A new method is proposed to reduce the verbosity of LLMs in step-by-step reasoning by retaining moderately easy problems during Reinforcement Learning with Verifiable Rewards (RLVR) training. This approach acts as an implicit length regularizer, preventing the model from excessively increasing output length on harder problems. Experiments using Qwen3-4B-Thinking-2507 show the model achieves baseline accuracy with nearly twice shorter solutions.
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.
Researchers at MBZUAI have developed Auto-DUB, a system using deep learning, NLP, and CV to improve audio-visual dubbing, particularly for educational videos. The three-step process generates subtitles, creates an audio representation, and synchronizes the audio with lip movements. The system aims to overcome language barriers in e-learning by providing accurate translations and lip-synced audio. Why it matters: This research addresses a critical need in online education by making content more accessible to non-native English speakers, potentially expanding access to global educational resources in the Arab world.