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University community mourns

MBZUAI ·

MBZUAI mourns the passing of UAE President Sheikh Khalifa bin Zayed Al Nahyan. The university offers condolences to the Royal family, the UAE government, and the people. The Ministry of Presidential Affairs declared 40 days of official mourning. Why it matters: This event marks a significant moment of transition and reflection for the UAE and its institutions.

Observing Eid with our neighbors

KAUST ·

This is an announcement from KAUST wishing readers well for Eid. It includes a picture of King Abdullah. It states that all rights are reserved. Why it matters: This is a routine announcement from a major regional university.

A state of mind fostering excellence

KAUST ·

This is an announcement from KAUST. It encourages people to apply to KAUST. The announcement also mentions the late King Abdullah bin Abdulaziz Al Saud. Why it matters: Routine announcements like this help increase awareness of KAUST as a leading research university.

KAUST hosts future faculty convention

KAUST ·

KAUST hosted a Future Faculty Program convention. Najah Ashry, KAUST VP of Saudi Initiatives, and Jean Frechet, KAUST VP for research, spoke at the event. The convention hosted visitors from Saudi public and private universities. Why it matters: The event likely aimed to foster collaboration and faculty development within the Saudi higher education system.

Cross-modal understanding and generation of multimodal content

MBZUAI ·

Nicu Sebe from the University of Trento presented recent work on video generation, focusing on animating objects in a source image using external information like labels, driving videos, or text. He introduced a Learnable Game Engine (LGE) trained from monocular annotated videos, which maintains states of scenes, objects, and agents to render controllable viewpoints. Why it matters: This talk highlights advancements in cross-modal AI, potentially enabling new applications in gaming, simulation, and content creation within the region.

Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR

arXiv ·

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.

Beyond Attention: Orchid’s Adaptive Convolutions for Next-Level Sequence Modeling

MBZUAI ·

A new neural network architecture called Orchid was introduced that uses adaptive convolutions to achieve quasilinear computational complexity O(N logN) for sequence modeling. Orchid adapts its convolution kernel dynamically based on the input sequence. Evaluations across language modeling and image classification show that Orchid outperforms attention-based architectures like BERT and Vision Transformers, often with smaller model sizes. Why it matters: Orchid extends the feasible sequence length beyond the practical limits of dense attention layers, representing progress toward more efficient and scalable deep learning models.

Saving ghost cities

KAUST ·

In a 2018 KAUST lecture, MIT professor Kamal Youcef-Toumi discussed the case of Ordos Kangbashi, a Chinese city designed for a million residents that became a near-ghost town. Despite government incentives, the city struggled due to an economic downturn and lack of social and economic balance. Youcef-Toumi emphasized the importance of the public realm and a balance between social and economic development for successful cities. Why it matters: The analysis provides insights relevant to urban planning in Saudi Arabia and the broader GCC region, where new cities and megaprojects are being developed.