mlsanityData to 7 Oct 2026

CMind: An AI Agent for Localizing C Memory Bugs

arXiv 2601.14434 ↗First posted 20 Jan 2026cs.SEcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

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.

Key facts. No citing papers in the map yet (data through 7 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.

Builds on

13 of 35 references.

References · 13

  1. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  2. 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystackcited by 211
  3. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  4. 2024AgentFL: Scaling LLM-based Fault Localization to Project-Level Contextcited by 47
  5. 2025CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Taskscited by 24
  6. 2022Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detectioncited by 18
  7. 2025Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Contextcited by 9
  8. 2025When Deep Learning Meets Information Retrieval-based Bug Localization: A Surveycited by 6
  9. 2023On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localizationcited by 4
  10. 2025The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Modelscited by 4
  11. 2025Do Code LLMs Do Static Analysis?cited by 2
  12. 2023ACER: An AST-based Call Graph Generator Frameworkcited by 1
1 more
  1. 2025Human Attention During Localization of Memory Bugs in C Programscited by 1

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  1. 2026Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition
  2. 2026Agentic Fuzzing: Opportunities and Challenges
  3. 2026CNnotator: LLM-Guided Memory Safety Annotation Synthesis
  4. 2026Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads
  5. 2026LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces
  6. 2026SPARC: Scenario Planning and Reasoning for Automated C Unit Test Generation
  7. 2025Automated Repair of C Programs Using Large Language Models
  8. 2024MarsCode Agent: AI-native Automated Bug Fixing