arXiv:2606.17648v1 Announce Type: new Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code reasoning in which models first brew the answer, making it linearly recoverable many layers before it becomes self-decodable, and then diverge into one of four resolution outcomes: Resolved, Overprocessed, Misresolved, or Unresolved. Understanding this lifecycle matters because similar task accuracies can mask fundamentally different failure modes that surface-level evaluation cannot detect.
The rapid advancement and widespread deployment of large language models heighten the need for deeper understanding of their internal reasoning processes, especially as they tackle complex tasks like code generation and analysis.
Understanding the internal lifecycle and failure modes of LLMs in code reasoning is crucial for building more robust, reliable, and explainable AI systems, impacting their trustworthiness and applicability in critical domains.
This research provides a more nuanced framework for evaluating LLM performance beyond surface-level accuracy, enabling targeted improvements and better diagnostic capabilities for AI developers.
- · AI developers
- · ML researchers
- · Software engineering firms
- · Cybersecurity sector
- · Companies relying on uninspected black-box LLM code
- · Developers without detailed debugging tools for LLMs
Improved diagnostic tools and methodologies for understanding LLM internal states will emerge.
This deeper insight will lead to the development of more resilient and auditable AI agents capable of complex logical tasks.
Enhanced LLM explainability in code reasoning could accelerate autonomous agent adoption in high-stakes software development and critical infrastructure management.
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Read at arXiv cs.AI