In the rapidly evolving world of Large Language Models (LLMs), a quiet but critical tug-of-war is taking place over how we understand these complex systems. The field of mechanistic interpretability, the science of reverse-engineering how AI thinks, finds itself pulled between competing visions. On one side, pragmatists argue for empirical feedback and grounding work in real models, even if understanding remains partial. On the other, ambitious theorists bet on identifying the deep, necessary circuits of intelligence, hoping for a generalized understanding across models.
Neel Somani, founder of Eclipse Labs and a researcher with a background in quantitative finance and computer security, argues that this divide misses the point. The real issue isn’t just about methods; it is about the lack of an agreed-upon end goal.
The field is rich with tools, feature labeling, circuit discovery, causal interventions, but these methods are often loosely coordinated. Somani suggests that to truly harness the power of AI while ensuring safety, the industry must move beyond storytelling and scientific curiosity. The true “telos,” or ultimate purpose of mechanistic interpretability, must be debuggability.
The Search for a Unified Goal
Currently, interpretability research oscillates between three distinct ideals, none of which fully capture what is necessary for reliable AI systems.
First is the goal of legibility, or the desire to produce explanations that humans can understand. Success here looks like a coherent story about why a model acted a certain way. However, Somani points out that a plausible story is not the same as a reliable control mechanism. Explanations that sound correct to a human observer often crumble under counterfactual intervention, offering no real handle for fixing errors.
Second is the ideal of scientific understanding, where researchers use interventions to test hypotheses and identify causal structures. While this mirrors the scientific method used in the natural world, Neel Somani notes a critical distinction: LLMs are not natural objects; they are engineered artifacts. Treating them purely as subjects of observation leaves a powerful affordance on the table. We don’t just need to understand them; we need to be able to modify them while preserving their specified properties.
The third perspective is capability enhancement, where interpretability is valued only if it accelerates optimization. The natural endpoint of this philosophy is systems that are maximally effective but maximally opaque, a dangerous equilibrium for safety-critical software.
Neel Somani proposes that the field should instead orient itself toward debuggability. This approach subsumes the need for explanation and understanding but adds the critical engineering requirements of localization, predictable intervention, and certification.
The Three Pillars of AI Debuggability
In Somani’s framework, debugging an LLM is an engineering discipline that proceeds through three strictly escalating stages: localization, intervention, and certification.
Localization is the first step. It requires identifying exactly which internal mechanisms are responsible for a specific behavior. Crucially, this goes beyond correlation. A debuggable localization must support counterexample search within a bounded domain. An engineer must be able to determine if a behavior can occur without the mechanism, or if the mechanism can fire without producing the behavior.
Once a failure is localized, the next requirement is intervention. This is the surgical aspect of debugging. If a specific head or subspace is identified as the culprit for a hallucination or safety failure, we must be able to modify or constrain it. The intervention should remove the undesired behavior without causing collateral damage elsewhere in the domain.
The final and most rigorous goal is certification. This involves making exhaustive, falsifiable claims about model behavior. For a formally specified domain, certification might mean proving that a model cannot bypass a specific guard layer unless a distinct feature is active. Neel Somani argues that this shifts the standard from “we haven’t seen it fail” to “we can prove it won’t fail within these parameters.”
What a Debuggable Explanation Promises
It is important to clarify what debuggability is not. Neel Somani emphasizes that this approach does not imply that a massive neural network can be cleanly de-compiled into a single symbolic program. Transformer models exploit continuous geometry and high-dimensional spaces; any realistic abstraction must preserve this complexity rather than erase it.
Furthermore, debuggability does not aim for global safety proofs. The input space of frontier LLMs is too vast to prove that a model will “never” produce harmful output. Instead, Somani likens the process to debugging complex software like a web browser. One cannot prove that Chrome will never crash, but one can prove that specific routines are memory-safe and that critical invariants are preserved during updates.
For LLMs, meaningful debuggability consists of creating a family of verified, compositional abstractions. These are partial and local, but they are exact where they apply. This allows engineers to make confident claims: “This mechanism, on this domain, behaves this way, and if it didn’t, we would know.”
The Necessity of Formal Methods
To achieve this level of precision, Neel Somani argues that the field must embrace formal methods. Tools like SMT solvers and neural verification frameworks are not optional add-ons; they are the only way to make precise claims about impossibility and closure under intervention.
There is already precedent for this. Research into sparse circuit extraction has shown that models contain algorithmic subcircuits that remain stable under intervention. Symbolic Circuit Distillation has demonstrated that neural mechanisms can be proven formally equivalent to symbolic programs. These results suggest that the barrier to verifiable AI is not theoretical impossibility, but rather engineering integration and scale.
A Vision for De-compilation
Somani outlines a potential pipeline for “de-compiling” neural networks into debuggable objects. The process begins by identifying stable linear regions—essentially local programs within the model defined by explicit guard conditions.
Next, these regions are factored into meaningful subspaces. Instead of blunt interventions that ablate entire sections of the network, subspaces allow for surgical editing of specific directions, such as sentiment or syntactic markers.
Finally, these components are composed into formally verifiable causal circuits. These circuits cease to be mere explanatory stories; they become objects that engineers can edit, reason about, and certify. If a bypass exists, formal search will find it.
Interpretability as Control
Ultimately, Neel Somani’s perspective shifts the conversation from passive observation to active engineering. The central question for the future of AI is not just whether we can understand these systems, but whether that insight can support reliable, bounded control.
By moving toward a “telos” of debuggability, the industry can create a patchwork of verified abstractions. This approach transforms mechanistic interpretability from an academic exercise into a crucial tool for building safe, reliable, and robust enterprise systems.
About the Expert
Neel Somani is a researcher and entrepreneur focused on the intersection of cryptography, machine learning, and quantitative finance. A graduate of UC Berkeley with a triple major in math, computer science, and business, Somani previously worked as a quantitative researcher at Citadel and a software engineer at Airbnb. In 2022, he founded Eclipse Labs, raising $65M to build Ethereum’s fastest L2. He is currently focused on philanthropy and machine learning research.