ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis

The research titled 'LLaVAction' focuses on evaluating and training multi-modal large language models (MLLMs) for action understanding, reformulating the EPIC-KITCHENS-100 dataset into a benchmark for MLLMs. The study reveals that leading MLLMs struggle with recognizing correct actions when faced with difficult distractors, highlighting a gap in their fine-grained action understanding capabilities.

DriveRX has been introduced as a vision-language reasoning model aimed at enhancing cross-task autonomous driving by addressing the limitations of traditional end-to-end models, which struggle with complex scenarios due to a lack of structured reasoning. This model is part of a broader framework called AutoDriveRL, which optimizes four core tasks through a unified training approach.

A theoretical analysis of decentralization in autoregressive generation has been presented, introducing the Decentralized Discrete Flow Matching objective, which expresses probability generating velocity as a linear combination of expert flows. Experiments demonstrate the equivalence between decentralized and centralized training settings for multimodal language models, specifically comparing LLaVA and InternVL 2.5-1B.