7 citations · 10 across the 7 of their papers we have counts for
8 papers
Can AI Follow In Einstein's Footsteps?
Michael Shalyt, Nathan Regev, Marin Soljačić +1
AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful,…
The Ramanujan Challenge For AI
Michael Shalyt, Rotem Kalisch, Carsten Schneider +7
To help evaluate the mathematical skills of current AI systems, we present a set of formulas for fundamental mathematical constants. These problems are attractive for AI evaluation…
On Conservative Matrix Fields: Continuous Asymptotics and Arithmetic
Shachar Weinbaum, Elyasheev Leibtag, Rotem Kalisch +2
We present the Conservative Matrix Field (CMF) as a tool for the analysis and computation of D-finite functions. We use conservative matrix fields to establish asymptotic propertie…
ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark
Michael Shalyt, Rotem Elimelech, Ido Kaminer
Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization with genuine reasoning. To address this…
From Euler to AI: Unifying Formulas for Mathematical Constants
Tomer Raz, Michael Shalyt, Elyasheev Leibtag +4
The constant has fascinated scholars throughout the centuries, inspiring numerous formulas for its evaluation, such as infinite sums and continued fractions. Despite their indi…
Unsupervised Discovery of Formulas for Mathematical Constants
Michael Shalyt, Uri Seligmann, Itay Beit Halachmi +3
Ongoing efforts that span over decades show a rise of AI methods for accelerating scientific discovery, yet accelerating discovery in mathematics remains a persistent challenge for…
Algorithm-assisted discovery of an intrinsic order among mathematical constants
Rotem Elimelech, Ofir David, Carlos De la Cruz Mengual +6
In recent decades, a growing number of discoveries in fields of mathematics have been assisted by computer algorithms, primarily for exploring large parameter spaces that humans wo…
Lindbladians for controlled stochastic Hamiltonians
J. E. Avron, O. Kenneth, A. Retzker +1
We construct Lindbladians associated with controlled stochastic Hamiltonians in weak coupling. This allows to determine the power spectrum of the noise from measurements of dephasi…