1 citations · 1 across the 4 of their papers we have counts for
5 papers
BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators
Fabian Waschkowski, Prabod Rathnayaka, Lukas Wesemann
Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed through the Metal~4 tensor AP…
BaseRT: Best-in-Class LLM Inference on Apple Silicon via Native Metal
Prabod Rathnayaka, Fabian Waschkowski, Lukas Wesemann
We present BaseRT, a native Metal inference runtime for large language models (LLMs) on Apple Silicon, and report the highest inference throughput on this hardware to date. Existin…
Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting
Shu Zou, Xinyu Tian, Lukas Wesemann +3
Prompting has emerged as a practical way to adapt frozen vision-language models (VLMs) for video anomaly detection (VAD). Yet, existing prompts are often overly abstract, overlooki…
More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
Xinyu Tian, Shu Zou, Zhaoyuan Yang +5
Reasoning has emerged as a pivotal capability in Large Language Models (LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy Optimization (GRPO), these model…
Robust Spatiotemporal Forecasting Using Adaptive Deep-Unfolded Variational Mode Decomposition
Osama Ahmad, Lukas Wesemann, Fabian Waschkowski +1
Accurate spatiotemporal forecasting is critical for numerous complex systems but remains challenging due to complex volatility patterns and spectral entanglement in conventional gr…