Knowledge catalogue
arXiv:2604.12894v1 Announce Type: new Abstract: We present CUBE (Control-based Unified B-spline Encoding), a new geometric representation for human faces that combines B-spline volumes with learned fe
arXiv:2604.12195v1 Announce Type: new Abstract: Work in cognitive science and artificial intelligence has suggested that exposing learning agents to traces of interaction between multiple individuals
arXiv:2604.11947v1 Announce Type: cross Abstract: Unlocking large-scale low-bandwidth decentralized training has the potential to utilize otherwise untapped compute resources. In centralized settings,
arXiv:2501.13340v4 Announce Type: replace Abstract: Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational re
arXiv:2604.12169v1 Announce Type: new Abstract: We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the prepar
arXiv:2511.21211v3 Announce Type: replace Abstract: Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. Howev
arXiv:2604.12079v1 Announce Type: cross Abstract: Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability
arXiv:2604.12271v1 Announce Type: new Abstract: Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared messa
arXiv:2604.12319v1 Announce Type: new Abstract: Multimodal semantic segmentation has emerged as a powerful paradigm for enhancing scene understanding by leveraging complementary information from multi
arXiv:2604.12446v1 Announce Type: cross Abstract: Text-to-image (T2I) diffusion models have achieved remarkable success in image synthesis, but their reliance on large-scale data and open ecosystems i
arXiv:2604.12752v1 Announce Type: new Abstract: In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical a
arXiv:2604.13035v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and
arXiv:2604.12377v1 Announce Type: cross Abstract: Korean is a morphologically rich language with a featural writing system in which each character is systematically composed of subcharacter units know
arXiv:2604.12110v1 Announce Type: new Abstract: Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance,
arXiv:2604.12254v1 Announce Type: cross Abstract: SpanKey is a lightweight way to gate inference without encrypting weights or chasing leaderboard accuracy on gated inference. The idea is to condition
arXiv:2604.12247v1 Announce Type: cross Abstract: Speculative decoding has emerged as a promising approach to accelerate autoregressive inference in large language models (LLMs). Self-draft methods, w
arXiv:2604.12746v1 Announce Type: new Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detec
arXiv:2604.11890v1 Announce Type: new Abstract: We study signal propagation at initialization in transformers through the averaged partial Jacobian norm (APJN), a measure of gradient amplification acr
arXiv:2508.04282v3 Announce Type: replace Abstract: Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments
arXiv:2604.12534v1 Announce Type: new Abstract: Similarity in formal argumentation has recently gained attention due to its significance in problems such as argument aggregation in semantics and enthy
arXiv:2604.12097v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in daily applications, from content generation to code writing, where each interaction treats the mod
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. NASA is building the first nuclear reactor-powered interplanet
arXiv:2604.12289v1 Announce Type: cross Abstract: Online hate speech is associated with substantial social harms, yet it remains unclear how consistently platforms enforce hate speech policies or whet
arXiv:2604.11998v1 Announce Type: cross Abstract: Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particu
arXiv:2604.12046v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinf
arXiv:2604.12216v1 Announce Type: cross Abstract: The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes. Watermarkin
Francois Chollet makes the observation that achieving a perfect score on certain AI benchmark games or tasks only requires surpassing the median performance of an unfiltered random population sample,
arXiv:2604.12931v1 Announce Type: cross Abstract: Token-based semantic communication is promising for future wireless networks, as it can compact semantic tokens under very limited channel capacity. H
arXiv:2604.12503v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenari
Security researcher Alexander Hagenah released 'TotalRecall Reloaded,' a tool that exploits a weakness in Windows 11's Recall feature by injecting a DLL payload into `AIXHost.exe` — the unprotected pr
arXiv:2604.12309v1 Announce Type: new Abstract: We present a novel method for generating geometrically realistic and consistent orbital videos from a single image of an object. Existing video generati
