Knowledge catalogue
arXiv:2604.15514v1 Announce Type: new Abstract: In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we
arXiv:2604.16008v1 Announce Type: new Abstract: This paper introduces a robust discrimination method for distinguishing real ship targets from corner-reflector-array jamming with frequency-agile radar
arXiv:2603.16427v2 Announce Type: replace Abstract: This paper considers self-supervised cross-modal coordination as a strategy enabling utilization of multiple modalities and large volumes of unlabel
arXiv:2510.07774v3 Announce Type: replace Abstract: In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning abil
arXiv:2409.01794v2 Announce Type: replace-cross Abstract: In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum
arXiv:2604.15990v1 Announce Type: cross Abstract: This paper traces a conceptual shift from understanding vulnerability as a static, essentialized property of data subjects to examining how it is acti
Grok 4.3 has introduced native LaTeX compilation functionality integrated directly into Grok Files, enabling users to compile LaTeX documents without requiring external tools or applications. This fea
arXiv:2604.16010v1 Announce Type: new Abstract: This paper proposes image-adaptive contrast limited adaptive histogram equalization (IA-CLAHE). Conventional CLAHE is widely used to boost the performan
arXiv:2604.15726v1 Announce Type: new Abstract: This position paper argues that large language model (LLM) reasoning should be studied as latent-state trajectory formation rather than as faithful surf
arXiv:2604.15472v1 Announce Type: cross Abstract: This paper is dedicated to lossless data compression with probability estimation using neural networks. First, we propose a probability estimation arc
arXiv:2604.15325v1 Announce Type: cross Abstract: This paper presents the design of NEFFY 2.0, a social robot designed as a haptic slow-paced breathing companion for stress reduction, and reports find
arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T
arXiv:2604.16235v1 Announce Type: new Abstract: This paper presents a systematic benchmark of state-of-the-art multilingual large language models (LLMs) adapted via token pruning - a compression techn
Plugins for Excel, PowerPoint, Word, … coming soon Academics, business leaders, and anyone stuck making PowerPoints: Grok 4.3 just turned a full tDCS/TMS neuroscience paper into this clean 9-slide aca
arXiv:2510.24887v4 Announce Type: replace Abstract: This paper examines the feasibility of utilizing lightweight body landmark detection for recognizing isolated signs in Brazilian Sign Language (LIBR
arXiv:2604.15524v1 Announce Type: cross Abstract: This paper presents a novel density control framework for multi-robot systems with spatial safety and energy sustainability guarantees. Stochastic rob
arXiv:2604.16200v1 Announce Type: new Abstract: This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in
arXiv:2507.03759v2 Announce Type: replace-cross Abstract: This paper presents ``randomized SINDy', a sequential machine learning algorithm designed for dynamic data that has a time-dependent structure
arXiv:2411.13109v4 Announce Type: replace Abstract: This paper revisits the topic of rotation estimation through the lens of special unitary matrices. We begin by reformulating Wahba's problem using S
arXiv:2604.16147v1 Announce Type: cross Abstract: This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricul
arXiv:2604.15706v1 Announce Type: new Abstract: Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented la
arXiv:2604.15468v1 Announce Type: cross Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engin
arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar
arXiv:2503.07976v2 Announce Type: replace-cross Abstract: This paper investigates approximation capabilities of two-dimensional (2D) deep convolutional neural networks (CNNs), with Korobov functions s
arXiv:2604.15238v1 Announce Type: cross Abstract: This paper investigates continuous-time and discrete-time firing-rate and Hopfield recurrent neural networks (RNNs), with applications in nonlinear co
arXiv:2604.13940v1 Announce Type: new Abstract: Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and t
arXiv:2505.10846v3 Announce Type: replace Abstract: This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its cor
arXiv:2510.24538v2 Announce Type: replace Abstract: Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we c
arXiv:2509.02571v2 Announce Type: replace-cross Abstract: This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening
arXiv:2604.14331v1 Announce Type: new Abstract: Applying kernel methods to matchings is challenging due to their discrete, non-Euclidean nature. In this paper, we develop a principled framework for co
arXiv:2604.13049v1 Announce Type: cross Abstract: Online reviews and recommendation systems help users navigate overwhelming choice, but they are vulnerable to self-reinforcing distortions. This paper
arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern
arXiv:2604.15311v1 Announce Type: new Abstract: This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gr
arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor
arXiv:2604.14808v1 Announce Type: new Abstract: Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM
arXiv:2604.14595v1 Announce Type: new Abstract: This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness
arXiv:2604.14816v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automati
arXiv:2604.14206v1 Announce Type: new Abstract: This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct
arXiv:2604.15272v1 Announce Type: cross Abstract: This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that
arXiv:2512.23578v3 Announce Type: replace Abstract: In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn
arXiv:2604.14558v1 Announce Type: new Abstract: This paper presents the NTIRE 2026 image super-resolution (imes4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026.
arXiv:2604.15310v1 Announce Type: new Abstract: This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We
arXiv:2604.15074v1 Announce Type: new Abstract: This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning c
arXiv:2210.09817v3 Announce Type: replace Abstract: In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach rel
arXiv:2604.14949v1 Announce Type: cross Abstract: In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regres
arXiv:2512.18436v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason a
arXiv:2604.14059v1 Announce Type: cross Abstract: This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (
arXiv:2604.13147v1 Announce Type: cross Abstract: This paper studies continuous-time stochastic control problems whose controlled states are fully non-Markovian and depend on unknown model parameters.
arXiv:2604.13946v1 Announce Type: cross Abstract: Automated code generation remains a persistent challenge in software engineering, as conventional multi-agent frameworks are often constrained by stat
arXiv:2604.13995v1 Announce Type: new Abstract: This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed metho
arXiv:2604.13232v1 Announce Type: new Abstract: This discussion paper re-examines SemEval-2020 Task 1, the most influential shared benchmark for lexical semantic change detection, through a three-part
arXiv:2604.13179v1 Announce Type: cross Abstract: This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipli
arXiv:2604.13728v1 Announce Type: cross Abstract: We present a hybrid retrieval system for COVID-19 scientific literature, evaluated on the TREC-COVID benchmark (171,332 papers, 50 expert queries). Th
arXiv:2602.20913v2 Announce Type: replace Abstract: This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, a
arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026. Principled domain reweighting can substantially improve sample efficienc
arXiv:2505.19054v2 Announce Type: replace Abstract: Modern learning-based locomotion controllers typically rely on fully trainable deep neural networks with a large number of parameters. This paper st
arXiv:2604.04101v2 Announce Type: replace Abstract: This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challen
arXiv:2604.13890v1 Announce Type: cross Abstract: Why do capitalist economies recurrently generate crises whose severity is disproportionate to the size of the triggering shock? This paper proposes a
arXiv:2604.13861v1 Announce Type: new Abstract: This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket namely batting orde