17 citations · 18 across the 4 of their papers we have counts for
5 papers
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…
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…
AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation
Ali Reza Ibrahimzada, Kaiyao Ke, Mrigank Pawagi +4
Code translation transforms programs from one programming language (PL) to another. Several rule-based transpilers have been designed to automate code translation between different…
Probeable Problems for Beginner-level Programming-with-AI Contests
Mrigank Pawagi, Viraj Kumar
To broaden participation, competitive programming contests may include beginner-level problems that do not require knowledge of advanced Computer Science concepts (e.g., algorithms…
GuardRails: Automated Suggestions for Clarifying Ambiguous Purpose Statements
Mrigank Pawagi, Viraj Kumar
Before implementing a function, programmers are encouraged to write a purpose statement i.e., a short, natural-language explanation of what the function computes. A purpose stateme…