This paper introduces Diffusion-BBO, a new online black-box optimization (BBO) framework that uses a conditional diffusion model as an inverse surrogate model. The framework employs an Uncertainty-aware Exploration (UaE) acquisition function to propose scores in the objective space for conditional sampling. The approach is shown theoretically to achieve a near-optimal solution and empirically outperforms existing online BBO baselines across 6 scientific discovery tasks.
Daniela Rus from MIT CSAIL discussed the role of AI in revolutionizing autonomous vehicles, emphasizing the need for risk evaluation, intent understanding, and adaptation to diverse driving styles. The talk highlighted integrating risk and behavior analysis in autonomous vehicle control systems. Social Value Orientation (SVO) can be incorporated into decision-making for self-driving vehicles. Why it matters: This research advances the development of safer and more adaptive autonomous vehicles, crucial for their successful deployment in diverse real-world driving scenarios within the GCC region and globally.
KAUST has announced a collaboration with Ocean Aero and Shelf Subsea to enhance Red Sea research using autonomous underwater and surface vehicles (AUSVs). Ocean Aero's Triton Generation III AUSV, which can sail and submerge for long-range data collection, will be customized with sensors for KAUST's Red Sea Research Center. KAUST's CEMSE division will integrate AI and IoT features into the vehicles. Why it matters: This partnership will advance KAUST's marine research capabilities and contribute to the understanding of the Red Sea's unique environment, aligning with Saudi Arabia's Vision 2030 and the UN's Ocean Science Decade.
MBZUAI researchers have developed SVRPBench, a new open benchmark for testing vehicle routing algorithms under real-world conditions. SVRPBench simulates unpredictable urban delivery scenarios including rush-hour traffic, accidents, and customer delivery time preferences. The benchmark uses realistic city models with clustered customer locations, unlike existing deterministic benchmarks. Why it matters: This benchmark offers a more practical evaluation for vehicle routing algorithms, potentially leading to significant cost savings and improved efficiency in logistics within the region and beyond.
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.