5 citations · 9 across the 9 of their papers we have counts for
10 papers
UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
Yifei Lu, Cheng Liu, Dianzhi Yu +4
Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extracti…
LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
Yanyu Chen, Jiyue Jiang, Dianzhi Yu +8
The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, m…
Dynamic Mixture of Latent Memories for Self-Evolving Agents
Dianzhi Yu, Vireo Zhang, Hongru Wang +7
Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting previously acquired abilities.…
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Jinhu Qi, Muzhi Li, Jiahong Liu +9
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their mul…
ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning
Wanghan Xu, Yuhao Zhou, Hengyuan Zhao +8
Large language models can fail in critic interaction not only by answering incorrectly, but also by abandoning an initially correct scientific solution after user criticism. This i…
ConSurv: Multimodal Continual Learning for Survival Analysis
Dianzhi Yu, Conghao Xiong, Yankai Chen +6
Survival prediction of cancers is crucial for clinical practice, as it informs mortality risks and influences treatment plans. However, a static model trained on a single dataset f…
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Chunyu Miao, Henry Peng Zou, Yangning Li +28
Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Dianzhi Yu, Xinni Zhang, Yankai Chen +4
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models…
Recent Advances in Speech Language Models: A Survey
Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao +5
Large Language Models (LLMs) have recently garnered significant attention, primarily for their capabilities in text-based interactions. However, natural human interaction often rel…
An Entropy-based Text Watermarking Detection Method
Yijian Lu, Aiwei Liu, Dianzhi Yu +2
Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the…