7 accepted papers
The development of autonomous agents for complex, long-horizon tasks is a central goal in AI. However, dominant training paradigms face a critical limitation: reinforcement learning (RL) methods that optimize solely for final task success often reinforce flawed or inefficient reasoning paths, a prob…
Large Language Model (LLM) agents have demonstrated strong potential in complex, interactive decision-making tasks. However, when training LLM agents end-to-end with reinforcement learning (RL), efficiently optimizing agent policies in dynamic environments remains a significant challenge. Existing R
The online calibration of 4D millimeter-wave radar and camera is crucial for advancing perception and SLAM technologies in complex environments. It eliminates the reliance on manual labeling, offering real-time and convenience. However, the sparse nature of 4D radar point clouds presents challenges
Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Exi…
Transformer has widely been applied in various low-vision tasks, achieving significant strides in single image super-resolution. However, its low-pass characteristic still limits the ability of Transformer-based models to represent rich texture details in images. Additionally, the quadratic computat…
Tool learning enhances Large Language Models’ (LLMs) dynamic interaction with external tools, improving their ability to solve complex problems. However, current empirical methods, which primarily focus on isolated tools learning, still struggle with accurate multi-tool selection due to issues like…
"Graph convolutional network based methods that model the body joints’ relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these methods have two critical issues: first, deep graph convolutions filter features within only limited graph spectrum band, l…