Untangling the Code: Detecting and Fixing AI-Generated Errors

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Author: Denis Avetisyan

A new framework uses deterministic analysis to reliably identify and automatically correct semantic errors in code created by large language models.

The framework addresses the inevitable problem of large language model hallucinations by proactively identifying and correcting fabricated content, acknowledging that even the most sophisticated models are prone to generating outputs inconsistent with established knowledge.
The framework addresses the inevitable problem of large language model hallucinations by proactively identifying and correcting fabricated content, acknowledging that even the most sophisticated models are prone to generating outputs inconsistent with established knowledge.

This paper presents a static analysis approach leveraging Abstract Syntax Trees to achieve 100% precision in detecting knowledge-conflicting hallucinations and 77.0% auto-correction accuracy.

Despite advances in code generation, large language models (LLMs) frequently introduce subtle semantic errors-knowledge-conflicting hallucinations-that evade typical linting and cause runtime failures. This paper, ‘Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis’, investigates a deterministic, static-analysis framework to reliably detect and auto-correct these errors by parsing generated code into an Abstract Syntax Tree (AST) and validating it against a dynamically-generated knowledge base. Our results demonstrate 100% precision in hallucination detection and 77.0% auto-correction accuracy, suggesting a viable alternative to probabilistic repair methods. Could this deterministic post-processing approach pave the way for truly trustworthy, LLM-generated code?

The Illusion of Intelligence: LLMs and the Hallucination Problem

The landscape of software development is undergoing a rapid transformation with the increasing adoption of large language models for code generation. These models offer an unprecedented level of automation, capable of producing functional code snippets, entire functions, and even complex applications from natural language prompts. This capability streamlines development workflows, accelerates prototyping, and lowers the barrier to entry for aspiring programmers. The potential benefits extend beyond simple productivity gains; LLMs are being leveraged to automate repetitive tasks, assist in debugging, and even generate code for specialized hardware, promising a future where software creation is significantly more efficient and accessible. However, this automation is not without its challenges, requiring careful consideration of code quality and reliability.

Large language models, while demonstrating impressive capabilities in code generation, are susceptible to a phenomenon known as ‘hallucination’. This doesn’t refer to visual or auditory experiences, but rather to the generation of code that, while technically valid in its syntax, produces logically incorrect results or contradicts established facts within the relevant domain. Essentially, the model confidently presents code that appears correct but is, in fact, semantically flawed – it may compile and run without errors, yet yield unintended or demonstrably false outputs. This is particularly concerning as these ‘knowledge conflicting hallucinations’ can be subtle and difficult to detect through standard testing methods, potentially leading to critical failures in applications where code reliability is paramount, such as safety-critical systems or scientific simulations.

The increasing reliance on Large Language Models for code generation introduces substantial risk, particularly within safety-critical systems where even subtle errors can have catastrophic consequences. Current LLMs, while proficient in producing syntactically correct code, are susceptible to ‘hallucinations’ – generating outputs that, despite appearing valid, fundamentally contradict established programming principles or domain-specific knowledge. This presents a critical need for automated error detection and correction; a challenge addressed by recent research demonstrating a deterministic framework capable of identifying and repairing these semantic flaws. This framework offers a pathway toward building more reliable and trustworthy AI-assisted coding tools, mitigating the potential for undetected errors and bolstering confidence in LLM-generated code across vital applications.

Knowledge is Power: A New Paradigm for Repair

The foundation of our repair system is a structured Knowledge Base (KB) designed to contain validated information pertaining to programming languages and their associated libraries. This KB is not simply a collection of documentation; it’s a formally organized repository where facts about APIs, function parameters, expected return types, and valid usage patterns are explicitly represented. Data within the KB undergoes a rigorous validation process, utilizing both automated checks against language specifications and manual review by domain experts, ensuring high accuracy and reliability. The KB’s structure enables efficient querying and reasoning, allowing the system to rapidly determine whether generated code conforms to established programming conventions and library requirements. This structured format is critical for identifying discrepancies and, ultimately, guiding the repair process.

Deterministic Repair addresses errors in Large Language Model (LLM)-generated code by integrating static analysis with knowledge validation. Static analysis, performed on the Abstract Syntax Tree, identifies structural issues and potential runtime errors without executing the code. This is then combined with a structured Knowledge Base – a repository of validated programming language and library information – to verify the LLM’s outputs. Discrepancies between the generated code and the Knowledge Base are flagged as errors, and the system deterministically applies corrections based on the validated knowledge, ensuring a predictable and reliable repair process.

