# CMind: An AI Agent for Localizing C Memory Bugs

arXiv: https://arxiv.org/abs/2601.14434 · HTML: https://mlsanity.com/paper/2601.14434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 13 of 13)

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- [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://mlsanity.com/paper/2406.10149/) (2024)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [AgentFL: Scaling LLM-based Fault Localization to Project-Level Context](https://mlsanity.com/paper/2403.16362/) (2024)
- [CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks](https://mlsanity.com/paper/2507.05269/) (2025)
- [Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection](https://mlsanity.com/paper/2212.08108/) (2022)
- [Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context](https://mlsanity.com/paper/2510.20229/) (2025)
- [The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models](https://mlsanity.com/paper/2501.09653/) (2025)
- [When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey](https://mlsanity.com/paper/2505.00144/) (2025)
- [On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization](https://mlsanity.com/paper/2310.08083/) (2023)
- [Do Code LLMs Do Static Analysis?](https://mlsanity.com/paper/2505.12118/) (2025)
- [ACER: An AST-based Call Graph Generator Framework](https://mlsanity.com/paper/2308.15669/) (2023)
- [Human Attention During Localization of Memory Bugs in C Programs](https://mlsanity.com/paper/2506.00693/) (2025)

## Abstract (arXiv)

This demonstration paper presents CMind, an artificial intelligence agent for localizing C memory bugs. The novel aspect to CMind is that it follows steps that we observed human programmers perform during empirical study of those programmers finding memory bugs in C programs. The input to the tool is a C program's source code and a bug report describing the problem. The output is the tool's hypothesis about the reason for the bug and its location. CMind reads the bug report to find potential entry points to the program, then navigates the program's source code, analyzes that source code, and generates a hypothesis location and rationale that fit a template. The tool combines large language model reasoning with guided decision making we encoded to mimic human behavior. The video demonstration is available at https://youtu.be/_vVd0LRvVHI.