18 accepted papers
Open-Ended Deep Research (OEDR) pushes LLM agents beyond short-form QA toward long-horizon workflows that iteratively search, connect, and synthesize evidence into structured reports. However, existing OEDR agents largely follow either linear "search-then-generate" accumulation or outline-centric pl…
Accurate and interpretable medical image segmentation remains a major challenge, as existing deep learning models primarily optimize pixel-level accuracy while overlooking positional reasoning--an essential component for automated report generation and clinical interpretability. We introduce CG-Reas
This paper studies causal discovery in irregularly sampled time series—a key challenge in risk-sensitive domains like finance, healthcare, and climate science, where missing data and inconsistent sampling frequencies distort causal mechanisms. The main challenge comes from the interdependence betwee…
Large language model (LLM)–based multi-agent systems are challenging to debug because failures often arise from long, branching interaction traces. The prevailing practice is to leverage LLMs for log-based failure localization, attributing errors to a specific agent and step. However, this paradigm…
Currently, existing methods in land cover recognition in open-pit coal mining areas face the issue of insufficient accuracy due to multiscale and blurred boundaries when processing remote sensing images. This paper introduces a remote sensing image semantic segmentation network, DSDN-Net, to tackle…
Abstraction is a critical technique in general problem-solving, allowing complex tasks to be decomposed into smaller, manageable sub-tasks. While traditional symbolic planning relies on predefined primitive symbols to construct structured abstractions, its reliance on formal representations limits a
Generalizing causal effects from Randomized Controlled Trials (RCTs) to target populations across diverse environments is of significant practical importance, as RCTs are often costly and logistically complex to conduct. A key challenge is environmental shift, defined as changes in the distribution…
Dynamic pruning leverage the feature information of each input sample to dynamically adjust the network structure, generating multiple subnetworks suitable for different sample complexity. However, it inevitably introduces higher computational complexity and increased memory consumption. In addition…
Compressed video action recognition is a crucial task in video processing. Compared with traditional methods, it directly processes RGB (I-frames) and motion (motion vectors and residuals) modalities, which effectively alleviates computational burdens. However, this task suffers from dynamic noise a…
The Cine MRI of the small intestine is a dynamic magnetic resonance imaging technique used to observe and evaluate small intestine motility. It captures sequential images of the organ in motion over time through rapid imaging. The Transformer architecture is highly effective at capturing long-range…
3D Gaussian Splatting (3DGS) has emerged as a key rendering pipeline for digital asset creation due to its balance between efficiency and visual quality. To address the issues of unstable pose estimation and scene representation distortion caused by geometric texture inconsistency in large outdoor s
Despite impressive advancements in Visual-Language Models (VLMs) for multi-modal tasks, their reliance on RGB inputs limits precise spatial understanding. Existing methods for integrating spatial cues, such as point clouds or depth, either require specialized sensors or fail to effectively exploit d…
Efficient, accurate, and flexible relative localization is crucial in air-ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi-robot SLAM systems with the same sensor configuration, which are tightly coupled w
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless responses when faced with malicious instructions. Many defense strategies have been developed to enhance the safety of…
As an important work in computer vision, some recent representative works such as Two-stream networks, 3D ConvNets, and Transformer-based networks have achieved outstanding performance. However, due to the high computational cost, the explosion of computation time and parameters, they cannot meet th…
In the coal transportation scene, the object detection model proposed for the driver behavior detection task generally has the problems of inaccurate positioning and difficult detection of small objects, we propose a new model YOLOX-B, which introduces a serialized atrous spatial pyramid pooling str…
This work introduces a novel surface registration method based on foliation. A foliation decomposes the surface into a family of closed loops, such that the decomposition has local tensor product structure. By projecting each loop to a point, the surface is collapsed into a graph. Two homeomorphic s…