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[2512.24008] SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing
Summary
The paper presents SPARK, a framework for personalized search using agent-driven retrieval and knowledge-sharing, enhancing user experience through dynamic persona-based LLM agents.
Why It Matters
SPARK addresses the limitations of traditional search systems by enabling personalized retrieval that adapts to users' evolving needs. This innovation is crucial in improving the efficiency and relevance of search results, making it significant for AI development and user experience.
Key Takeaways
- SPARK utilizes persona-based LLM agents for task-specific retrieval.
- The framework emphasizes inter-agent collaboration through structured communication.
- It incorporates adaptive learning mechanisms for continuous persona refinement.
- SPARK aims to enhance personalization quality and reduce cognitive load.
- The model predicts improvements in coordination efficiency and user satisfaction.
Computer Science > Artificial Intelligence arXiv:2512.24008 (cs) [Submitted on 30 Dec 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing Authors:Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury View a PDF of the paper titled SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing, by Gaurab Chhetri and Subasish Das and Tausif Islam Chowdhury View PDF HTML (experimental) 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. Draw...
