Beyond Language: Is Mathematical AI the Future of Trustworthy Systems?

SAN FRANCISCO, CA – December 02, 2025 – For the past decade, the artificial intelligence landscape has been dominated by the meteoric rise of Large Language Models (LLMs), systems renowned for their ability to generate human-like text. Now, a strategic shift is emerging, one that questions the very foundation of these word-predicting models. San Francisco-based Logical Intelligence has ignited this conversation by announcing its internal AI tool, Aleph, achieved a 76 percent score on the Putnam Benchmark, a demanding test of formal mathematical reasoning. The achievement is not just a high score; it’s a bold statement on the company’s pivot away from language-based AI toward a future built on mathematical certainty.

This move challenges the prevailing AI paradigm, suggesting that for systems where failure is not an option, a different approach is necessary—one that reasons with logic rather than guessing the next word in a sentence.

A New Foundation for AI Reasoning

The core limitation of today's popular AI models, according to a growing chorus of experts, lies in their architecture. LLMs operate by predicting tokens, or fragments of words, in a sequence. This method, while powerful for creative and conversational tasks, produces what Logical Intelligence describes as “long, fragile chains of tokens that can fall apart with a single incorrect step.” The result is an AI prone to “hallucinations”—confidently stated falsehoods—making it inherently unpredictable and risky for high-stakes applications.

Logical Intelligence is championing an alternative: language-free, mathematically grounded Energy Based Models (EBMs). Unlike LLMs, which process information sequentially, these EBMs operate on a different principle entirely. “An EBM does not think in words. It reasons in continuous mathematical states shaped by the structure of the problem,” the company explains. Instead of building an answer piece by piece, the model updates its entire internal state at once, allowing it to explore alternatives, correct its course, and converge on a stable, verifiable solution. The process behaves less like a predictive text engine and more like a trained mathematician systematically working through a proof.

“If you need certainty, you cannot rely on word prediction,” stated Eve Bodnia, founder and CEO of Logical Intelligence, in the company’s announcement. “You need a system that works through the structure of a problem. EBMs give us the foundation for that.” This strategic shift represents a return to mathematical rigor, prioritizing provable correctness over probabilistic fluency.

The Benchmark for Trust

Aleph’s performance on the Putnam Benchmark is the first major proof point for the company's strategy. The benchmark, known officially as PutnamBench, is derived from the notoriously difficult William Lowell Putnam Mathematical Competition, an exam where even top math students often score near zero. Critically, PutnamBench doesn't evaluate an AI's ability to describe math in text; it measures its capacity to generate formal, machine-checkable proofs. It tests for pure logical deduction.

Achieving a 76% score, which corresponds to solving 500 distinct problems, places Aleph ahead of publicly evaluated LLMs on this specific task of formal theorem-proving. What makes this more compelling is the nature of Aleph itself. Bodnia clarifies it is an internal tool developed on top of an LLM, not the company’s core product. “We built Aleph as an internal tool to test the mathematical rigor of the environment we are creating,” she noted. “Aleph’s performance proves that our foundations are strong.” The success of this hybrid tool suggests that embedding principles of mathematical verification can dramatically enhance an AI’s reasoning capabilities, validating the company’s foundational approach.

The broader AI industry is also seeing a trend towards mathematical reasoning, with models like DeepSeek Math-V2 showing remarkable performance on original Putnam competition problems. This indicates a wider recognition that the next frontier for AI is not just generating content, but understanding and verifying complex logical structures. Logical Intelligence’s focus on generating machine-checkable proofs, however, carves out a distinct niche. “Most AI systems can describe mathematics. Very few can prove anything,” Bodnia added. “Aleph gives us a new level of certainty in AI today.”

Securing Critical Infrastructure

The ultimate goal of this strategic shift extends far beyond academic benchmarks. Logical Intelligence is positioning its EBMs as the future backbone for systems where uncertainty is unacceptable. This includes a vast range of critical infrastructure and advanced industries: true self-driving vehicles, aviation control systems, power grid management, automated manufacturing, national defense systems, and complex chip design.

In these domains, the probabilistic nature of LLMs is not a feature but a significant liability. A single hallucination in an autonomous vehicle’s control system or a power grid’s load-balancing algorithm could have catastrophic consequences. The demand for high-assurance AI has created a significant market gap, one that requires deterministic, auditable, and provably correct systems. Formal verification—the use of mathematical methods to prove a system's correctness—is already the gold standard in certifying safety-critical software, and Logical Intelligence aims to make it native to AI.

By building models that generate verifiable proofs, the company offers a pathway to an AI that can be trusted to behave the same way every time, providing the reliability necessary for integration into our most essential services. This focus on provable logic is what separates their approach from efforts centered on explainability alone; instead of just explaining a decision, their system aims to prove it is mathematically sound.

A Team of Titans and a Roadmap to 2026

Underpinning Logical Intelligence’s ambitious vision is a team with formidable credentials in mathematics and computer science. The leadership includes CEO Eve Bodnia, a PhD candidate in Algebraic Topology; CTO Mikhail Rubinchik, an ICPC medalist and coach; and CRO Vlad Isenbaev, a 2009 ICPC World Champion. Perhaps most notably, the company's Chief Science Officer is Michael Freedman, a recipient of the 1986 Fields Medal—mathematics’ highest honor—for his work on the Poincaré conjecture. The company also states a Turing Award laureate guides its long-term scientific direction, adding another layer of deep expertise.

This concentration of talent is already being deployed. While the full general-purpose model is slated for release in 2026, the company is not waiting to build commercial traction. Research shows it is already working with a “small group of organizations” in pilot programs and has established a key partnership with the Solana Foundation to help build fully-verified cryptographic protocols. This early focus on the high-stakes cryptocurrency space, where software errors can lead to billion-dollar losses, is a shrewd move to demonstrate the immediate value of formal verification.

The journey is just beginning, but the destination is clear. Logical Intelligence is building a commercial and technological strategy around the principle of mathematical truth, betting that as AI becomes more integrated into our world, the demand for proof will outweigh the appeal of prediction. As Bodnia concluded, “Aleph is our first milestone. The full system is coming in 2026.”