Knowledge catalogue
arXiv:2604.09244v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as the mainstream of embodied intelligence. Recent VLA models have expanded their input modalities fr
arXiv:2509.26435v2 Announce Type: replace-cross Abstract: Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interd
arXiv:2604.08595v1 Announce Type: cross Abstract: Existing evaluation methods for LLM-based AI systems, such as LLM-as-a-Judge, verdict systems, and NLI, do not always align well with human assessment
arXiv:2512.07417v2 Announce Type: replace Abstract: Effective traffic control is essential for mitigating congestion in transportation networks. Conventional traffic management strategies, including r
arXiv:2502.08691v2 Announce Type: replace-cross Abstract: Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significa
arXiv:2604.09066v1 Announce Type: new Abstract: Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to
arXiv:2604.08564v1 Announce Type: new Abstract: Auto-regressive models (ARMs) have established a dominant paradigm in language modeling. However, their strictly sequential decoding paradigm imposes fu
arXiv:2603.24245v3 Announce Type: replace Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult fo
arXiv:2405.16772v2 Announce Type: replace-cross Abstract: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. Ho
arXiv:2512.04175v2 Announce Type: replace Abstract: Generalizing deepfake detection to unseen manipulations remains a key challenge. A recent approach to tackle this issue is to train a network with p
arXiv:2511.17687v2 Announce Type: replace Abstract: The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capabil
arXiv:2604.08926v1 Announce Type: new Abstract: Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dil
arXiv:2604.08805v1 Announce Type: cross Abstract: In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (A
arXiv:2511.01383v2 Announce Type: replace Abstract: Accurate point-wise velocity estimation in 3D is crucial for robot interaction with non-rigid dynamic agents, enabling robust performance in path pl
arXiv:2604.09282v1 Announce Type: cross Abstract: Reliable position and attitude sensing is critical for highly automated vehicles that operate on conventional roadways. Lidar sensors are increasingly
arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th
arXiv:2604.09145v1 Announce Type: new Abstract: Nighttime photography is severely degraded by light pollution induced by pervasive artificial lighting in urban environments. After long-range scatterin
arXiv:2604.08922v1 Announce Type: new Abstract: Complex degradations like noise, blur, and low resolution are typical challenges in real world image fusion tasks, limiting the performance and practica
arXiv:2510.10181v3 Announce Type: replace-cross Abstract: Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve t
arXiv:2604.08720v1 Announce Type: cross Abstract: Performance optimization of AI infrastructure is key to the fast adoption of large language models (LLMs). The PyTorch compiler (torch.compile), a cor
arXiv:2604.09106v1 Announce Type: new Abstract: Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural netwo
arXiv:2604.08609v1 Announce Type: cross Abstract: Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts
arXiv:2604.09417v1 Announce Type: new Abstract: Many-objective optimisation, a subset of multi-objective optimisation, involves optimisation problems with more than three objectives. As the number of
arXiv:2501.14377v2 Announce Type: replace Abstract: Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert h
arXiv:2506.09067v2 Announce Type: replace-cross Abstract: Generative medical vision-language models~(Med-VLMs) are primarily designed to generate complex textual information~(e.g., diagnostic reports)
arXiv:2412.12686v3 Announce Type: replace Abstract: Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their En
arXiv:2602.07142v2 Announce Type: replace-cross Abstract: Collaboration is a cornerstone of 21st-century learning, yet teachers continue to face challenges in supporting productive peer interaction. E
arXiv:2604.09249v1 Announce Type: new Abstract: Fashion understanding requires both visual perception and expert-level reasoning about style, occasion, compatibility, and outfit rationale. However, ex
arXiv:2604.08850v1 Announce Type: new Abstract: Independent Component Analysis (ICA) is a fundamental unsupervised learning technique foruncovering latent structure in data by separating mixed signals
arXiv:2604.09114v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a target image that depicts a reference image modified by a textual description. While recent vision-lan
