用三个模型LLM-as-a-Judge集成，CogEvo-Edu将总体分数从5.32提升到9.23，并在静态RAG、简单记忆和单智能体变体的所有六个指标上均有所提升，证明了共同进化学生档案、知识库和教学策略的价值。
英文摘要：
Large language models (LLMs) are increasingly deployed as conversational tutors in STEM education, yet most systems still rely on a single LLM with a static retrieval-augmented generation (RAG) pipeline over course materials. This design struggles in complex domains such as digital signal processing (DSP), where tutors must maintain coherent long-term student models, manage heterogeneous knowledge bases, and adapt teaching strategies over extended interactions. We argue that retrieval, memory, and control should be treated as a coupled cognitive evolution process. We instantiate this view in CogEvo-Edu, a hierarchical educational multi-agent system comprising a Cognitive Perception Layer (CPL), a Knowledge Evolution Layer (KEL), and a Meta-Control Layer (MCL). CPL maintains dual memories and performs confidence-weighted consolidation to build structured, self-correcting student profiles under limited context. KEL assigns each knowledge chunk a spatiotemporal value that drives activation, semantic compression, and forgetting. MCL formulates tutoring as hierarchical sequential decision making, orchestrating specialized agents and jointly adapting CPL/KEL hyperparameters via a dual inner--outer loop. To evaluate CogEvo-Edu, we construct DSP-EduBench, a vertical benchmark for DSP tutoring with heterogeneous resources, simulated student profiles, and long-horizon interaction scripts. Using a three-model LLM-as-a-Judge ensemble, CogEvo-Edu raises the overall score from 5.32 to 9.23 and improves all six indicators over static RAG, simple memory, and a single-agent variant, demonstrating the value of jointly evolving student profiles, knowledge bases, and teaching policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
