Understanding multimodal user personas in long-term dialogues is essential for building personalized and human-like dialogue systems. However, existing datasets suffer from limited persona diversity and static, overly simplified settings, making them insufficient for capturing the complexity of real-world interactions. To address these limitations, we introduce LongMP-Bench, a benchmark designed to evaluate the capabilities of models in understanding evolving user personas within long-term multimodal dialogues. We present a multi-step, scalable data construction pipeline that generates long-term interaction records centered around multimodal personas, followed by human refinement for quality assurance. The resulting dataset contains long conversations from 150 users, each exhibiting visual consistency and dynamic persona development over time. Built on this dataset, we define a suite of tasks to comprehensively assess models’ ability to track persona evolution, integrate visual and textual inputs, and apply persona understanding in realistic dialogue scenarios. Extensive experiments on LongMP-Bench highlight the substantial challenges in multimodal persona understanding, especially in tracking persona shifts and leveraging multimodal context effectively. We will release our benchmark and code to facilitate future research in multimodal and personalized dialogue systems.

Existing emotional support conversation (ESC) systems often react directly to surface-level distress expressions, generating reassurance or advice without adequately modeling the underlying psychological needs driving the distress. However, effective emotional support requires an interpretive process that connects what is expressed with why it is expressed and ultimately what support is needed. We argue that current ESC systems lack such an interpretive layer between observation and supportive intervention. To address this gap, we propose HierINT, a psychologically grounded hierarchical interpretive schema for emotional support conversations. HierINT distinguishes observable communicative intentions from the underlying psychological interpretations that drive them, including cognitive appraisals and unmet fundamental psychological needs. Building on this schema, we construct HierINT-Dlg, a dataset with hierarchical interpretive annotations and need-aligned support strategies. We further propose HierINT-CoRe, a collaborative reasoning framework that operationalizes HierINT by jointly modeling psychological interpretation and support strategy planning prior to response generation. Experiments on ESConv and HierINT-Dlg demonstrate consistent improvements over surface-level and intention-aware baselines, particularly on empowerment and personalization metrics. Our results suggest that effective emotional support requires moving beyond reactive response generation toward psychologically grounded need-informed intervention. Our data and code are available at https://github.com/43zxj/HierINT.