25 citations · 32 across the 7 of their papers we have counts for
3 papers · 1 filter
Predict before you train: Scaling Laws for particle physics foundation models
Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute…
Towards Engineering Scaling Laws with Pretraining Data Composition
Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson +1
Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relatio…
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…