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20212026
most citedTemporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

14 citations · 46 across the 27 of their papers we have counts for

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11 papers · 1 filter

cs.AI2026

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…

cs.AI2026

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

Weiwei Ding, Zixuan Li, Long Bai +7

Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frame…

cs.AI2026

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…

cs.AI2025

KnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA

Zhuo Chen, Fei Wang, Zixuan Li +6

Knowledge Base Question Answering (KBQA) aims to answer natural-language questions over a structured Knowledge Base (KB). Recent work improves KBQA by adopting an agentic reasoning…

cs.AI2025

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.…

cs.AI2025

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-…

cs.AI2023★ 1 cited

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…

cs.AI2022★ 3 cited

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

Zixuan Li, Zhongni Hou, Saiping Guan +7

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\emph{subject}, \emph{relation}, \emph{object}, \emph{time…

cs.AI2022★ 8 cited

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

Zixuan Li, Saiping Guan, Xiaolong Jin +7

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG seq…

cs.AI2021★ 3 cited

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Zixuan Li, Xiaolong Jin, Saiping Guan +4

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenge…

cs.AI2021★ 14 cited

Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

Zixuan Li, Xiaolong Jin, Wei Li +5

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future…