SPARK：通过智能体驱动的检索和知识共享实现搜索个性化
SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing
AI总结：
提出SPARK框架，利用基于角色的多智能体LLM协作实现动态个性化搜索，通过协调器激活相关专家智能体，结合记忆和推理模块，从分布式行为中涌现个性化特性。
摘要：
个性化搜索需要能够建模用户不断变化的多维信息需求；这对于受限于静态配置文件或单一检索管道的系统来说是一个挑战。我们提出了SPARK（通过智能体驱动的检索和知识共享实现搜索个性化），这是一个框架，其中协调的基于角色的大语言模型（LLM）智能体提供特定任务的检索和涌现个性化。SPARK形式化了一个由角色、专业知识、任务上下文和领域定义的角色空间，并引入了一个角色协调器，该协调器动态解释传入的查询以激活最相关的专门智能体。每个智能体执行独立的检索增强生成过程，由专用的长期和短期记忆存储以及上下文感知推理模块支持。智能体间的协作通过结构化通信协议促进，包括共享记忆库、迭代辩论和接力式知识转移。借鉴认知架构、多智能体协调理论和信息检索的原理，SPARK建模了由最小协调规则支配的分布式智能体行为如何产生涌现个性化特性。该框架在协调效率、个性化质量和认知负载分布方面产生了可检验的预测，同时结合了自适应学习机制以实现持续的角色细化。通过将细粒度的智能体专业化与协作检索相结合，SPARK为能够捕捉人类信息寻求行为的复杂性、流动性和上下文敏感性的下一代搜索系统提供了见解。
英文摘要：
Personalized search demands the ability to model users' evolving, multi-dimensional information needs; a challenge for systems constrained by static profiles or monolithic retrieval pipelines. We present SPARK (Search Personalization via Agent-Driven Retrieval and Knowledge-sharing), a framework in which coordinated persona-based large language model (LLM) agents deliver task-specific retrieval and emergent personalization. SPARK formalizes a persona space defined by role, expertise, task context, and domain, and introduces a Persona Coordinator that dynamically interprets incoming queries to activate the most relevant specialized agents. Each agent executes an independent retrieval-augmented generation process, supported by dedicated long- and short-term memory stores and context-aware reasoning modules. Inter-agent collaboration is facilitated through structured communication protocols, including shared memory repositories, iterative debate, and relay-style knowledge transfer. Drawing on principles from cognitive architectures, multi-agent coordination theory, and information retrieval, SPARK models how emergent personalization properties arise from distributed agent behaviors governed by minimal coordination rules. The framework yields testable predictions regarding coordination efficiency, personalization quality, and cognitive load distribution, while incorporating adaptive learning mechanisms for continuous persona refinement. By integrating fine-grained agent specialization with cooperative retrieval, SPARK provides insights for next-generation search systems capable of capturing the complexity, fluidity, and context sensitivity of human information-seeking behavior.
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。
