Knowledge catalogue
arXiv:2605.18911v1 Announce Type: cross Abstract: Wildfire prediction is important for early warning and resource allocation, yet existing Earth foundation models (Earth FMs) are pretrained for genera
arXiv:2605.20104v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, rece
arXiv:2605.19527v1 Announce Type: new Abstract: Occluded person re-identification focuses on matching partially visible pedestrians across multiple camera views. However, occlusions disrupt body-regio
arXiv:2605.19322v1 Announce Type: new Abstract: Recent advances in Video Large Language Models (Video-LLMs) have greatly expanded multimodal reasoning capabilities. However, the massive number of visu
arXiv:2605.19004v1 Announce Type: new Abstract: Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensin
arXiv:2512.23461v2 Announce Type: replace-cross Abstract: Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values
arXiv:2605.19093v1 Announce Type: new Abstract: System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are di
arXiv:2605.19506v1 Announce Type: new Abstract: First-person dynamic spatial reasoning requires models to track continuous motion and precise geometric structure, but the quadratic attention cost of T
arXiv:2605.19283v1 Announce Type: cross Abstract: Sequential prediction is challenging in regimes of delayed disambiguation, where early observations are ambiguous and multiple latent explanations rem
arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node
arXiv:2605.18908v1 Announce Type: cross Abstract: Deep neural networks (DNNs) remain critically vulnerable to backdoor attacks. Existing post-training detectors often require clean or surrogate data,
arXiv:2512.08237v3 Announce Type: replace Abstract: The advancement of vision-only Bird's-Eye-View (BEV) perception, a core paradigm for cost-effective autonomous driving, is hindered by the long-stan
arXiv:2507.03122v2 Announce Type: replace-cross Abstract: This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical note
arXiv:2605.18936v1 Announce Type: cross Abstract: Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, s
arXiv:2510.03589v2 Announce Type: replace Abstract: Spatio-temporal sensor data in real-world systems is often sparse, noisy, and irregular, making latent field reconstruction fundamentally underconst
arXiv:2605.20005v1 Announce Type: new Abstract: Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as cata
arXiv:2605.18866v1 Announce Type: cross Abstract: Reconstructing continuous flow fields from sparse surface-mounted sensors is central to aerodynamic design, flow control, and digital-twin instrumenta
arXiv:2605.19824v1 Announce Type: new Abstract: Recent attempts to support high-level scene interpretation and planning in Autonomous Vehicles (AVs) using ensembles of Large Language Models (LLMs) and
arXiv:2605.19263v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) offer a mesh-free framework for solving partial differential equations (PDEs), yet training often suffers from
arXiv:2511.16062v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) excel on homophilous graphs but often fail under heterophily due to self-reinforcing and phase-inconsistent signals. We
arXiv:2605.18873v1 Announce Type: cross Abstract: Training and evaluating false data injection attack (FDIA) detectors for power systems is constrained by data scarcity. Operational grid measurements
arXiv:2605.20006v1 Announce Type: new Abstract: Geospatial reasoning requires solving image-grounded problems over the complex spatial structure of a scene. However, developing this capability is hind
arXiv:2605.20145v1 Announce Type: cross Abstract: Bayesian optimization (BO) selects evaluation points for expensive black-box objectives using Gaussian process (GP) predictive distributions. Kernel c
arXiv:2605.19733v1 Announce Type: cross Abstract: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years
arXiv:2605.19765v1 Announce Type: new Abstract: Existing affective-computing, social-signal-processing, and meeting corpora capture important parts of human interaction, but they rarely support analys
arXiv:2605.19192v1 Announce Type: new Abstract: Multimodal agents use screenshots, documents, and webpages to choose tool calls. When a false visual claim triggers a click, email, extraction, or trans
arXiv:2605.19631v1 Announce Type: cross Abstract: End-to-end autonomous driving has emerged as a compelling alternative to traditional modular pipelines by directly mapping raw sensor data to driving
arXiv:2510.00600v2 Announce Type: replace-cross Abstract: Using Large Language Models to produce intermediate thoughts, a.k.a. Chain-of-thought (CoT), before providing an answer has been a successful
arXiv:2605.19301v1 Announce Type: new Abstract: Vision-Language Models require efficient adaptation to continually emerging downstream tasks. While Parameter-Efficient Fine-Tuning mitigates catastroph
arXiv:2605.19592v1 Announce Type: cross Abstract: Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deploym
arXiv:2605.18772v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge
arXiv:2605.19152v1 Announce Type: cross Abstract: Physical computing systems provide a promising route toward hardware-native machine learning, but their computational capabilities remain difficult to
arXiv:2605.20088v1 Announce Type: cross Abstract: Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of ti
arXiv:2605.19042v1 Announce Type: new Abstract: Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data.
arXiv:2605.19982v1 Announce Type: new Abstract: Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, de
arXiv:2605.19435v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) is critical for autonomous navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standa
arXiv:2605.20170v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) have shown impressive performances in user-facing tasks such as question answering, as well as consistent improvemen
arXiv:2408.06843v5 Announce Type: replace Abstract: We present an efficient task and motion replanning approach for sequential multi-object manipulation in dynamic environments. Conventional Task And
arXiv:2605.19562v1 Announce Type: cross Abstract: This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV)
arXiv:2510.03485v2 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically undere
arXiv:2605.19975v1 Announce Type: cross Abstract: Neural policies have shown promise in solving vehicle routing problems due to their reduced reliance on handcrafted heuristics. However, current train
arXiv:2509.22202v3 Announce Type: replace-cross Abstract: Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent l
arXiv:2605.18907v1 Announce Type: cross Abstract: Deep neural networks (DNN), despite their remarkable performance, are highly vulnerable to backdoor attacks. Existing defenses mainly rely on activati
arXiv:2605.19390v1 Announce Type: new Abstract: Recent large multimodal models (LMMs) have become increasingly capable on image and video understanding, yet still struggle to sustain 4D continuous spa
arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r
arXiv:2411.08982v3 Announce Type: replace Abstract: Selective parameter activation provided by Mixture-of-Expert (MoE) models have made them a popular choice in modern foundational models. However, Mo
arXiv:2605.19080v1 Announce Type: cross Abstract: In Online Continual Learning (OCL), a neural network sequentially learns from a non-stationary data stream in a single-pass with access only to a limi
arXiv:2605.18776v1 Announce Type: cross Abstract: The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely
arXiv:2605.19833v1 Announce Type: cross Abstract: Despite rapid advances in automatic speech recognition (ASR) and large audio-language models, robust recognition in real-world environments remains li
arXiv:2605.18809v1 Announce Type: cross Abstract: General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape fa
arXiv:2510.25897v2 Announce Type: replace Abstract: The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one rew
arXiv:2605.19194v1 Announce Type: new Abstract: The Mixture-of-Agents (MoA) framework has shown promise in improving large language model (LLM) performance by aggregating outputs from multiple agents.
arXiv:2605.18956v1 Announce Type: new Abstract: Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and
arXiv:2605.20174v1 Announce Type: new Abstract: Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years
arXiv:2605.18817v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising masked token sequences, offering a tradeoff between parallelism and quality comp
arXiv:2605.19354v1 Announce Type: cross Abstract: MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes mo
arXiv:2605.18818v1 Announce Type: new Abstract: Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running mod
arXiv:2605.19634v1 Announce Type: cross Abstract: Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen e
arXiv:2605.19580v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging,
arXiv:2409.03192v2 Announce Type: replace Abstract: Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However,