15 citations · 15 across the 8 of their papers we have counts for
9 papers
A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization
Qiuyi Qu, Yicheng Sui, Yufei Sun +5
GPU code optimization is a key performance bottleneck for HPC workloads as well as large-model training and inference. Although compiler optimizations and hand-written kernels can…
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
Jun Xu, Xinkai Du, Yu Ao +17
Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retriev…
RPTS: Tree-Structured Reasoning Process Scoring for Faithful Multimodal Evaluation
Haofeng Wang, Yu Zhang
Large Vision-Language Models (LVLMs) excel in multimodal reasoning and have shown impressive performance on various multimodal benchmarks. However, most of these benchmarks evaluat…
MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
Wenlong Wu, Haofen Wang, Bohan Li +3
Retrieval Augmented Generation (RAG) has emerged as a promising solution to address hallucination issues in Large Language Models (LLMs). However, the integration of multiple retri…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
Dalong Zhang, Jun Xu, Jun Zhou +16
In this paper, we introduce KAG-Thinker, which upgrade KAG to a multi-turn interactive thinking and deep reasoning framework powered by a dedicated parameter-light large language m…
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
Mingyang Chen, Linzhuang Sun, Tianpeng Li +10
Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external…
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin
Tianlin Guo, Lingling Zhang, Jiaxin Wang +4
Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than t…
Exploring Hybrid Question Answering via Program-based Prompting
Qi Shi, Han Cui, Haofeng Wang +3
Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heter…