SPARK: Agent-Driven Personalized Search
Research Paper#Personalized Search, LLM Agents, Information Retrieval🔬 Research|Analyzed: Jan 3, 2026 15:56•
Published: Dec 30, 2025 06:09
•1 min read
•ArXivAnalysis
This paper introduces SPARK, a novel framework for personalized search using coordinated LLM agents. It addresses the limitations of static profiles and monolithic retrieval pipelines by employing specialized agents that handle task-specific retrieval and emergent personalization. The framework's focus on agent coordination, knowledge sharing, and continuous learning offers a promising approach to capturing the complexity of human information-seeking behavior. The use of cognitive architectures and multi-agent coordination theory provides a strong theoretical foundation.
Key Takeaways
- •SPARK utilizes coordinated LLM agents for personalized search.
- •The framework employs a persona space and a Persona Coordinator for dynamic query interpretation.
- •Agents use retrieval-augmented generation, memory stores, and reasoning modules.
- •Inter-agent collaboration is facilitated through structured communication.
- •SPARK aims to capture the complexity of human information-seeking behavior.
Reference / Citation
View Original"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."