GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, performance is unparalleled. Unfortunately, GPU HBMs remain typically small when compared with lower-bandwidth CPU main memory. Besides…
Global cloud service providers handle inference workloads for Large Language Models (LLMs) that span latency-sensitive (e.g., chatbots) and insensitive (e.g., report writing) tasks, resulting in diverse and often conflicting Service Level Agreement (SLA) requirements. Managing such mixed workloads is challenging due to the complexity of…
Full workshop title: The 5th Workshop on Computer Vision in the Wild (CVinW): Towards Unified Multimodal Agents for Reasoning in the Wild Host conference: The Conference on Computer Vision and Pattern Recognition (CVPR) (opens in new tab) | June 3-4, 2026 Workshop organizers:Â Reuben Tan, Zhengyuan…
CPUs provide isolation mechanisms like virtualization and privilege levels to protect software. Yet these focus on architectural isolation while typically overlooking microarchitectural side channels, exemplified by Meltdown and Foreshadow. Software must therefore supplement architectural defenses with ad-hoc microarchitectural patches, which are constantly evolving as new…
Large Language Models (LLMs) are revolutionizing how users interact with information systems, yet their high inference cost poses serious scalability and sustainability challenges. Caching inference responses, allowing them to be retrieved without another forward pass through the LLM, has emerged as one possible solution. Traditional…
Large Language Models (LLMs) are increasingly employed in complex workflows, where different LLMs and fine-tuned variants collaboratively address complex tasks. However, these systems face significant inefficiencies due to redundant context processing of the shared context. We propose DroidSpeak, a framework that optimizes context sharing between…
Figure 1: Overall pipeline of cognitive load estimation with brain foundation model (BFM) in adaptive training systems. We investigate the use of Brain Foundational Models (BFMs) for continuous cognitive-load monitoring and examine key challenges in scalability, generalization, interpretability. We apply BFMs to continuous cognitive-load estimation…
Container platforms like Kubernetes are widely adopted for deploying latency-sensitive cloud services, and CPU resources for these containers are over-provisioned to ensure low 99th percentile tail latency under peak load. At the same time, cloud services exhibit bursty traffic patterns resulting in CPU usage variability…
Misconfiguration is frequently cited as a leading cause of service disruptions and outages. To prevent misconfiguration, we introduce network contracts — lightweight configuration checks that run efficiently, localize errors to specific lines, and require no heavyweight modeling of network protocols. We develop a tool Concord…
Reinforcement learning (RL) has become a cornerstone for enhancing the reasoning capabilities of large language models (LLMs), with recent innovations such as Group Relative Policy Optimization (GRPO) demonstrating exceptional effectiveness. In this study, we identify a critical yet underexplored issue in RL training: low-probability tokens…
The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human cognition and behavior from neural signals. In particular, the rise of multimodal AI models have brought new possibilities that have never been imagined before.…
Designing system algorithms remains challenging, where the discontinuous nature of the solution space often forces system engineers to rely on generic heuristics at the expense of performance. We study whether LLMs can practically drive algorithm generation, and find that they are biased towards well-known generic…
