6 papers
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents
Yufeng Peng, Yixin Zhang, Ziyi Liu +5
Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness. We investigate how this operating space shapes agent be…
LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning
Xinrui He, Qiyu Kang, Xuhao Li +1
Spiking Neural Networks (SNNs) are well-regarded for their biological plausibility and energy efficiency in processing sequential data. However, dominant SNN architectures typicall…
Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids
Junhao Ren, Honglin Gao, Lan Zhao +3
Uncertainties in renewable generation and demand dynamics challenge day-ahead scheduling. To enhance renewable penetration and maintain intra-day balance, we develop a multi-agent…
AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
Chao Wang, Francesco Banterle, Bin Ren +25
This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms…
AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou +44
This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of…
Fractional-order Spiking Neural Network
Chengjie Ge, Yufeng Peng, Zihao Li +6
Spiking Neural Networks (SNNs) draw inspiration from biological neurons to enable brain-like computation, demonstrating effectiveness in processing temporal information with energy…