Beyond Real Weights: Hypercomplex Representations for Stable Quantization

The introduction of MM-CoT marks a significant advancement in the evaluation of Chain-of-Thought reasoning within multimodal models, focusing on their ability to ground reasoning in visual evidence and maintain logical coherence. This benchmark aims to address the gap in existing assessments that prioritize generation over verification, ensuring models can select event chains that meet visual and logical criteria.

ReCAD has been introduced as a reinforcement learning framework that utilizes pretrained large models to generate precise parametric CAD models from multimodal inputs, enhancing the capabilities of vision-language models in computer-aided design. This approach allows for complex CAD operations with minimal functional input, contrasting with traditional methods that rely heavily on supervised fine-tuning.

A new technique called Dropout Prompt Learning has been proposed to enhance the robustness of vision-language models by applying dropout to both textual and visual tokens, allowing for flexible dropout probabilities based on token significance. This method aims to improve generalization in challenging scenarios such as low-shot learning and out-of-distribution generalization.

The introduction of TV2TV marks a significant advancement in video generation models, addressing challenges related to complex outputs that require semantic branching and high-level reasoning. This unified framework integrates language modeling and video flow matching through a Mixture-of-Transformers architecture, allowing for an interleaved generation process of text and video frames.