Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior

A recent study has explored the integration of visual and textual information in Multimodal Large Language Models (MLLMs), revealing that visual-text fusion occurs at specific layers within these models rather than uniformly across the network. The research highlights a late-stage

A recent study has introduced a framework aimed at mitigating hallucination issues in Multimodal Large Language Models (MLLMs) during Reinforcement Learning (RL) optimization. The research identifies key factors contributing to hallucinations, including over-reliance on visual reasoning and insufficient exploration diversity. The proposed framework incorporates modules for caption feedback, diversity-aware sampling, and conflict regularization to enhance model reliability.

A new benchmark called KidVis has been introduced to evaluate the visual perceptual capabilities of Multimodal Large Language Models (MLLMs), specifically assessing their performance against that of 6-7 year old children across six atomic visual capabilities. The results reveal a significant performance gap, with human children scoring an average of 95.32 compared to GPT-5's score of 67.33.

A recent study has introduced an anthropomorphic thresholded visual-search model observer, enhancing task-based image quality assessment by mimicking the human visual system. This model selectively processes high-salience features, improving discrimination performance and diagnostic accuracy while filtering out irrelevant variability.

A new method called PRISM has been introduced to optimize the selection of training data for Multimodal Large Language Models (MLLMs), addressing the redundancy in rapidly growing datasets that increases computational costs. This self-pruning intrinsic selection method aims to enhance efficiency without the need for extensive training or proxy-based inference techniques.

A recent study introduced MoHoBench, a benchmark designed to assess the honesty of Multimodal Large Language Models (MLLMs) when confronted with unanswerable visual questions. This research highlights the need for a systematic evaluation of MLLMs' response behaviors, as their trustworthiness in generating content remains underexplored.