arXiv:2603.02417v3 Announce Type: replace-cross Abstract: Classical stochastic-approximation analyses treat the covariance of stochastic gradients as an exogenous modeling input. We show that under ex
arXiv:2410.15001v5 Announce Type: replace Abstract: Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation
arXiv:2604.08617v1 Announce Type: cross Abstract: Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representat
arXiv:2604.08576v1 Announce Type: cross Abstract: Sixth-generation (6G) wireless networks must support heterogeneous services: enhanced Mobile Broadband (eMBB) requiring 1 Tbps data rates, massive Mac
arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot beha
arXiv:2604.09304v1 Announce Type: new Abstract: For decades, Physically-Based Rendering (PBR) is the fundation of synthesizing photorealisitic images, and therefore sometimes roughly referred as Photo
arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on te
arXiv:2604.09222v1 Announce Type: cross Abstract: Audio large language models (ALLMs) enable rich speech-text interaction, but they also introduce jailbreak vulnerabilities in the audio modality. Exis
arXiv:2604.08966v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have advanced Video Temporal Grounding (VTG), existing methods often couple output paradigms with differe
arXiv:2604.09001v1 Announce Type: new Abstract: Enumerating Minimal Unsatisfiable Subsets (MUSes) is a fundamental task in constraint satisfaction problems (CSPs). Its major challenge is the exponenti
arXiv:2601.05383v4 Announce Type: replace Abstract: Imitation learning (IL) provides a data-driven framework for approximating policies for large-scale combinatorial optimisation problems formulated a
arXiv:2603.02622v2 Announce Type: replace Abstract: While the Implicit Bias(or Implicit Regularization) of standard loss functions has been studied, the optimization geometry induced by discriminative
arXiv:2604.08646v1 Announce Type: new Abstract: Instruction-based video editing is a natural way to control video content with text, but adapting a video generation model into an editor usually appear
arXiv:2604.09121v1 Announce Type: cross Abstract: Recent years have witnessed remarkable progress in automatic speech recognition (ASR), driven by advances in model architectures and large-scale train
arXiv:2603.11795v2 Announce Type: replace Abstract: Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composa
arXiv:2508.16165v2 Announce Type: replace-cross Abstract: Usability evaluation is an essential method to support the design of effective and intuitive user interfaces (UIs). However, it commonly relie
arXiv:2410.08559v5 Announce Type: replace-cross Abstract: Electrocardiogram (ECG) captures the heart's electrical signals, offering valuable information for diagnosing cardiac conditions. However, the
arXiv:2604.08936v1 Announce Type: new Abstract: Medical foundation models (MFMs) aim to learn universal representations from multimodal medical images that can generalize effectively to diverse downst
arXiv:2501.15461v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as
arXiv:2502.13718v2 Announce Type: replace Abstract: Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies
arXiv:2505.20638v2 Announce Type: replace-cross Abstract: While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music nec
arXiv:2604.08916v1 Announce Type: new Abstract: Conventional 3D instance segmentation methods rely on labor-intensive 3D annotations for supervised training, which limits their scalability and general
arXiv:2604.08923v1 Announce Type: new Abstract: Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regre
arXiv:2604.09062v1 Announce Type: new Abstract: Valid segmentation of the optic disc (OD) and optic cup (OC) from fundus photographs is essential for glaucoma screening. Unfortunately, existing deep l
arXiv:2604.02319v2 Announce Type: replace Abstract: When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide ran
arXiv:2604.09096v1 Announce Type: new Abstract: Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differen
arXiv:2603.03339v5 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, per
arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c
arXiv:2604.09430v1 Announce Type: cross Abstract: Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language M
arXiv:2604.09156v1 Announce Type: new Abstract: Parallel kinematics machines (PKM) can exhibit kinematic as well as actuation redundancy. While the meaning of kinematic redundancy has been clarified a