A George Mason University computer scientist is launching a five-year effort to make artificial intelligence systems that generate computer code more understandable, scalable and useful in practice. Ziyu Yao, an assistant professor of computer science in the university’s College of Engineering and Computing, has received $674,100 from the National Science Foundation for a CAREER project titled “Towards Scalable and Actionable Interpretability of Language Models for Code Generation.” The research is scheduled to begin in July 2026 and conclude in late June 2031.

The project addresses a problem that has become increasingly urgent as language models move from experimental chatbots into software development, data analysis and information-retrieval systems. These models can produce code in seconds, but their internal decision-making processes remain difficult to examine. Researchers often know what a model has generated without knowing which internal representations, learned patterns or reasoning pathways led to the result. That gap creates challenges for reliability, debugging, safety and the responsible deployment of AI-generated software.

Yao’s research focuses on mechanistic interpretability, a field that attempts to connect a model’s observable behavior with the computational mechanisms operating inside its neural network. Rather than treating a language model as an entirely opaque statistical system, mechanistic interpretability seeks to identify the internal components that perform recognizable functions. These may include circuits that track syntax, retrieve factual or semantic knowledge, follow a sequence of logical steps or transform a natural-language instruction into executable code.

The scale of modern language models makes that analysis particularly difficult. A model may contain billions of parameters and thousands of interacting layers, while a single response can depend on many overlapping processes. Examining every internal mechanism is often too expensive and can produce information that is technically interesting but of limited practical value. Yao’s proposed framework will therefore concentrate on the mechanisms most relevant to downstream applications, with code generation serving as a structured environment in which those mechanisms can be studied more precisely.

A central feature of the project is the use of language-model agents to assist researchers with interpretation itself. These agents could help organize activation patterns, compare model behaviors, generate hypotheses about internal operations and test whether a proposed mechanism is consistently associated with a particular output. The goal is not to replace human researchers, but to create an interactive analysis process capable of handling more evidence than conventional manual inspection. If successful, the approach could make interpretability studies more efficient while keeping the results connected to concrete model behavior.

Code generation offers an especially valuable testing ground because computer programs contain explicit rules and measurable structures. A generated program can be evaluated for syntactic validity, functional correctness, security vulnerabilities and adherence to a user’s specifications. This gives researchers multiple ways to compare what a model appears to know with how it applies that knowledge. A model may produce grammatically correct code that fails logically, recall an appropriate programming pattern but apply it in the wrong context, or arrive at a correct solution through a process that is difficult to verify.

Yao plans to investigate three broad classes of mechanisms involved in code generation. The first concerns syntactic rule application, such as the correct arrangement of operators, declarations, control structures and function calls. The second involves semantic knowledge recall, including a model’s ability to connect programming concepts with libraries, APIs, data structures and established implementation patterns. The third concerns deliberate reasoning, in which the model may decompose a task, track intermediate steps and select a solution strategy before producing code.

The research will also examine how these mechanisms are shaped by the data used to train language models and by different learning paradigms. Training data can influence which programming languages, libraries and coding conventions a model recognizes, while the quality and distribution of that data may affect whether the model learns robust principles or relies on superficial correlations. Different training methods, including forms of supervised learning and feedback-driven optimization, may alter how models retrieve information, follow instructions or reason through multi-step programming tasks. Understanding those effects could help researchers distinguish generalizable capabilities from brittle memorization.

The project’s final goal is to turn interpretation into a tool for improving models rather than simply describing them. If researchers can identify why a model repeatedly generates insecure code, misuses an API or loses track of an intermediate requirement, they may be able to design targeted interventions. Such interventions could involve changing training examples, adjusting learning objectives, modifying decoding strategies or introducing safeguards focused on specific internal behaviors. Interpretation-informed improvement could make code-generation systems more accurate and more transparent without requiring every problem to be addressed through larger models or more data.

The educational component will extend the project beyond the laboratory. Outcomes will be incorporated into AI literacy outreach programs, early research opportunities for undergraduates, new curriculum materials and open-source educational resources. These initiatives are intended to help students and the broader public understand not only what AI systems can do, but also how researchers evaluate their reliability and limitations. As code-generating tools become part of everyday software work, the ability to question, test and interpret their outputs may become as important as the ability to use them.

George Mason University is Virginia’s largest public research university, enrolling more than 40,000 students from all 50 states and 130 countries. The university has emphasized research, innovation, entrepreneurship and accessibility as it has expanded over the past half century. Yao’s NSF-supported project places those priorities within one of the fastest-moving areas of computer science: developing AI systems that can explain their behavior and improve through scientific examination rather than remaining inaccessible black boxes.

Subject of Research: Mechanistic interpretability of language models for code generation, including syntax, semantic knowledge retrieval, reasoning, training data and model improvement.

Article Title: George Mason Researcher Receives NSF Funding to Reveal How AI Models Generate Code

Web References: https://www.gmu.edu/about ; https://www.gmu.edu/masonnow

References: National Science Foundation CAREER project, “Towards Scalable and Actionable Interpretability of Language Models for Code Generation.”

Keywords

Artificial intelligence, language models, code generation, mechanistic interpretability, AI research, computer science, neural networks, software development, National Science Foundation, George Mason University

Tags: AI code generation interpretabilityAI debugging and safetyAI model decision-making transparencyAI system reliability and trustworthinessAI-driven software engineeringexplainable AI in software developmentlanguage models for code synthesisneural network internal mechanismsneural network mechanistic interpretabilityNSF CAREER award in AI researchresponsible AI deploymentscalable language model understanding