Knowledge catalogue
arXiv:2503.08445v2 Announce Type: replace Abstract: Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancem
arXiv:2601.07506v2 Announce Type: replace Abstract: While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation
arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but
arXiv:2606.11585v1 Announce Type: cross Abstract: We introduce Kuramoto attention, a self-attention layer in which each hidden coordinate is an angle. The layer scores tokens by gated cosine similarit
arXiv:2603.06910v2 Announce Type: replace Abstract: Multilingual large language models (LLMs) are increasingly used in socially sensitive mental health contexts, including support chatbots, screening,
arXiv:2606.11773v1 Announce Type: cross Abstract: Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative-Weights Update (OMWU) are two very popular algorithms to solve convex/concave
arXiv:2606.11319v1 Announce Type: new Abstract: Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnabili
arXiv:2505.10018v4 Announce Type: replace Abstract: Multi-robot collaboration is becoming increasingly critical and presents significant challenges in modern robotics, especially for building a global
arXiv:2606.11786v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages
arXiv:2304.13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of
arXiv:2606.11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language
arXiv:2606.11197v1 Announce Type: cross Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-con
arXiv:2409.12707v2 Announce Type: replace-cross Abstract: Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehi
arXiv:2606.12260v1 Announce Type: cross Abstract: How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual
arXiv:2606.11279v1 Announce Type: cross Abstract: Automatic speech recognition systems have been shown to under-perform when it comes to transcribing words rarely seen in the training data, namely spe
arXiv:2606.12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recom
arXiv:2606.11531v1 Announce Type: new Abstract: We introduce the ladderpath index as a measure of language complexity grounded in algorithmic information theory. It counts the minimum steps needed to
arXiv:2602.02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (
arXiv:2606.12051v1 Announce Type: new Abstract: Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. We contend
arXiv:2606.11278v1 Announce Type: new Abstract: In this paper, we introduce the Soft Lamprey-Inspired Dual Environment Robot (SLIDER) and a proper modeling and optimization procedure employed to desig
arXiv:2606.12240v1 Announce Type: cross Abstract: Multivariate time-series data often exhibit complex temporal dependencies, irregular sampling, and heterogeneous dynamics across multiple time scales,
arXiv:2606.11794v1 Announce Type: cross Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical s
arXiv:2606.12146v1 Announce Type: cross Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical fo
arXiv:2602.00945v2 Announce Type: replace-cross Abstract: LLMs are multilingual by training, yet their lingua franca is often English, reflecting English language dominance in pretraining. Other langu
arXiv:2606.11946v1 Announce Type: cross Abstract: The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph represent
arXiv:2410.12327v2 Announce Type: replace Abstract: Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting re
arXiv:2606.11363v1 Announce Type: new Abstract: Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify en
arXiv:2603.09276v2 Announce Type: replace-cross Abstract: We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective funct
arXiv:2606.12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a
arXiv:2606.12234v1 Announce Type: new Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved t
arXiv:2602.09591v3 Announce Type: replace-cross Abstract: Reinforcement learning substantially improves reasoning in large language models, but it also tends to lengthen chain-of-thought outputs and i
arXiv:2606.11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, static
arXiv:2606.12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing clima
arXiv:2507.03065v2 Announce Type: replace Abstract: Why do some macroscopic structures remain identifiable even though their microscopic constituents continually change? Vortices persist while fluid p
arXiv:2510.08073v2 Announce Type: replace Abstract: AI-generated videos have achieved near-perfect visual realism (e.g., Sora), urgently necessitating reliable detection mechanisms. However, detecting
arXiv:2606.12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing
arXiv:2603.15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect b
arXiv:2606.11196v1 Announce Type: cross Abstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework
arXiv:2603.13854v2 Announce Type: replace-cross Abstract: We introduce power term polynomial algebra, a representation language for Boolean formulae designed to bridge conjunctive normal form (CNF) an
arXiv:2602.02229v2 Announce Type: replace Abstract: We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powe
arXiv:2606.11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: 'in silico' be
arXiv:2606.11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging beca
arXiv:2606.11243v1 Announce Type: cross Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and fl
arXiv:2410.24145v3 Announce Type: replace-cross Abstract: We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length an
arXiv:2606.11466v1 Announce Type: new Abstract: Point cloud semantic segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure. Transformer-bas
arXiv:2606.11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-cl
arXiv:2606.11339v1 Announce Type: cross Abstract: We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD,
arXiv:2606.11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of i
arXiv:2606.12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faste
arXiv:2606.11857v1 Announce Type: cross Abstract: Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.11p vehicular comm
arXiv:2606.11683v1 Announce Type: cross Abstract: Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existing m
arXiv:2606.11391v1 Announce Type: new Abstract: Tensor Product Representations provide the structural fidelity required for symbolic reasoning in models but suffer from exponential dimensionality grow
arXiv:2508.09459v3 Announce Type: replace-cross Abstract: Visual manipulation localization (VML) aims to identify tampered regions in images and videos, a task that has become increasingly challenging
arXiv:2606.11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gr
arXiv:2606.11860v1 Announce Type: new Abstract: In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation lear
arXiv:2606.11841v1 Announce Type: new Abstract: Low-light novel view synthesis is challenging because dark multi-view images contain noise, weak structural detail, and compressed dynamic range. Recent
arXiv:2510.06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses signif
arXiv:2512.13666v2 Announce Type: replace-cross Abstract: The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy
arXiv:2606.11876v1 Announce Type: cross Abstract: Circulating-tumour DNA (ctDNA) carries evidence of drug resistance months before imaging shows it, but the earliest evidence lives below the assay's l
arXiv:2512.11982v2 Announce Type: replace-cross Abstract: Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of gal