The field of multimodal reasoning and safety-critical applications is rapidly advancing, with a focus on developing more accurate and reliable models for real-world scenarios. Recent research has explored the use of multimodal inputs, such as images and text, to improve spatial reasoning and visual question answering. Additionally, there is a growing emphasis on ensuring the safety and reliability of AI systems, particularly in applications such as autonomous driving and embodied AI. Noteworthy papers in this area include:

• The introduction of DualXrayBench, a comprehensive benchmark for X-ray inspection that includes multiple views and modalities, which achieves significant improvements across all X-ray tasks.
• The proposal of Latent Representation Probing, a method for detecting uncertainty signals in vision-language models, which improves abstention accuracy by 7.6% over best baselines.
• The development of GuardTrace-VL, a vision-aware safety auditor that monitors the full Question-Thinking-Answer pipeline, achieving an F1 score of 93.1% on unsafe reasoning detection tasks.
• The introduction of MADRA, a training-free Multi-Agent Debate Risk Assessment framework that enhances safety awareness without sacrificing task performance, achieving over 90% rejection of unsafe tasks.