# Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis

arXiv: https://arxiv.org/abs/2601.19106 · HTML: https://mlsanity.com/paper/2601.19106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges](https://mlsanity.com/paper/2504.20799/) (2025)
- [Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness](https://mlsanity.com/paper/2508.14419/) (2025)

## Built on by (most cited first, 1 of 1)

- [Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing](https://mlsanity.com/paper/2606.25332/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-existent API parameters, that evade linters and cause runtime failures. Existing mitigations like constrained decoding or non-deterministic LLM-in-the-loop repair are often unreliable for these errors. This paper investigates whether a deterministic, static-analysis framework can reliably detect textitand auto-correct KCHs. We propose a post-processing framework that parses generated code into an Abstract Syntax Tree (AST) and validates it against a dynamically-generated Knowledge Base (KB) built via library introspection. This non-executing approach uses deterministic rules to find and fix both API and identifier-level conflicts. On a manually-curated dataset of 200 Python snippets, our framework detected KCHs with 100% precision and 87.6% recall (0.934 F1-score), and successfully auto-corrected 77.0% of all identified hallucinations. Our findings demonstrate that this deterministic post-processing approach is a viable and reliable alternative to probabilistic repair, offering a clear path toward trustworthy code generation.