The global deployment of Large Language Models (LLMs) underscores the urgent need to evaluate their cultural alignment. However, assessing genuine "cultural awareness" across modalities (text, vision, speech) and languages remains a significant challenge. To comprehensively investigate this domain, we propose MMAC, a systematic framework that encompasses a tri-modally aligned cultural benchmark creation pipeline and a five-dimensional evaluation protocol to assess cross-country awareness disparities, evaluate cross-lingual and cross-modal consistency, and verify cultural knowledge generalization and grounding validity. Given the prevailing Western cultural bias in current models, we focus on 8 Asian countries as our dataset foundation to more acutely reveal potential cultural deficiencies in LLMs. Our dataset, MMAC-bench, features 27,000 human-curated questions across 10 languages. Crucially, it is the first dataset aligned at the input level across text, image, and speech, enabling direct cross-modal transfer tests. Each question consists of multiple-choice options accompanied by open-ended generated explanations, where 79% require multi-step reasoning grounded in cultural context, moving beyond simple memorization. We probe the causes of modal divergence, offering insights into fostering culturally robust MLLMs.

This study addresses the issue of what a Retrieval-Augmented Generation (RAG) chatbot should remember and what it should forget, based on findings from psychology. RAG retrieves relevant memories from past interactions to generate responses, and its effectiveness has been demonstrated. As conversations continue, however, the amount of stored memory keeps growing, which not only requires large storage capacity but also risks retaining unnecessary information, potentially reducing retrieval efficiency.To tackle this problem, we propose LUFY (Long-term Understanding and identiFYing key exchanges), a RAG chatbot that evaluates six distinct memory-related metrics derived from psychological models and real-world data. Instead of simply summing these metrics, it uses learned weights to account for the importance of each one. By using these weighted scores, the system can prioritize and retain relevant memories while gradually forgetting less important ones during both retrieval and memory management.To evaluate the effectiveness of LUFY in long-term conversations, we conducted experiments with human participants, who engaged in text-based conversations with three types of chatbots, each using different forgetting mechanisms, for at least two hours. The length of these conversations was more than 4.5 times longer than the longest conversations reported in previous studies. The results showed that prioritizing emotionally engaging memories while forgetting most of the conversation significantly enhanced user satisfaction.