5 citations · 15 across the 8 of their papers we have counts for
8 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations
DongHyun Choi, Lucas Spangher, Chris Hidey +2
Transformer-based Large Language Models, which suffer from high computational costs, advance so quickly that techniques proposed to streamline earlier iterations are not guaranteed…
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use
Nicholas Roth, Christopher Hidey, Lucas Spangher +6
In this paper, we propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI. Our approach decomposes the ag…
Project MPG: towards a generalized performance benchmark for LLM capabilities
Lucas Spangher, Tianle Li, William F. Arnold +6
There exists an extremely wide array of LLM benchmarking tasks, whereas oftentimes a single number is the most actionable for decision-making, especially by non-experts. No such ag…
Time Series Viewmakers for Robust Disruption Prediction
Dhruva Chayapathy, Tavis Siebert, Lucas Spangher +3
Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. Here we endeavor to study the probl…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Autoregressive Transformers for Disruption Prediction in Nuclear Fusion Plasmas
Lucas Spangher, William Arnold, Alexander Spangher +2
The physical sciences require models tailored to specific nuances of different dynamics. In this work, we study outcome predictions in nuclear fusion tokamaks, where a major challe…
Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas
William F Arnold, Lucas Spangher, Christina Rea
Grid decarbonization for climate change requires dispatchable carbon-free energy like nuclear fusion. The tokamak concept offers a promising path for fusion, but one of the foremos…