CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

Daria Voronkova; Ilya Trofimov; Anton Dmitriev; Eduard Tulchinskii; Evgeny Burnaev; Serguei Barannikov

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arXiv · 2609.07779

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

Daria Voronkova·Ilya Trofimov·Anton Dmitriev·Eduard Tulchinskii·Evgeny Burnaev·Serguei Barannikov

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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.

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Daria Voronkova, Ilya Trofimov, Anton Dmitriev, Eduard Tulchinskii, Evgeny Burnaev, Serguei Barannikov. 2026-09-07. CodeTD: Topology of Attention Detects Hallucinations in Code LLMs. https://arxiv.org/abs/2609.07779

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