Image-guided surgery demands adaptive, real-time decision support, yet static AI models struggle with structured task planning and providing interactive guidance. Large language models (LLMs)-powered agents offer a promising solution by enabling dynamic task planning and predictive decision support. Despite recent advances, the absence of surgical agent datasets and robust parameter-efficient fine-tuning techniques limits the development of LLM agents capable of complex intraoperative reasoning. In this paper, we introduce Surgical AI Copilot, an LLM agent for image-guided pituitary surgery, capable of conversation, planning, and task execution in response to queries involving tasks such as MRI tumor segmentation, endoscope anatomy segmentation, overlaying preoperative imaging with intraoperative views, instrument tracking, and surgical visual question answering (VQA). To enable structured agent planning, we develop the PitAgent dataset, a surgical context-aware planning dataset covering surgical tasks like workflow analysis, instrument localization, anatomical segmentation, and query-based reasoning. Additionally, we propose DEFT-GaLore, a Deterministic Energy-based Fourier Transform (DEFT) gradient projection technique for efficient low-rank adaptation of recent LLMs (e.g., LLaMA 3.2, Qwen 2.5), enabling their use as surgical agent planners. We extensively validate our agent's performance and the proposed adaptation technique against other state-of-the-art low-rank adaptation methods on agent planning and prompt generation tasks, including a zero-shot surgical VQA benchmark, demonstrating the significant potential for truly efficient and scalable surgical LLM agents in real-time operative settings.

Precise medical image segmentation is vital for computer-aided diagnosis, yet current methods struggle with subtle endoscopic areas where lesions and normal tissue appear similar. To address this, we propose a shape-aware partitioning model with a dual-branch architecture. Its high-frequency branch captures edges and fine details, while the low-frequency branch focuses on overall shape and color distribution. The proposed model integrates these features via a hybrid decoder and a chimeric wavelet block, facilitating continuous bilateral information interaction. We also introduce a dual-domain loss function to comprehensively evaluate model output against ground truth, especially when pixel value differences are small but frequency domain differences are significant. The proposed method markedly enhances the accuracy and efficiency of computer-aided diagnosis, particularly in polyp and skin lesion segmentation. By accurately capturing lesion shape and volume, it provides a robust tool crucial for disease grading and treatment planning. Moreover, it outperforms comparable hybrid architectures integrating convolutional neural networks and transformers on public endoscopic polyp segmentation benchmarks. Quantitatively, it achieves a 15.29% higher intersection over union than conventional hybrid networks with a 33.75 giga floating-point operations reduction. Furthermore, it shows a 5.93% improvement over hierarchical hybrid models with an 11.74 giga floating-point operations decrease.

3D networks with unmanned aerial vehicles (UAVs) are emerging as a cornerstone of next-generation communication infrastructure, offering flexibility and enhanced coverage in challenging environments. However, prior works predominantly focus on UAV-specific optimizations and high-throughput strategies, often overlooking the critical aspect of network reliability when incorporating these movable entities in the infrastructure. Resilience is paramount in such networks, as it ensures stable performance and connectivity in the face of dynamic conditions, such as mobile edges, transient ground devices, and significant signal interference from urban environments. To address this gap, this article proposes a topology-driven scheme from a holistic view of the 3D networks, leveraging comprehensive scene-based information to enable real-time network adaptability through topological (re)configuration. We decompose this reliability problem into three intertwined stages: topological resilience quantification, UAV self-positioning, and learning-based connectivity optimization. This framework ensures network resilience from a functional perspective, emphasizing the ability to consistently deliver high-quality performance while mitigating connectivity interruptions, essential for reliability of next-generation 3D communication infrastructure. Experimental results validate the effectiveness of our approach, demonstrating significant improvements over traditional methods in terms of bandwidth allocation to ground devices and load balancing among UAVs. Notably, our system excels in highly dynamic scenarios, where it adapts to network instability and connectivity failures on-demand, ensuring consistent and reliable communication performance.

Abstract To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.

We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appro- priate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estima- tion. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pair- wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs in- stead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hy- pergraphs, and further develop algorithms for hypergraph embedding and transductive classification on the basis of the spectral hypergraph cluster- ing approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs.

Images and other high-dimensional data can frequently be characterized by a low dimensional manifold (e.g. one that corresponds to the degrees of freedom of the camera). Recently, nonlinear manifold learning techniques have been used to map images to points in a lower dimension space, capturing some of the dynamics of the camera or the subjects. In general, these methods do not take advantage of any prior understanding of the dynamics we might have, relying instead on local Euclidean distances that can be misleading in image space. In practice, we frequently have some prior knowledge regarding the transformations that relate images (e.g. rotation, translation, etc). We present a method for augmenting existing embedding techniques with additional information derived from known transformations, either in the form of tangent spaces that locally characterize the manifold or distances derived from reconstruction errors. The extra information is incorporated directly into the cost function of the embedding technique. The techniques we augment are largely attractive because there is a closed form solution for their cost optimization. Our approach likewise produces a closed form solution for the augmented cost function. Experiments demonstrate the effectiveness of the approach on a variety of image data.

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.