Static analysis examines code structure without executing the program, leveraging the Abstract Syntax Tree (AST) representation to facilitate this process. The AST provides a hierarchical representation of the code’s syntactic structure, enabling the identification of potential errors such as syntax violations, type mismatches, and undefined variable usage. By traversing the AST, the system can verify code constructs against language rules and identify deviations before runtime, offering a preventative approach to error detection and contributing to improved code quality and reliability. This pre-execution analysis contrasts with dynamic analysis, which requires program execution to reveal errors.

The deterministic repair methodology achieves 100% precision in detecting knowledge-conflicting hallucinations, indicating a complete absence of false positive identifications. This metric signifies that every instance flagged as a knowledge conflict is, in fact, a genuine discrepancy between the generated code and validated information within the Knowledge Base. This level of precision is critical for ensuring the reliability of the repair process and avoiding unnecessary or incorrect code modifications, and is achieved through the combined use of static analysis and knowledge validation techniques.

Tracing the Flaws: Validating and Repairing with Structured Knowledge

The system utilizes a Knowledge Base to validate Application Programming Interface (API) calls present in generated code. This validation process confirms that each call conforms to the documented interfaces – including correct parameter types, required arguments, and expected return values – as defined within the Knowledge Base. By cross-referencing generated code against this established documentation, the system identifies deviations that indicate potential errors or incorrect implementations, thereby ensuring adherence to expected API behavior and preventing runtime failures due to mismatched interfaces.

The system identifies error origins by analyzing connections between code elements and the documented knowledge base. This process involves mapping code constructs – such as function calls, variable assignments, and control flow statements – to corresponding entries within the knowledge base, which details expected API behavior, data types, and valid relationships. Discrepancies between the code’s structure and the knowledge base’s documented relationships indicate potential errors; the system then traces these discrepancies to pinpoint the exact source of the issue, whether it’s an incorrect function parameter, a missing dependency, or a violation of established interface contracts. This relational analysis enables precise error localization, facilitating targeted repair strategies.

Structural Trimming is employed as a post-processing step to improve the reliability of code repair by identifying and removing code segments deemed potentially harmful or originating from model hallucinations. This technique operates by analyzing the abstract syntax tree of the generated code and evaluating the provenance and semantic consistency of individual code blocks. Blocks lacking clear support from the Knowledge Base or exhibiting anomalous control flow are flagged for removal, effectively reducing the risk of introducing errors or security vulnerabilities into the repaired code. The process prioritizes maintaining the overall functionality while eliminating unsupported or questionable code additions.

Evaluation of the automated repair system demonstrated successful correction of 124 out of 161 identified hallucinated code snippets. This translates to an overall auto-correction rate of 77.0%. The identified hallucinations were programmatically detected and rectified using knowledge-based validation and structural trimming techniques. This repair rate represents the system’s ability to autonomously address a significant proportion of generated code errors without human intervention, indicating a substantial improvement in code generation reliability.

The system demonstrated a 97.9% accuracy rate in correcting Missing Import errors, which represents the highest repair performance achieved across all identified Knowledge Conflicting Hallucination types. This indicates a strong capability to identify dependencies not explicitly included in the generated code but required for proper functionality, as documented within the Knowledge Base. The high success rate suggests effective utilization of the Knowledge Base to validate required imports and automatically insert them into the code, resolving the hallucination and ensuring the code’s executability.

The Living System: Building and Maintaining a Dynamic Knowledge Base

A truly useful knowledge base isn’t a static repository, but rather a living system that requires constant attention and improvement to remain relevant and accurate. The rapidly evolving nature of programming languages, frameworks, and best practices necessitates a continuous cycle of updates and refinements. Without ongoing maintenance, information inevitably becomes outdated, leading to decreased reliability and potentially flawed outputs. This proactive approach ensures the knowledge base doesn’t simply reflect the past, but accurately represents the current state of the field, bolstering its long-term value and establishing it as a trusted resource. Such dynamic upkeep is critical for maintaining a high level of precision and preventing the propagation of misinformation within the system.

