3 citations · 6 across the 9 of their papers we have counts for
9 papers
CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
Tara Saba, Zhiyang Chen, Jikai Jason Li +3
High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process. Recent work has explored using LLMs to au…
Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs
Zhiyang Chen, Tara Saba, Xun Deng +2
Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training introduces a significant security risk: t…
Towards Repository-Level Program Verification with Large Language Models
Si Cheng Zhong, Xujie Si
Recent advancements in large language models (LLMs) suggest great promises in code and proof generations. However, scaling automated formal verification to real-world projects requ…
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Honghua Dong, Jiacheng Yang, Xun Deng +4
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, the…
-Bench: Evaluating Conversational Agents in a Dual-Control Environment
Victor Barres, Honghua Dong, Soham Ray +2
Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a pa…
VerifyThisBench: Generating Code, Specifications, and Proofs All at Once
Xun Deng, Sicheng Zhong, Barış Bayazıt +3
Large language models (LLMs) have demonstrated remarkable progress in code generation, but many existing benchmarks are approaching saturation and offer little guarantee on the tru…
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation
Sicheng Zhong, Jiading Zhu, Yifang Tian +1
Scaling automated formal verification to real-world projects requires resolving cross-module dependencies and global contexts, which are challenges overlooked by existing function-…
Assessing Code Generation with Intermediate Languages
Xun Deng, Sicheng Zhong, Honghua Dong +4
Intermediate step methodologies like chain of thoughts (COT) have demonstrated effectiveness in enhancing the performance of Large Language Models (LLMs) on code generation. This s…
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
Hao Tang, Keya Hu, Jin Peng Zhou +4
Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too comple…