SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing

TL;DR

SPARK introduces a multi-agent LLM framework for personalized search, dynamically activating specialized agents to improve retrieval relevance and adapt to users' evolving needs through structured collaboration.

Contribution

The paper presents a novel multi-agent LLM-based framework that formalizes persona-driven retrieval and emergent personalization in search systems.

Findings

Enhanced personalization through agent collaboration

Dynamic activation of specialized agents improves retrieval relevance

Framework offers insights into cognitive load and coordination efficiency

Abstract

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.