works on

From the 1 of 17 linked papers with an AI index.

activity
20242026
most citedUnderstanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

6 citations · 6 across the 4 of their papers we have counts for

collaborators

17 papers

math.CO2026

Optimal local convergence criteria for integer and Gaussian integer continued fractions

Ian Short, Margaret Stanier, Matty van Son +1

The objective of this work is to determine optimal local restrictions on the coefficients of integer and Gaussian integer continued fractions that imply convergence. We identify al…

cs.SE2026

Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs

Xiaoning Ren, Yinxing Xue, Lei Ma +1

The paper presents Code-MUE, a black‑box method that measures the uncertainty of code‑generating large language models by building execution‑based semantic interaction graphs and c…

cs.SE20266 cited

Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

Shengming Zhao, Yuchen Shao, Yuheng Huang +4

Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges w…

cs.SE2026

Foundation Models for Autonomous Driving System: An Initial Roadmap

Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8

Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…

cs.CL2026

Evaluating Implicit Regulatory Compliance in LLM Tool Invocation via Logic-Guided Synthesis

Da Song, Yuheng Huang, Boqi Chen +4

The integration of large language models (LLMs) into autonomous agents has enabled complex tool use, yet in high-stakes domains, these systems must strictly adhere to regulatory st…

cs.SE2025

Evaluating LLMs on Sequential API Call Through Automated Test Generation

Yuheng Huang, Jiayang Song, Da Song +4

By integrating tools from external APIs, Large Language Models (LLMs) have expanded their promising capabilities in a diverse spectrum of complex real-world tasks. However, testing…