collaborators

12 papers

cs.CL2026

Zero-Mem: Zero-Token Memory Operations for LLM Agents

Yilin Xiao, Zhehan Zhu, Yujing Zhang +8

LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating t…

cs.CL2026

Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions

Zijin Hong, Hao Wu, Su Dong +8

Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contam…

cs.AI2026

Graph-based Agent Memory: Taxonomy, Techniques, and Applications

Chang Yang, Chuang Zhou, Yilin Xiao +15

Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…

cs.CL2026

Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs

Su Dong, Qinggang Zhang, Yilin Xiao +3

Large language models (LLMs) often struggle with knowledge-intensive tasks due to hallucinations and outdated parametric knowledge. While Retrieval-Augmented Generation (RAG) addre…

cs.CL2026

DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China

Congluo Xu, Jiuyue Liu, Ziyang Li +1

Motivated by the emerging adoption of Large Language Models (LLMs) in economics and management research, this paper investigates whether LLMs can reliably identify corporate greenw…

cs.CL2026

LAG: Logic-Augmented Generation from a Cartesian Perspective

Yilin Xiao, Chuang Zhou, Yujing Zhang +5

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet exhibit critical limitations in knowledge-intensive tasks, often generating…