2 citations · 3 across the 9 of their papers we have counts for
9 papers
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
Meiling Tao, Yiling Tao, Peng Wang
User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constrain…
When Search Agents Should Ask: DiscoBench for Clarification-Aware Deep Search
Yiling Tao, Shihan Deng, Meiling Tao +3
Search agents powered by large language models (LLMs) are increasingly used to solve complex information-seeking tasks, requiring multi-step retrieval and reasoning to fulfill user…
GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems
Yiling Tao, Xinran Zheng, Shuo Yang +2
While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security vulnerabilities. In this paper, we propose…
MARS: Memory-Enhanced Agents with Reflective Self-improvement
Xuechen Liang, Meiling Tao, Yinghui Xia +8
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making, lack of…
Self-evolving Agents with reflective and memory-augmented abilities
Xuechen Liang, Yangfan He, Yinghui Xia +11
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this…
LanguaShrink: Reducing Token Overhead with Psycholinguistics
Xuechen Liang, Meiling Tao, Yinghui Xia +3
As large language models (LLMs) improve their capabilities in handling complex tasks, the issues of computational cost and efficiency due to long prompts are becoming increasingly…
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary
Meiling Tao, Xuechen Liang, Xinyuan Song +4
Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary f…
CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models
Xuechen Liang, Yangfan He, Meiling Tao +5
Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significa…
RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models
Meiling Tao, Xuechen Liang, Tianyu Shi +2
This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of…