17 papers
BMAM: Brain-inspired Multi-Agent Memory Framework
Yang Li, Jiaxiang Liu, Yusong Wang +2
Language-model-based agents operating over extended interaction horizons face persistent challenges in preserving temporally grounded information and maintaining behavioral consist…
Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs
Junbo Li, Peng Zhou, Rui Meng +3
Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Re…
ATPO: Agentic Turn-based Policy Optimization via Tree Search
Zefang Zong, Dingwei Chen, Yang Li +6
LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has rece…
Grokked Models are Better Unlearners
Yuanbang Liang, Yang Li
Grokking-delayed generalization that emerges well after a model has fit the training data-has been linked to robustness and representation quality. We ask whether this training reg…
Adapting Like Humans: A Metacognitive Agent with Test-time Reasoning
Yang Li, Zhiyuan He, Yuxuan Huang +5
Recent Vision-Language Models (VLMs) exhibit strong perceptual reasoning abilities, yet they often struggle to adapt efficiently when encountering novel tasks at test time. In cont…
ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
Qing Zhang, Bing Xu, Xudong Zhang +9
The remarkable performance of Large Language Models (LLMs) highly relies on crafted prompts. However, manual prompt engineering is a laborious process, creating a core bottleneck f…