# Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Python

arXiv: https://arxiv.org/abs/2511.18288 · HTML: https://mlsanity.com/paper/2511.18288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 14 of 14)

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- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs](https://mlsanity.com/paper/2412.21187/) (2024)
- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?](https://mlsanity.com/paper/2502.19361/) (2025)
- [What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces](https://mlsanity.com/paper/2503.05703/) (2025)
- [Python Symbolic Execution with LLM-powered Code Generation](https://mlsanity.com/paper/2409.09271/) (2024)
- [Scalpel: The Python Static Analysis Framework](https://mlsanity.com/paper/2202.11840/) (2022)
- [Integrating Symbolic Execution with LLMs for Automated Generation of Program Specifications](https://mlsanity.com/paper/2506.09550/) (2025)
- [Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models](https://mlsanity.com/paper/2506.08171/) (2025)
- [PALM: Path-aware LLM-based Test Generation with Comprehension](https://mlsanity.com/paper/2506.19287/) (2025)

## Abstract (arXiv)

Execution path reasoning is a key step towards program semantics understanding. It is crucial for generating test cases that cover certain branches/paths, or detecting bugs that are triggered by some paths without actually executing the program. Traditionally, execution path reasoning can be achieved by symbolic execution techniques, but existing SMT-based symbolic execution approaches struggle with complex data structures and external API calls. This challenge is even more pronounced in languages with highly flexible syntax, such as Python, resulting in a lack of widely adopted tools for reasoning on execution paths. Therefore, reasoning execution paths with AI-based approaches become a promising direction. In this paper, we investigate the feasibility of adopting large language models (LLMs) for execution path reasoning on Python, where traditional path-based symbolic execution tools are unavailable. We conduct an empirical study on two types of path reasoning tasks: generation tasks for test case generation and classification tasks for bug detection. We build new evaluation pipelines and benchmarks from both competition-level programs and real-world repositories. Our results show that state-of-the-art LLMs can perform correct reasoning on execution paths and improve test coverage on real-world software, though models with stronger reasoning abilities do not always outperform weaker ones. These findings highlight the potential of utilizing LLMs as a complementary heuristic for path-aware code reasoning, especially in program languages lacking mature symbolic execution tools. We have released our benchmark and evaluation scripts at https://github.com/jacobwwh/llm-path-study.