CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

• URL: http://arxiv.org/abs/2609.07779v1
• Date: Mon, 07 Sep 2026 17:26:13 GMT
• Title: CodeTD: Topology of Attention Detects Hallucinations in Code LLMs
• Abstract summary: We introduce CodeTD -- the first approach to pre-execution assessment of code correctness based on topological data analysis.<n>Our method quantifies prompt-generation mismatch using topological patterns of attention maps.<n>We carry out experiments with common benchmarks (HumanEval, MBPP, BigCodeBench, MultiPL-E), 5 programming languages and 10 Code LLMs of size up to 34B parameters.
• Score: 27.185430677659667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As AI-code assistant tools become widespread, automatic assessment of the correctness of generated code becomes a significant challenge. Code LLMs are prone to hallucinations, which may lead to code that does not solve the required problem, or even to code with severe security vulnerabilities. In this paper, we introduce CodeTD -- the first approach to pre-execution assessment of code correctness based on topological data analysis (TDA) of Code LLMs' attention maps. Our method quantifies prompt-generation mismatch using topological patterns of attention maps. We carry out experiments with common benchmarks (HumanEval, MBPP, BigCodeBench, MultiPL-E), 5 programming languages and 10 Code LLMs of size up to 34B parameters. The experimental results show that the proposed method outperforms recent baselines. Moreover, CodeTD is transferable between coding benchmarks.

Related papers

• Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding [40.63304781183767]
  Generative large language models (LLMs) are often reported as weak medical coders.<n>We present a controlled empirical study of post-training for generative ICD coding.
  arXiv  Detail & Related papers  (2026-06-11T22:04:50Z)
• Inferring Code Correctness from Specification [0.0]
  Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale.<n>We propose TRAILS (Targeted Reasoning Agreement via Inputs and Specifications), an approach that grounds LLM reasoning with concrete (input, output) pairs.<n>We evaluate TRAILS on two datasets, LiveCodeBench and CoCoClaNeL, across three LLMs (Qwen3Coder-30B, Devstral-Small-24B, and Olmo3.1-Instruct), comparing against HoarePrompt and a Zero-Shot Chain-of-Thought baseline.
  arXiv  Detail & Related papers  (2026-05-28T12:04:51Z)
• CodeSimpleQA: Scaling Factuality in Code Large Language Models [55.705748501461294]
  We present CodeSimpleQA, a comprehensive benchmark designed to evaluate the factual accuracy of code LLMs in answering code-related questions.<n>We also create CodeSimpleQA-Instruct, a large-scale instruction corpus with 66M samples, and develop a post-training framework combining supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-22T14:27:17Z)
• A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI [54.34738767990601]
  As Large Language Models become increasingly integrated into software engineering tasks, understanding and mitigating hallucination in code becomes essential.<n>We provide a systematic review of hallucination phenomena in code-oriented LLMs from four key perspectives.
  arXiv  Detail & Related papers  (2025-11-02T02:58:41Z)
• Is Compression Really Linear with Code Intelligence? [60.123628177110206]
  textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
  arXiv  Detail & Related papers  (2025-05-16T16:59:14Z)
• An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models [6.976968804436321]
  This paper proposes a novel approach to embedding source code by combining large language and sentence embedding models.<n>To evaluate the performance of our proposed approach, we conducted a series of experiments on three datasets with different programming languages.
  arXiv  Detail & Related papers  (2024-09-23T01:03:15Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [92.62952504133926]
  This study evaluated the performance of three leading closed-source LLMs and six popular open-source LLMs on three commonly used benchmarks.<n>We developed a taxonomy of bugs for incorrect codes and analyzed the root cause for common bug types.<n>We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants.<n>Our results demonstrate a significant improvement over existing SOTA synthetic content detectors.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification [73.66920648926161]
  We introduce the concept of code hallucinations and propose a classification method for code hallucination based on execution verification.<n>We present a dynamic detection algorithm called CodeHalu designed to detect and quantify code hallucinations.<n>We also introduce the CodeHaluEval benchmark, which includes 8,883 samples from 699 tasks, to systematically and quantitatively evaluate code hallucinations.
  arXiv  Detail & Related papers  (2024-04-30T23:56:38Z)
• Fixing Large Language Models' Specification Misunderstanding for Better Code Generation [13.494822086550604]
  muFiX is a novel prompting technique to improve the code generation performance of large language models (LLMs)<n>It first exploits test case analysis to obtain specification understanding and enables a self-improvement process.<n>muFiX further fixes the specification understanding towards the direction reducing the gap between the provided understanding and the actual understanding.
  arXiv  Detail & Related papers  (2023-09-28T02:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.