4 papers
SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
Qiao Xiao, Haochen Shi, Yisen Gao +9
Large language model (LLM) agents increasingly rely on agent harnesses that manage context, tools, and multi-turn execution, making tools a central interface for acting in realisti…
From Tokens to Regions: CUDA-Sensitive Instruction Tuning for GPU Kernel Generation
Wentao Chen, Jiace Zhu, Xing Zhe Chai +4
High-performance CUDA kernels are essential for scalable AI systems, while Large Language Models (LLMs) still struggle to generate correct kernels due to strict and implicit execut…
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
Hong Ting Tsang, Jiaxin Bai, Haoyu Huang +5
Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered…
TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation
Qiao Xiao, Hong Ting Tsang, Jiaxin Bai
Graph-based Retrieval-augmented generation (RAG) has become a widely studied approach for improving the reasoning, accuracy, and factuality of Large Language Models (LLMs). However…