TAMEing Long Contexts in Personalization: Towards Training-Free and State-Aware MLLM Personalized Assistant

• URL: http://arxiv.org/abs/2512.21616v1
• Date: Thu, 25 Dec 2025 10:23:56 GMT
• Title: TAMEing Long Contexts in Personalization: Towards Training-Free and State-Aware MLLM Personalized Assistant
• Authors: Rongpei Hong, Jian Lang, Ting Zhong, Yong Wang, Fan Zhou,
• Abstract summary: Long-Context MLLM Personalization evaluation benchmark is proposed.<n>New framework TAME endows MLLMs with double memories to manage the temporal and persistent variations of each personalized concept.
• Score: 32.497044980186544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Model (MLLM) Personalization is a critical research problem that facilitates personalized dialogues with MLLMs targeting specific entities (known as personalized concepts). However, existing methods and benchmarks focus on the simple, context-agnostic visual identification and textual replacement of the personalized concept (e.g., "A yellow puppy" -> "Your puppy Mochi"), overlooking the ability to support long-context conversations. An ideal personalized MLLM assistant is capable of engaging in long-context dialogues with humans and continually improving its experience quality by learning from past dialogue histories. To bridge this gap, we propose LCMP, the first Long-Context MLLM Personalization evaluation benchmark. LCMP assesses the capability of MLLMs in perceiving variations of personalized concepts and generating contextually appropriate personalized responses that reflect these variations. As a strong baseline for LCMP, we introduce a novel training-free and state-aware framework TAME. TAME endows MLLMs with double memories to manage the temporal and persistent variations of each personalized concept in a differentiated manner. In addition, TAME incorporates a new training-free Retrieve-then-Align Augmented Generation (RA2G) paradigm. RA2G introduces an alignment step to extract the contextually fitted information from the multi-memory retrieved knowledge to the current questions, enabling better interactions for complex real-world user queries. Experiments on LCMP demonstrate that TAME achieves the best performance, showcasing remarkable and evolving interaction experiences in long-context scenarios.

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