Knowledge catalogue
arXiv:2607.06651v1 Announce Type: new Abstract: Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and comp
arXiv:2607.07178v1 Announce Type: cross Abstract: Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large Language Model (LLM) tasks. However, exis
arXiv:2607.07302v1 Announce Type: new Abstract: This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created b
arXiv:2607.06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS)
arXiv:2607.07251v1 Announce Type: new Abstract: One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial rea
arXiv:2603.09385v2 Announce Type: replace Abstract: Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approa
arXiv:2607.06724v1 Announce Type: new Abstract: LLM-based robot planners are fluent but cannot guarantee that their plans are executable or safe. Classical PDDL planners can guarantee these properties
arXiv:2508.17298v3 Announce Type: replace-cross Abstract: Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability t
arXiv:2607.07545v1 Announce Type: new Abstract: Recent advances in generative Artificial Intelligence have made synthetic face images increasingly realistic, creating new challenges for multimedia for
arXiv:2607.06644v1 Announce Type: cross Abstract: Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatc
arXiv:2607.07513v1 Announce Type: new Abstract: Self-supervised learning matches supervised accuracy from a fraction of the labels, but the labeled-sample efficiency behind this has lacked a theoretic
arXiv:2509.21160v2 Announce Type: replace-cross Abstract: With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes. These scheme
arXiv:2508.12265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world t
arXiv:2407.11217v4 Announce Type: replace-cross Abstract: Clustering problems such as k-means and k-median are staples of unsupervised learning, and many algorithmic techniques have been developed to
arXiv:2512.02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions. Extracting
arXiv:2607.07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the l
arXiv:2606.17420v2 Announce Type: replace-cross Abstract: Tau positron emission tomography (PET) is widely used for the in vivo characterization of disease stage and progression in Alzheimer's disease
arXiv:2607.06648v1 Announce Type: cross Abstract: Latent reasoning methods perform multi-step inference entirely in the model's continuous hidden states, promising more compact and efficient reasoning
arXiv:2607.06883v1 Announce Type: cross Abstract: We consider the problem of finding stationary points for stochastic convex optimization problems. Rather than surrogates to stationarity, such as a pr
arXiv:2607.06621v1 Announce Type: new Abstract: The pre-softmax score of an attention head is a bilinear form score(i,j) = x_i^T M x_j in a learned operator M = W_q^T W_k. Because M is generally non-s
arXiv:2501.10870v2 Announce Type: replace-cross Abstract: The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rat
arXiv:2607.06957v1 Announce Type: cross Abstract: Realistic and diverse traffic simulation is essential to autonomous driving development. Yet prevailing benchmarks predominantly reward realism, and r
arXiv:2603.17390v2 Announce Type: replace Abstract: Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to
arXiv:2607.07258v1 Announce Type: cross Abstract: In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised l
arXiv:2607.06615v1 Announce Type: cross Abstract: Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and comp
arXiv:2607.07478v1 Announce Type: cross Abstract: FFT-based spectral preprocessing of learned query-key (Q/K) projections substantially improves transformer attention on character-level language model
arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective
arXiv:2607.07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantizati
arXiv:2601.21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framin
arXiv:2607.06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Lar
arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks
arXiv:2605.04724v2 Announce Type: replace-cross Abstract: The pervasive integration of AI has enabled Offensive AI: the exploitation of AI for malicious ends across the cyber-kill chain. A critical ma
arXiv:2603.26772v2 Announce Type: replace-cross Abstract: Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, d
arXiv:2607.07039v1 Announce Type: cross Abstract: Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechan
arXiv:2607.06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantee
arXiv:2501.05711v4 Announce Type: replace Abstract: Vision Language Models (VLMs) have achieved strong performance across a wide range of video understanding tasks. However, their viewpoint-invariant
arXiv:2607.07702v1 Announce Type: new Abstract: The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to d
arXiv:2308.13380v3 Announce Type: replace-cross Abstract: Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavio
arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating a cold start problem for item calibration
arXiv:2607.07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as
arXiv:2607.06782v1 Announce Type: cross Abstract: Global localization from 3D point clouds remains challenging under limited or asymmetric fields of view (FOV), which fail to provide the dense, symmet
arXiv:2603.14412v2 Announce Type: replace Abstract: Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution m
arXiv:2607.07032v1 Announce Type: new Abstract: Spectral positional encodings (PEs) for directed graphs face two obstacles: magnetic Laplacians require an O(n^3) Hermitian eigendecomposition per poten
arXiv:2607.06882v1 Announce Type: cross Abstract: Visual navigation policies built on large pretrained models have so far followed a common recipe: a dedicated visual encoder, a bespoke action head, a
arXiv:2607.06856v1 Announce Type: new Abstract: Prior work suggests that diffusion representations capture low-level geometry but struggle with high-level semantics. We demonstrate that state-of-the-a
arXiv:2607.06943v1 Announce Type: new Abstract: Multimodal learning robust to missing modalities is essential for real-world applications. Existing methods mainly focus on inter-modality missing, wher
arXiv:2607.07382v1 Announce Type: new Abstract: Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning mod
arXiv:2607.07533v1 Announce Type: new Abstract: Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks, yet collecting this data demands repeated motion capt
arXiv:2607.06833v1 Announce Type: new Abstract: Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial ma
arXiv:2607.07452v1 Announce Type: new Abstract: Dense visual SLAM is a fundamental problem in robotics. Recent advances in 3DGS have demonstrated its potential for dense SLAM. Existing 3DGS frameworks
arXiv:2607.07206v1 Announce Type: new Abstract: Adaptive optimizers mix several mechanisms: a metric or preconditioner maps gradients to descent directions, while estimation, memory, step-size control
arXiv:2607.06871v1 Announce Type: new Abstract: Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields infe
arXiv:2607.06855v1 Announce Type: cross Abstract: On-policy distillation is a practical post-training recipe for large language models, supplying dense teacher supervision on the student's own traject
arXiv:2512.05044v2 Announce Type: replace Abstract: Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and recons
arXiv:2607.07101v1 Announce Type: cross Abstract: Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolated vector lacking explicit alignment wit
arXiv:2607.07494v1 Announce Type: cross Abstract: Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision formats, su
arXiv:2607.07029v1 Announce Type: cross Abstract: Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated t
arXiv:2604.02915v2 Announce Type: replace Abstract: We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeli
arXiv:2607.06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio. Some models provide such an alignment directly and others do
arXiv:2607.07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from p