1 citations · 1 across the 15 of their papers we have counts for
18 papers
PsyMemBehav: Psychology-Informed and Memory-Grounded Two-Level Identity-to-Action Reasoning for Social-Media Behavior Simulation
Wenji Zhang, Xilong Cheng, Yunxiao Qin +8
Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and language in sparse, noisy be…
RippleMem: From Isolated Retrieval to Associative Recollection for Long-Term Agent Memory
Jingbo Ji, Lingyi Li, Xilong Cheng +4
LLM-based agents increasingly rely on external memory to support long-horizon reasoning and interaction. However, the main bottleneck is not simply storing past experience, but rec…
Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection
Zhen Yang, Mengqi Wang, Gengda Zhao +3
Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data contamination detection (DCD) th…
SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time. As skill libraries grow, a cen…
LEGOUI: Designing with UI-DSL Bricks to Balance Transparency and Controllability
Yinsi Zhou, Mingyue Yuan, Hongyue Xu +8
Generative user interface design tools enable rapid prototyping but often operate as black boxes with limited transparency and controllability. When outputs diverge from the design…
HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents
Zian Zhai, Xingyu Tan, Gaowang Zou +2
Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limit…
LLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline
Xiwei Xu, Chen Wang, Jacky Jiang +5
Generative AI and large language models (LLMs) are increasingly applied to question generation and automated assessment. However, deploying LLMs in preparation for high-stakes exam…
Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
Xiangjun Zai, Xingyu Tan, Chen Chen +2
Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers often depend on event order, s…
Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses
Xiaojun Wu, Cehao Yang, Honghao Liu +7
LLM agents increasingly rely on prompts, tools, memory, SOPs, skills, and harness feedback, yet current self-evolution pipelines often update these assets through heuristic reflect…
EXG: Self-Evolving Agents with Experience Graphs
Yuxin Jin, Siyuan Zhang, Hanchen Wang +3
Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents rem…
Multi-Perspective Evidence Synthesis and Reasoning for Unsupervised Multimodal Entity Linking
Mo Zhou, Jianwei Wang, Kai Wang +3
Multimodal Entity Linking (MEL) is a fundamental task in data management that maps ambiguous mentions with diverse modalities to the multimodal entities in a knowledge base. Howeve…
Advancing Multimodal Agent Reasoning with Long-Term Neuro-Symbolic Memory
Rongjie Jiang, Jianwei Wang, Gengda Zhao +3
Recent advances in large language models have driven the emergence of intelligent agents operating in open-world, multimodal environments. To support long-term reasoning, such agen…
Graph of States: Solving Abductive Tasks with Large Language Models
Yu Luo, Rongchen Gao, Lu Teng +9
Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains…
Ctrl-G: Controlling Structural and Interaction Exposure in Knowledge Graph Augmented LLM Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Knowledge graphs (KGs) provide structured evidence for Web question answering with large language models (LLMs), but sending retrieved evidence to a remote LLM can expose sensitive…
Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection
Mengqi Wang, Jianwei Wang, Qing Liu +5
Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods lever…
MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Large Language Models (LLMs) have achieved impressive reasoning abilities, but struggle with temporal understanding, especially when questions involve multiple entities, compound o…
PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang +3
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a…
LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements
Jianwei Wang, Mengqi Wang, Yinsi Zhou +5
Health, Safety, and Environment (HSE) compliance assessment demands dynamic real-time decision-making under complicated regulations and complex human-machine-environment interactio…