The development of Emotional Support Conversation (ESC) systems is critical for delivering mental health support tailored to the needs of help-seekers. Recent advances in large language models (LLMs) have contributed to progress in this domain, while most existing studies focus on generating responses directly and overlook the integration of domain-specific reasoning and expert interaction.Therefore, in this paper, we propose a training-free Multi-Agent collaboration framework for ESC (MultiAgentESC).The framework is designed to emulate the human-like process of providing emotional support through three stages: dialogue analysis, strategy deliberation, and response generation.At each stage, a multi-agent system is employed to iteratively enhance information understanding and reasoning, simulating real-world decision-making processes by incorporating diverse interactions among these expert agents.Additionally, we introduce a novel response-centered approach to handle the one-to-many problem on strategy selection, where multiple valid strategies are initially employed to generate diverse responses, followed by the selection of the optimal response through multi-agent collaboration.Experiments on the ESConv dataset reveal that our proposed framework excels at providing emotional support as well as diversifying support strategy selection.

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos partially relevant to a given query. The core challenge lies in learning robust query-video alignment against spurious semantic correlations arising from inherent data uncertainty: 1) query ambiguity, where the query incompletely characterizes the target video and often contains uninformative tokens, and 2) partial video relevance, where abundant query-irrelevant segments introduce contextual noise in cross-modal alignment. Existing methods often focus on enhancing multi-scale clip representations and retrieving the most relevant clip. However, the inherent data uncertainty in PRVR renders them vulnerable to distractor videos with spurious similarities, leading to suboptimal performance. To fill this research gap, we propose Robust Alignment Learning (RAL) framework, which explicitly models the uncertainty in data. Key innovations include: 1) we pioneer probabilistic modeling for PRVR by encoding videos and queries as multivariate Gaussian distributions. This not only quantifies data uncertainty but also enables proxy-level matching to capture the variability in cross-modal correspondences; 2) we consider the heterogeneous informativeness of query words and introduce learnable confidence gates to dynamically weight similarity. As a plug-and-play solution, RAL can be seamlessly integrated into the existing architectures. Extensive experiments across diverse retrieval backbones demonstrate its effectiveness.

Understanding the internal mechanisms by which multi-modal large language models (LLMs) interpret different modalities and integrate cross-modal representations is becoming increasingly critical for continuous improvements in both academia and industry. In this paper, we propose a novel method to identify key neurons for interpretability — how multi-modal LLMs bridge visual and textual concepts for captioning. Our method improves conventional works upon efficiency and applied range by removing needs of costly gradient computation. Based on those identified neurons, we further design a multi-modal knowledge editing method, beneficial to mitigate sensitive words or hallucination. For rationale of our design, we provide theoretical assumption. For empirical evaluation, we have conducted extensive quantitative and qualitative experiments. The results not only validate the effectiveness of our methods, but also offer insightful findings that highlight three key properties of multi-modal neurons: sensitivity, specificity and causal-effect, to shed light for future research.