Natalia Berloff and Mikhail Epstein are the 2026 George Gamow award laureates. The award is established by the Russian American Science Association (RASA-America) in honor of the distinguished Russian American physicist Professor Georgy Antonovich Gamow (1904–1968), and to recognize members of the Russian-speaking scientific diaspora for outstanding achievements widely acknowledged by the scientific community.

Natalia Beloff, professor at the Cambridge University is awarded  for her groundbreaking contributions to quantum hydrodynamics and physics-inspired computing, and for her extraordinary breadth of interdisciplinary research connecting fundamental theoretical advances with experimental realizability and technological potential across the logistics, fintech, pharmaceutical, and data management sectors.

Mikhail Epstein, professor at Emory University is awarded for his pioneering contributions to the study of Russian postmodernism and conceptualism, significant advances in the philosophy and literary theory, construction of alternative concepts and propositions to multiple symbolic systems and theoretical models; and for his role as a public intellectual and advocate for the humanities and their transformative potential, articulating a vision of scholarship as a creative and forward-looking process.

Seeing inside an object is one thing; seeing exactly what it is made of, in three dimensions, is far more difficult. Now, researchers at Bar-Ilan University and collaborating institutions have developed a new X-ray imaging technique that can map the three-dimensional chemical composition of an object 43 times faster than conventional approaches, while using more than 97% less measurement data.

The new method, reported in Optica, could eventually make it easier for researchers to examine materials, biological samples and other complex structures that are difficult or too time-consuming to study with existing techniques.

A new large language model (LLM)-based artificial intelligence framework called AlphaProof Nexus can autonomously tackle select math problems, seeking proofs and using verification to ensure the resulting solutions are logically sound, researchers report. The system solved dozens of previously open problems across several mathematical fields, suggesting that artificial intelligence (AI) could become a useful tool for automated mathematical discovery and research. “[The authors report] that even unsuccessful proof attempts by AI could help them understand the problems better and make progress on solving them,” write Jeremy Avigad and Matthew Ballard in a related Perspective. “This underscores that an essential goal of developing AI for mathematics is to support mathematicians in the search for knowledge and understanding that lead to further advances.” Large language models (LLMs) have demonstrated growing ability to solve difficult mathematical problems, but their tendency to produce subtle logical errors or “hallucinations” makes them unreliable for research without extensive human expert review. One promising way to mitigate these issues is to have AI agents generate mathematical proofs in formal programming languages such as Lean, which automatically verifies each logical step and prevents errors from going unnoticed.  While this approach has been successfully applied to competition mathematics and in the human-aided formalization of natural language arguments, its potential in solving open research-level mathematical problems remains unknown.

To address this gap, George Tsoukalas and colleagues developed AlphaProof Nexus, a framework that uses multiple AI agents to search for mathematical proofs with feedback from the Lean compiler. Tsoukalas et al. also developed a more advanced, full-featured agent that coordinated subagents through an evolutionary algorithm to use AlphaProof as a specialized proof tool. In tests, the system was able to solve nine of the 353 attempted Erdős problems, including two that had remained unsolved for more than 50 years. It was also able to solve 44 of 492 open On-Line Encyclopedia of Integer Sequences (OEIS) conjectures as well as several other research-level problems in fields such as algebraic geometry, optimization, quantum optics, and graph theory.