arXiv:2604.10861v1 Announce Type: cross Abstract: The computational demands of deep learning motivate the investigation of alternative approaches to computation. One alternative is physical neural net
arXiv:2511.06424v2 Announce Type: replace-cross Abstract: While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computati
arXiv:2604.12015v1 Announce Type: cross Abstract: In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heur
arXiv:2604.11833v1 Announce Type: new Abstract: Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack o
arXiv:2505.14129v2 Announce Type: replace Abstract: Evolution and learning have historically been interrelated topics, and their interplay is attracting increased interest lately. The emerging new fac
arXiv:2604.11843v1 Announce Type: new Abstract: Invisible watermarking for autoregressive (AR) image generation has recently gained attention as a means of protecting image ownership and tracing AI-ge
arXiv:2505.17384v2 Announce Type: replace-cross Abstract: Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a
arXiv:2604.12431v1 Announce Type: cross Abstract: Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify
arXiv:2602.22394v2 Announce Type: replace Abstract: Vision Transformers (ViTs), when pre-trained on large-scale data, provide general-purpose representations for diverse downstream tasks. However, art
arXiv:2604.12044v1 Announce Type: cross Abstract: Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Devia
arXiv:2510.21697v2 Announce Type: replace Abstract: In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by wo
arXiv:2604.12358v1 Announce Type: new Abstract: Recently, visual token pruning has been studied to handle the vast number of visual tokens in Multimodal Large Language Models. However, we observe that
arXiv:2604.12140v1 Announce Type: new Abstract: We present XANE(3), a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly f
'Young, anxious followers, looking for purpose, can be radicalized by apocalyptic AI rhetoric […] The real question is how long the people fueling AI panic expect to avoid responsibility for where tha
arXiv:2604.11302v1 Announce Type: cross Abstract: We present 3D-Anchored Lookahead Planning (3D-ALP), a System 2 reasoning engine for robotic manipulation that combines Monte Carlo Tree Search (MCTS)
arXiv:2604.11211v1 Announce Type: new Abstract: Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this
arXiv:2604.10246v1 Announce Type: new Abstract: Photogrammetric 3D reconstruction has long relied on traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) methods, which provide high acc
arXiv:2604.10241v1 Announce Type: new Abstract: Identifying the trajectories of rigid bodies and of interaction forces is essential for a wide range of tasks in robotics, biomechanics, and related dom
arXiv:2604.10650v1 Announce Type: cross Abstract: While the manifold hypothesis is widely adopted in modern machine learning, complex data is often better modeled as stratified spaces -- unions of man
arXiv:2604.10116v1 Announce Type: cross Abstract: Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a s
arXiv:2604.11064v1 Announce Type: cross Abstract: Continual Learning (CL) aims to train neural networks on a dynamic stream of tasks without forgetting previously learned knowledge. Among optimization
arXiv:2604.09752v1 Announce Type: cross Abstract: During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces seve
arXiv:2510.03989v2 Announce Type: replace-cross Abstract: The Transformer architecture has revolutionized the field of sequence modeling and underpins the recent breakthroughs in large language models
arXiv:2604.09979v1 Announce Type: cross Abstract: Self-supervised representation learning is central to modern machine learning because it extracts structured latent features from unlabeled data and e
arXiv:2604.10177v1 Announce Type: cross Abstract: We study the piecewise-stationary restless multi-armed bandit (PS-RMAB) problem, where each arm evolves as a Markov chain but mean rewards may change
arXiv:2604.10957v1 Announce Type: cross Abstract: Writing systems are cultural replicators whose evolution has never been studied quantitatively at global scale. We compile the Global Script Database
arXiv:2604.10248v1 Announce Type: new Abstract: Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operatin
arXiv:2604.10506v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have made significant strides in static image understanding but continue to face critical hurdles in spatiotemporal reason
arXiv:2604.10873v1 Announce Type: new Abstract: We propose an operational, quantitative definition of intelligence for arbitrary physical systems. The intelligence density of a system is the ratio of