16 citations · 58 across the 34 of their papers we have counts for
38 papers
MemScope: Evolving Agent Memory Architectures through Localized Code Repair
Miao Su, Yucan Guo, Zixuan Li +4
Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their adaptability across task typ…
RAdapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
Yucan Guo, Miao Su, Saiping Guan +6
Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla RAG efficiently handles simp…
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
Yucan Guo, Xiaohan Wang, Miao Su +8
Tool-Integrated Reasoning (TIR) is a fundamental capability for LLM agents to solve complex tasks by interacting with external tools iteratively. Reinforcement Learning (RL) has be…
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents
Miao Su, Yucan Guo, Zhongni Hou +8
Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization. However, existing methods fail…
Revisiting Task-Oriented Dataset Search in the Era of Large Language Models: Challenges, Benchmark, and Solution
Zixin Wei, Yucan Guo, Jinyang Li +3
The search for suitable datasets is the critical "first step" in data-driven research, but it remains a great challenge. Researchers often need to search for datasets based on high…
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning
Yucan Guo, Miao Su, Saiping Guan +4
Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent pr…
StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning
Yucan Guo, Saiping Guan, Miao Su +4
Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited. Sparse KG reasoning, the task of inferring missin…
A Survey of Link Prediction in N-ary Knowledge Graphs
Jiyao Wei, Saiping Guan, Da Li +3
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fac…
KnowCoder-V2: Deep Knowledge Analysis
Zixuan Li, Wenxuan Liu, Long Bai +13
Deep knowledge analysis tasks always involve the systematic extraction and association of knowledge from large volumes of data, followed by logical reasoning to discover insights.…
G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models
Long Bai, Zixuan Li, Xiaolong Jin +3
Forecasting over Temporal Knowledge Graphs (TKGs) which predicts future facts based on historical ones has received much attention. Recent studies have introduced Large Language Mo…
Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs
Zhongni Hou, Miao Su, Xiaolong Jin +4
Temporal Knowledge Graphs (TKGs), which utilize quadruples in the form of (subject, predicate, object, timestamp) to describe temporal facts, have attracted extensive attention. N-…
Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution
Jizhao Zhu, Akang Shi, Zixuan Li +4
In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solutio…
Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction
Wenxuan Liu, Zixuan Li, Long Bai +5
Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from…
KnowCoder-X: Boosting Multilingual Information Extraction via Code
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu +7
Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual alignment. However, although LLMs show promising cross-lingual alignment in Information Extraction (IE), a…
Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs
Saiping Guan, Jiyao Wei, Xiaolong Jin +2
Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated…
Temporal Knowledge Graph Question Answering: A Survey
Miao Su, Zixuan Li, Zhuo Chen +3
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a…
Class-Incremental Few-Shot Event Detection
Kailin Zhao, Xiaolong Jin, Long Bai +2
Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classe…
Self-Improvement Programming for Temporal Knowledge Graph Question Answering
Zhuo Chen, Zhao Zhang, Zixuan Li +4
Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs). The core challenge of this task lies in und…
Selective Temporal Knowledge Graph Reasoning
Zhongni Hou, Xiaolong Jin, Zixuan Li +3
Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reas…
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction
Zixuan Li, Yutao Zeng, Yuxin Zuo +14
In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified…
Retrieval-Augmented Code Generation for Universal Information Extraction
Yucan Guo, Zixuan Li, Xiaolong Jin +8
Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts, which brings challenges to existing methods due to…
An In-Context Schema Understanding Method for Knowledge Base Question Answering
Yantao Liu, Zixuan Li, Xiaolong Jin +5
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown stron…
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
Zhilei Hu, Zixuan Li, Daozhu Xu +5
Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes,…