Author

Ravishka Rathnasuriya

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Preprint Oct 2026

Beyond Confidence: Rethinking Uncertainty Calibration in Deep Code Models

Deep learning models have shown significant promise in code-related tasks. While confidence calibration techniques have been proposed to improve model reliability, their effectiveness in real-world tasks with strict accuracy requirements remains uncertain. In this paper, we investigate two key questions: first, whether...

Ravishka Rathnasuriya, Wei Yang · 0 citations

#artificial intelligence Preprint Oct 2026

Characterizing Overconfident Failure in LLM-Based Code Generation

Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based correctness checks. Existing validation methods, such as testing and program analysis, remain essential but are often incomplete, costly, or app...

Ravishka Rathnasuriya, Wei Yang · 0 citations

Jul 2026

Correct but Slow: An Empirical Study of the GPU Kernel Evaluation Gap in Modern Domain-Specific Languages

This study studies whether correctness-based evaluation identifies kernels unsuitable as library replacements, why such failures occur, and how they can be detected without exhaustive benchmark coverage.

Tingxi Li, Ravishka Rathnasuriya, Wei Yang · arXiv.org · 0 citations