A critical component of a robust knowledge base is the ability to readily incorporate new data and rectify inaccuracies, a process now significantly enhanced through an Automated Ingestion Pipeline. This system is designed to move beyond manual updates, automating the acquisition, validation, and integration of information from diverse sources. The pipeline accepts commonly used data formats, such as JSON, and utilizes the versatile Python ecosystem – including powerful libraries like Numpy, Pandas, Requests, and Matplotlib – to efficiently process and analyze incoming data. This streamlined approach not only accelerates the expansion of the knowledge base but also minimizes the potential for human error, ensuring that the information remains current, consistent, and reliable as the technological landscape evolves.

The system’s automated ingestion pipeline is built upon widely adopted data standards and a robust software foundation. It efficiently processes information encoded in JSON, a prevalent format for data interchange, and utilizes the versatile Python programming language for all stages of data handling. Core to its functionality are powerful Python libraries: Numpy facilitates numerical computation, Pandas enables streamlined data analysis and manipulation, Requests manages data acquisition from various sources, and Matplotlib provides visualization capabilities for quality control and pattern identification. This reliance on established tools and formats ensures both interoperability and scalability, allowing the knowledge base to readily incorporate new data and adapt to changing information landscapes with minimal friction.

Rigorous evaluation of the automated knowledge base system reveals a Detection F1-Score of 93.4% in identifying instances of Knowledge Conflicting Hallucinations. This metric signifies a robust ability to discern inconsistencies and inaccuracies within the accumulated data, indicating a high degree of reliability in the system’s responses. The F1-Score, a harmonic mean of precision and recall, demonstrates a balanced performance in both correctly identifying conflicting information and minimizing false positives – meaning the system avoids incorrectly flagging valid knowledge as erroneous. This level of accuracy is crucial for maintaining the integrity of the knowledge base and ensuring users receive consistently trustworthy information, particularly as the system scales and incorporates increasingly complex data sets.

The system’s architecture is fundamentally designed for resilience against the constant flux of the programming world. Rather than being a static repository, the knowledge base actively incorporates new information and resolves inconsistencies as they arise, ensuring its continued relevance. This adaptability stems from an automated ingestion pipeline that continuously monitors for updates in APIs, libraries, and best practices. By leveraging tools for data processing and analysis, the system doesn’t simply accumulate knowledge; it refines and validates it, effectively mitigating the risk of outdated or inaccurate information impacting performance. The result is a knowledge base that doesn’t just reflect the current state of programming, but actively learns and evolves alongside it, maintaining a consistently high level of accuracy and reliability over time.

The pursuit of flawless code generation, as this paper illustrates with its deterministic AST analysis, feels
 optimistic. It proposes a 100% precision in detecting hallucinations, a claim that brushes against experience. One anticipates production environments will inevitably reveal edge cases, unforeseen interactions, and the delightful chaos of real-world data. G. H. Hardy observed, “The most beautiful and profound things are always the most difficult to express.” This rings true; constructing a system to perfectly identify semantic errors-to formalize ‘correctness’-is a beautiful ambition, but one that invites scrutiny. Any auto-correction, even at 77.0%, is simply postponing the inevitable technical debt; a temporary reprieve before the system reveals its limitations, or, as one might say, hasn’t fully broken yet.

The Road Ahead

This work, predictably, doesn’t solve the problem of large language models inventing things. It merely pushes the failure point a little further down the line. A deterministic approach to hallucination detection is
 almost quaint. Like building a better slide rule after the invention of the computer. Still, 100% precision in detection is noteworthy. It suggests a potential ceiling on what even static analysis can achieve before the models simply generate code that looks correct but behaves
 creatively. The 77% auto-correction rate is
 acceptable. It’s a reminder that even ‘intelligent’ systems often require a human to clean up the mess.

Future efforts will inevitably focus on scaling this approach-handling larger codebases, more complex hallucinations, and, crucially, different programming languages. The real challenge, however, isn’t technical. It’s acknowledging that these models are probabilistic parrots, not reasoning engines. We don’t write code; we leave notes for digital archaeologists. Perhaps the next breakthrough won’t be better detection, but a robust system for auditing and explaining why the model made a particular error.

One can anticipate a proliferation of ‘hallucination-resistant’ architectures, each promising salvation. The field will cycle through buzzwords-‘trustworthy AI’, ‘explainable generation’-until the next shiny object appears. It’s a comforting pattern. After all, if a system crashes consistently, at least it’s predictable. And ‘cloud-native’ just means the same mess, but more expensive.

Original article: https://arxiv.org/pdf/2601.19106.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-29 01:40