arXiv:2607.08339v1 Announce Type: new Abstract: State-of-the-art code models achieve impressive performance, yet the extent to which they internally encode type information remains poorly understood. We probe the residual streams of pretrained code models for internal type representations using a parallel dataset of Java and Python code examples. Our results show that cross-lingual type representations emerge even from untyped code. Moreover, we test whether hidden states linearly encode the result type implied by typed function application by training probes on one language to infer argument
The proliferation of sophisticated code models necessitates deeper understanding of their internal representations to enhance capabilities and reliability.
Understanding how code models internally encode type information is crucial for developing more robust, verifiable, and explainable AI in software development, impacting efficiency and security.
This research provides a methodology to analyze internal code model states, which can lead to improvements in cross-lingual code generation, debugging, and program understanding tools.
- · AI/ML researchers
- · Software developers
- · Code model developers
- · Cybersecurity professionals
- · Manual code reviewers (long-term)
Improved understanding of how pre-trained code models handle fundamental linguistic and structural properties of code.
Development of more accurate and bug-resistant AI-assisted coding tools and automated code generation systems.
Accelerated evolution of code models towards higher levels of abstraction and potentially self-improving code generation, blurring the lines between human and AI programmers.
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Read at arXiv cs.CL