SPARK: Agent-Driven Personalized Search
Analysis
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.
“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.”