23 citations · 45 across the 11 of their papers we have counts for
12 papers
Breaking the high-degree barrier in Lehmer's problem with a human-AI discovery loop
Yuri Chervonyi, Sergei Gukov, Thang Luong
While artificial intelligence has made rapid progress in mathematical reasoning, many open problems depend on structure that is revealed only through large-scale computational expl…
MaD Physics: Evaluating information seeking under constraints in physical environments
Moksh Jain, Mehdi Bennani, Johannes Bausch +4
Scientific discovery is fundamentally a resource-constrained process that requires navigating complex trade-offs between the quality and quantity of measurements due to physical an…
Aletheia tackles FirstProof autonomously
Tony Feng, Junehyuk Jung, Sang-hyun Kim +14
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed t…
Towards Autonomous Mathematics Research
Tony Feng, Trieu H. Trinh, Garrett Bingham +25
Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from com…
Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems
Tony Feng, Trieu Trinh, Garrett Bingham +21
We present a case study in semi-autonomous mathematics discovery, using Gemini to systematically evaluate 700 conjectures labeled 'Open' in Bloom's Erdős Problems database. We empl…
Towards Robust Mathematical Reasoning
Thang Luong, Dawsen Hwang, Hoang H. Nguyen +17
Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are e…
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
Yuri Chervonyi, Trieu H. Trinh, Miroslav Olšák +8
We present AlphaGeometry2 (AG2), a significantly improved version of AlphaGeometry introduced in (Trinh et al., 2024), which has now surpassed an average gold medalist in solving O…
Towards practical reinforcement learning for tokamak magnetic control
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi +15
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbac…
Controlling Commercial Cooling Systems Using Reinforcement Learning
Jerry Luo, Cosmin Paduraru, Octavian Voicu +33
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with…
Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning
William Wong, Praneet Dutta, Octavian Voicu +3
Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A…
Semi-analytical Industrial Cooling System Model for Reinforcement Learning
Yuri Chervonyi, Praneet Dutta, Piotr Trochim +9
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) appli…
Zap: Making Predictions Based on Online User Behavior
Yuri Chervonyi, Dragos Harabor, Brian Zhang +1
This paper introduces Zap, a generic machine learning pipeline for making predictions based on online user behavior. Zap combines well known techniques for processing sequential da…