4 papers
Self-Decomposing Diffusion for Code Generation
Lize Shao, Zichen Xie, Zhizhen Chen +2
Diffusion language models (DLMs) are emerging as a promising paradigm for code generation, offering the potential for fast, parallel decoding. However, generating complex programs…
Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning
Zichen Xie, Mrigank Pawagi, Lize Shao +2
Model specifications define how large language models (LLMs) should behave, guiding alignment training, inference-time behavior, and evaluation. Yet these specifications may themse…
Constrained Code Generation with Discrete Diffusion
Lize Shao, Michael Cardei, Zichen Xie +2
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and ena…
VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
Zichen Xie, Mrigank Pawagi, Yuxin Liu +5
Large language models can generate useful code from natural language, but their outputs come without correctness guarantees. Verifiable code generation offers a path beyond testing…