most citedReinforcement learning with learned gadgets to tackle hard quantum problems on real hardware

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

hep-ph2026

The Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization

Sven Krippendorf, Leopoldo Sarra, Joseph Tooby-Smith

Rapid advances in AI have opened a new era for physics, enabling unprecedented capabilities in research and discovery. However, these advances also bring challenges in verifying, t…

cs.AI2026

AxDafny: Agentic Verified Code Generation in Dafny

Benjamin Breen, Austin Letson, Borja Requena Pozo +1

We study agentic code generation in Dafny, where a model must generate both executable code and the proof artifacts for verification. We present AxDafny, a verifier-guided repair f…

cs.AI2026

SorryDB: Can AI Provers Complete Real-World Lean Theorems?

Austin Letson, Leopoldo Sarra, Auguste Poiroux +9

We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed…

cs.AI2026

A Minimal Agent for Automated Theorem Proving

Borja Requena, Austin Letson, Krystian Nowakowski +2

We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core features shared amo…

quant-ph2024★ 4 cited

Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware

Akash Kundu, Leopoldo Sarra

Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device…