As machine learning systems continue to scale, the gap between capability and understanding has widened. Large language models now perform tasks that once seemed out of reach, yet the internal logic guiding those outputs often remains unclear. For researchers concerned with safety, correctness, and long term reliability, this opacity is not a philosophical inconvenience. It is a structural risk. Neel Somani approaches this problem from a discipline that predates modern machine learning itself: formal methods.
Formal methods encompass mathematically grounded techniques such as program verification, proof generation, and symbolic reasoning. They have long served as the backbone of security, privacy, and compiler correctness. Somani’s work focuses on bringing these tools into machine learning, where safety and interpretability still lack rigorous standards and remain largely driven by empirical testing and informal explanation.
Somani’s research philosophy begins with first principles. While it's possible to check a very large number of inputs to a function for correctness, no amount of testing is sufficient to establish a guarantee on a continuous domain. Modern large language models (transformers) operate on continuous inputs, so in a perfect world, we'd have stronger alternatives to testing.
During his undergraduate years at UC Berkeley, Somani pursued a triple major in computer science, mathematics, and business administration. His academic training exposed him to type systems, differential privacy, and formal verification. While working in research environments at Berkeley, he contributed to projects that formally proved whether specific algorithms satisfied privacy guarantees under precise mathematical definitions.
These experiences revealed a consistent pattern. Many properties we care about in machine learning can be formally defined, but verifying them at scale remains out of reach. Rather than abandoning formalism, Somani has oriented his research toward applying these methods where they are currently feasible, while laying groundwork for future expansion.
Formal methods gained prominence in security for a reason. When systems handle sensitive data or critical infrastructure, empirical success is not enough. A system must be proven to behave correctly within defined constraints. Somani argues that machine learning systems increasingly occupy a similar role.
Today, safety and interpretability research in AI remains fragmented. Researchers propose explanations for model behavior, but those explanations are often impossible to falsify. Claims about robustness, alignment, or internal reasoning are frequently supported by anecdote rather than proof. Somani describes the field as preparadigmatic, lacking a shared foundation for what constitutes a verified claim.
His work asks a simple but demanding question. If we expect strong guarantees from cryptographic systems, why should we accept weaker standards for AI systems that influence financial markets, healthcare decisions, and automated infrastructure?
After Berkeley, Somani joined Citadel’s commodities group as a quantitative researcher. There, he worked with optimization systems that directly affect real world markets. Many of these systems involve NP hard problems solved through mixed integer linear programming, where small modeling errors can produce outsized consequences.
This environment reinforced an important lesson. In high stakes systems, correctness matters more than elegance. Models must behave reliably under edge cases, not just average conditions. This perspective continues to inform Somani’s work in machine learning. As AI systems are entrusted with greater responsibility, expectations around verification and accountability must rise accordingly.
Rather than attempting to verify entire neural networks, Somani focuses on concrete components where formal methods can deliver immediate value. One example is his project Cuq, which applies formal verification techniques to GPU kernels written in Rust.
GPU code is notoriously difficult to reason about. Unlike many high level programming environments, GPUs offer limited safeguards against memory errors and undefined behavior. A minor mistake in indexing or synchronization can lead to subtle failures that evade standard testing. Cuq demonstrates that formal verification can be used to prove correctness properties of GPU kernels, reducing hidden risk in performance critical systems.
This work challenges the assumption that formal methods are too theoretical to be useful in modern machine learning pipelines. Instead, it shows that targeted applications can meaningfully improve reliability today.
Interpretability remains one of the most debated areas in AI research. Large language models are composed of layered transformations that resist straightforward explanation. Researchers often rely on visualizations or conceptual metaphors to describe what a model might be doing internally.
Somani’s project Symbolic Circuit Distillation takes a different approach. Building on work in mechanistic interpretability, the project extracts simplified circuits from a model and produces a human readable program that is provably equivalent to that circuit over a defined input space.
This distinction is critical. Rather than offering a plausible story about model behavior, the method allows researchers to formally prove whether an explanation is correct. While the technique currently applies only to limited cases, it establishes a standard for interpretability grounded in equivalence rather than intuition.
Somani describes his longer term research vision as an attempt to decompile transformer based models. The goal is to convert a trained model, whose internal behavior is opaque, into a human readable program that captures its function directly.
This is an ambitious direction, and Somani is careful not to oversell its feasibility. He has explicitly stated that his research agenda may evolve as technical constraints and community interest become clearer. This caution reflects a broader commitment to intellectual honesty. Rather than promising breakthroughs prematurely, his work focuses on establishing what is provably achievable.
If successful, decompilation would represent a fundamental shift in how interpretability is understood. Instead of asking whether humans can intuitively grasp a model, researchers would ask whether its behavior can be expressed in a formal language with defined semantics.
Somani’s work is not limited to theory. He has also contributed to practical optimization problems in machine learning infrastructure. His KV Marketplace project explores improvements to inference efficiency by optimizing GPU caching and reducing redundant computation.
Inference frameworks like vLLM and SGLang have standardized many optimization techniques, but there remain opportunities for further gains. By modifying inference engines directly, Somani demonstrates how careful systems level work can deliver measurable improvements without altering model outputs
This balance between theory and application is a defining feature of his research. Formal methods are not treated as an abstract ideal, but as tools that must ultimately integrate with real systems.
Looking forward, Somani suggests that formal methods may influence the design of future machine learning architectures. Certain components, such as attention mechanisms and normalization layers, are difficult to reason about within existing verification frameworks.
Rather than forcing formal tools to accommodate every architectural choice, future models may evolve to be more verification friendly. This mirrors historical developments in software engineering, where formal verification became feasible once systems stabilized around well defined abstractions.
Somani draws parallels to efforts like CompCert, which formally verified compiler correctness after compilation pipelines matured. He believes a similar trajectory may emerge in machine learning as foundational components become standardized
Throughout his work, Somani emphasizes correctness over rhetoric. He avoids making claims that cannot be substantiated and prioritizes technical accuracy even when it limits scope. This restraint is increasingly rare in a field driven by rapid iteration and public attention.
For hiring managers, research directors, and policy makers, his work offers a defensible framework for thinking about AI safety. Formal methods may not yet provide comprehensive guarantees for large models, but they offer a principled direction grounded in proof rather than conjecture.
As AI systems continue to shape critical decisions, the demand for verifiable guarantees will only grow. Neel Somani’s research demonstrates that while full verification remains a long term challenge, meaningful progress is already possible by applying rigorous tools to the parts of the system that matter most
