2 citations · 4 across the 9 of their papers we have counts for
9 papers
Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification
Yimeng Ye, Shuang Chen, Wenxuan Huang +8
While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability and rapid entropy collapse. Th…
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…
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System
Yuheng Feng, Yangfan He, Yinghui Xia +3
Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems…
DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning
Yangfan He, Xinyan Wang, Tianyu Shi
The task of industrial detection based on deep learning often involves solving two problems: (1) obtaining sufficient and effective data samples, (2) and using efficient and conven…
AICoderEval: Improving AI Domain Code Generation of Large Language Models
Yinghui Xia, Yuyan Chen, Tianyu Shi +2
Automated code generation is a pivotal capability of large language models (LLMs). However, assessing this capability in real-world scenarios remains challenging. Previous methods…
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…