Multimodal Sentiment Analysis aims to integrate information from various modalities to make complementary predictions. However, it often struggles with irrelevant or misleading visual and auditory information. Most existing approaches treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information. To address this challenge, we propose MoLAN, a unified ModaLity-aware noise dynAmic editiNg framework. Specifically, MoLAN performs modality-aware block partitioning by dividing the features of each modality into multiple blocks. Each block is then dynamically assigned a distinct denoising strength based on its noise level and semantic relevance, enabling fine-grained noise suppression while preserving essential multimodal information. Notably, MoLAN is a unified and flexible framework that can be seamlessly integrated into a wide range of multimodal models. Building upon this framework, we further introduce MoLAN+, a new multimodal sentiment analysis approach. Experiments across five models and four datasets demonstrate the broad effectiveness of the MoLAN framework. Extensive evaluations show that MoLAN+ achieves the state-of-the-art performance.

Multimodal Large Language Models (MLLMs) integrate visual encoders with Large Language Models (LLMs) and enable multimodal reasoning. However, for tasks that heavily rely on visual information, the model’s utilization of visual information remains unstable, which leads to reasoning failures. Prior works mainly strengthen multimodal reasoning by improving representation alignment or increasing computation. However, these methods do not explicitly characterize the differences in visual demands across tasks, making it difficult for the model to decide where and how strongly to attend to visual information. Consequently, visual attention allocation becomes a key factor that affects multimodal reasoning. To address these, we propose RATION, an entropy-driven task-adaptive visual attention allocation framework. First, we use a task routing strategy to infer the task type of each sample and identify the key layers. We use visual attention entropy as a control signal to dynamically allocate attention according to task demands. Experiments show that RATION achieves consistent performance gains across diverse reasoning tasks, datasets, and models, providing a clear direction toward more reliable multimodal reasoning.

Existing visual perception systems focus on region-level segmentation in single-turn dialogues, relying on complex and explicit query instructions. Such systems cannot reason at the pixel level and comprehend dynamic user intent that changes over interaction. Our work tackles this issue by introducing a novel task, Pixel-level Reasoning Segmentation (Pixel-level RS) based on multi-turn conversations, tracking evolving user intent via multi-turn interactions for fine-grained segmentation. To establish a benchmark for this novel task, we build a Pixel-level ReasonIng Segmentation Dataset Based on Multi-Turn Conversations (PRIST), comprising 24k utterances from 8.3k multi-turn conversational scenarios with segmentation targets. Building on PRIST, we further propose MIRAS, a Multi-turn Interactive ReAsoning Segmentation framework, integrates pixel-level segmentation with robust multi-turn conversation understanding, generating pixel-grounded explanations aligned with user intent. The PRIST dataset and MIRSA framework fill the gap in pixel-level reasoning segmentation. Experimental results on the PRIST dataset demonstrate that our method outperforms current segmentation-specific baselines in terms of segmentation and LLM-based reasoning metrics. The code and data are available at: https://anonymous.4open.science/r/PixelRS/.