The rise of Multimodal Large Language Models (MLLMs) offers new opportunities for Micro-Expression (ME) analysis. This paper introduces Micro-Expression Visual Question Answering (ME-VQA), a novel task reformulating ME annotations (e.g., emotion categories, action units) into QA pairs. To address key challenges-hardware limitations, context inconsistency, and compositional reasoning gaps-we propose a Relationship-Aware Hierarchical VQA Framework. Our approach leverages mined emotion correlations (e.g., coarse-to-fine label dependencies) and employs a two-stage process: 1) Coarse-grained anchoring for broad emotion categories, and 2) Fine-grained reasoning constrained by coarse outputs and statistical rules. We further optimize efficiency via a dual-phase video sampling strategy: during training, keyframes (onset/apex/offset) and random non-expression frames are used; uniform sampling is applied at inference. Experiments demonstrate significant improvements in answer consistency and accuracy.

Estimating engagement in group interactions is crucial for building socially intelligent systems, such as in human-agent and human-robot interaction. However, precisely modeling the continuous frame-level fluctuations of engagement remains challenging, particularly when considering the complex multi-party signal interactions within groups. Our method employs an encoder that integrates BiLSTM with Transformer to effectively capture both local and global temporal dependencies of multimodal features. Crucially, we explicitly fuse signals from both the target participant and their conversational partners in group to model the holistic group interaction dynamics. Furthermore, we introduce an 8x overlapped optimized sliding window strategy, constructing a ''sliding pipeline'', which significantly enhances the temporal smoothness, continuity, and stability of predictions. In the final regression stage, we replace the traditional multilayer perceptron(MLP) decoder with Kolmogorov-Arnold Network (KAN), leveraging their superior function approximation capability to achieve more accurate engagement predictions. Evaluated on the test sets of NoXi-base, NoXi-addition, MPIIGroupInteraction and NoXi-J datasets from the Multimediate'25 Engagement Challenge, our approach demonstrates significant performance improvements, achieving highly competitive Concordance Correlation Coefficients (CCC) of 0.678 for global, approximately 56.1% higher than the baseline, which shows a significant improvement.