6 papers
Stochastic Resetting: A Non-Equilibrium Framework for Prediction, Inference and Design
Tommer D. Keidar, Sagi Meir, Nir Sherf +4
Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling stochastic dynamics far fr…
More Bang for the Buck: Improving the Inference of Large Language Models at a Fixed Budget using Reset and Discard (ReD)
Sagi Meir, Tommer D. Keidar, Noam Levi +2
The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question correctly at least once in k trials. At a…
Universal Linear Response of First-Passage Kinetics: A Framework for Prediction and Inference
Tommer D. Keidar, Shlomi Reuveni
First-passage processes are pervasive across numerous scientific fields, yet a general framework for understanding their response to external perturbations remains elusive. While t…
First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
Sagi Meir, Tommer D. Keidar, Shlomi Reuveni +1
Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timesca…
Adaptive Resetting for Informed Search Strategies and the Design of Non-equilibrium Steady-states
Tommer D. Keidar, Ofir Blumer, Barak Hirshberg +1
Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently emerged as a powerful tool for accelerating processes ranging from queuing system…
Accelerating Molecular Dynamics through Informed Resetting
Jonathan R. Church, Ofir Blumer, Tommer D. Keidar +3
We present a procedure for enhanced sampling of molecular dynamics simulations through informed stochastic resetting. Many phenomena, such as protein folding and crystal nucleation…