7 papers
Context Learning for Multi-Agent Discussion
Xingyuan Hua, Sheng Yue, Xinyi Li +3
Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via structured discussion. However, we fin…
Executable Agentic Memory for GUI Agent
Zerui Qin, Sheng Yue, Xingyuan Hua +2
Modern GUI agents typically rely on a model-centric and step-wise interaction paradigm, where LLMs must re-interpret the UI and re-decide actions at every screen, which is fragile…
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
Xingyuan Hua, Sheng Yue, Ju Ren
Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of existing methods is that they…
AdamO: A Collapse-Suppressed Optimizer for Offline RL
Nan Qiao, Sheng Yue, Shuning Wang +1
Offline reinforcement learning (RL) can fail spectacularly when bootstrapped temporal-difference (TD) updates amplify their own errors, driving the critic toward extreme and unusab…
Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting
Nan Qiao, Shuning Wang, Sijing Duan +5
Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constrain…
FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data
Nan Qiao, Sheng Yue, Ju Ren +1
Offline Federated Reinforcement Learning (FRL), a marriage of federated learning and offline reinforcement learning, has attracted increasing interest recently. Albeit with some ad…