PersonaVLM: Long-Term Personalized Multimodal LLMs

TL;DR

PersonaVLM introduces a long-term personalized multimodal language model framework that remembers, reasons with, and adapts to individual user preferences over time, validated by a new comprehensive benchmark.

Contribution

It presents a novel framework for long-term personalization of multimodal LLMs, integrating memory, reasoning, and response alignment capabilities.

Findings

Improves personalization effectiveness by 22.4% on Persona-MME benchmark.

Outperforms GPT-4o by 5.2% in long-term personalized responses.

Establishes a new benchmark with over 2,000 interaction cases.

Abstract

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.