Logical Intelligence Introduces First Energy-Based Reasoning AI Model, Signals Early Steps Toward AGI, Adds Yann LeCun and Patrick Hillmann to Leadership

Logical Intelligence, an artificial intelligence company developing energy-based (EBM) reasoning systems, today announced that Kona 1.0, its pioneering EBM for reasoning, will enter pilot programs with select partners in the energy, advanced manufacturing, and semiconductor industries later this quarter.

Logical Intelligence also released a live demonstration of Kona 1.0 on its website, beginning with head-to-head sudoku challenges against leading large language models. Additional demonstrations, including chess and Go, are planned. The company said the goal is to give researchers and the public a clear view into how energy-based reasoning differs from probabilistic systems.

“Kona learns by recognizing and correcting its own mistakes, rather than guessing the most likely answer,” said Eve Bodnia, founder and CEO of Logical Intelligence. “If general intelligence means the ability to reason across domains, learn from error, and improve without being retrained for each task, then we are seeing in Kona the first credible signs of AGI. It is not the end state, but it is a clear break from narrow AI. AGI as a finished state will not emerge from any single model class. It will require an interdependent ecosystem composed of EBMs, LLMs, world models, and others, working together.”

The company named Yann LeCun, former chief AI scientist at Meta, as founding chair of its Technical Research Board and appointed Patrick Hillmann, formerly a director at General Electric and previously Chief Strategy Officer at Binance, as Chief Strategy Officer. LeCun and Hillmann complement Logical Intelligence’s leadership team which also includes Fields Medalist Michael Freedman as Chief of Mathematics and ICPC World Champion and Facebook alumnus Vlad Isenbaev as Chief of AI.

“A major question in AI is how to perform reasoning,” said LeCun. “My opinion has always been that true reasoning should be formulated as an optimization problem. This is the basis of what I have called energy-based models: reasoning and inference by minimizing an energy function. Logical Intelligence is the first company to move EBM-based reasoning from a research concept to products, enabling a new breed of more reliable AI systems.”

Logical Intelligence develops AI systems targeted at applications where behavior must be provably correct rather than statistically likely, including advanced manufacturing, energy infrastructure, semiconductor verification, and robotics. Unlike language models that guess the most likely answer, the company’s energy-based systems work by mapping out what is allowed and what is not, then finding solutions that stay inside those boundaries. The architecture is aimed at sectors where certification, liability, and auditability are prerequisites for deployment.

Kona 1.0 extends the company’s existing work in formal verification and verified code generation, which has been used to prove the correctness of software components in adversarial and regulated settings. The new model is designed to reason over entire systems rather than isolated functions, allowing correctness guarantees to persist as systems evolve.

“AI is moving into sectors where failure results in material consequences,” said Hillmann. “Markets are demanding systems that can be certified and defended, not just optimized for performance. We’ll also be engaging early with policymakers and industry leaders to ensure this technology is deployed carefully, with clear rules so that Kona scales in a responsible manner.”

About the recent appointees

Yann LeCun is founding chair of the technical research board at Logical Intelligence, executive chair of AMI Labs, and a professor at NYU. He was the chief AI scientist of Meta, the founding director of Meta-FAIR, and of the NYU Center for Data Science. After a PhD from Sorbonne Université and research positions at AT&T and NEC, he joined NYU in 2003 and Meta in 2013. He received the 2018 ACM Turing Award for his work on AI. He is a member of the US National Academies and the French Académie des Sciences.

Patrick Hillmann is chief strategy officer for Logical Intelligence. Most recently, Hillmann was chief strategy officer at Binance, the world’s largest cryptocurrency exchange. Earlier, he was an executive in the crisis and risk practice and global head of innovation at Edelman, and served in executive roles at General Electric and the National Association of Manufacturers. Hillmann has an M.A. in international economic policy from the University of Wisconsin-Madison.

Michael Freedman is chief of mathematics at Logical Intelligence. Freedman was awarded the Fields Medal, the highest honor in mathematics, for his work on the 4-dimensional generalized Poincaré conjecture, a landmark discovery in topology. He has received many awards and honors, including Sloan and Guggenheim Fellowships, a MacArthur Fellowship, and the National Medal of Science. He is an elected member of the National Academy of Sciences, and a fellow of both the American Academy of Arts and Sciences and of the American Mathematical Society. The founding director of Microsoft’s quantum computing program, Freedman also has an appointment at Harvard’s Center for Mathematical Sciences and Applications.

Vlad Isenbaev is chief of AI for Logical Intelligence. A software engineer and research scientist with experience in generative AI and reinforcement learning, energy based models for robotics, he has held technical positions at Nuro, Cruise Automotion, and Facebook.

About Logical Intelligence

Logical Intelligence develops energy-based AI systems for reasoning in mission-critical environments, including energy, advanced manufacturing, industrial automation, financial automation, and hardware design. Its Aleph agent is available today for formal verification and automated code generation with machine-checkable proofs, while its forthcoming Kona platform extends this approach into frontier-scale energy-based reasoning for continuously verifiable systems.

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