Knowledge catalogue
arXiv:2605.11730v1 Announce Type: new Abstract: Automated red-teaming for LLMs often discovers narrow attack slices, missing diverse real-world threats, and yielding insufficient data for safety fine-
arXiv:2605.11266v1 Announce Type: new Abstract: Recent advances in Gaussian Splatting have enabled fast, high-fidelity 3D scene generation, yet these methods remain purely visual and lack an understan
arXiv:2512.05683v2 Announce Type: replace Abstract: Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from d
arXiv:2605.11594v1 Announce Type: new Abstract: High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable f
arXiv:2605.11314v1 Announce Type: new Abstract: Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of
arXiv:2605.11151v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key chall
arXiv:2605.05749v2 Announce Type: replace Abstract: Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent fe
arXiv:2605.11267v1 Announce Type: new Abstract: Accurate measurement of island area and coastline length is crucial for coastal zone monitoring and oceanographic analysis. However, traditional measure
arXiv:2605.12347v1 Announce Type: new Abstract: Stable, low-latency whole-body teleoperation of humanoid robots is an open research challenge, complicated by kinematic mismatches between human and rob
arXiv:2508.08420v3 Announce Type: replace Abstract: We consider the problem of online regret minimization in linear bandits with access to prior observations (offline data) from the underlying bandit
arXiv:2605.10973v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) improves in-domain performance but can degrade out-of-domain (OOD) generalization. Prior work suggests that this degradatio
arXiv:2605.11007v1 Announce Type: new Abstract: We show that the core components of the Transformer block -- attention, residual connections, and normalization -- arise naturally from a single geometr
arXiv:2605.11618v1 Announce Type: new Abstract: Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path
arXiv:2605.11114v1 Announce Type: new Abstract: Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside
arXiv:2502.04763v2 Announce Type: replace-cross Abstract: The Shapley value is the prevalent solution for fair division problems in which a payout is to be divided among multiple agents. By adopting a
arXiv:2506.14097v2 Announce Type: replace Abstract: This paper reformulates complementarity-based time-stepping for frictionless nonsmooth contact between smooth rigid bodies as a recursively generate
arXiv:2504.10428v2 Announce Type: replace-cross Abstract: Binary classification from positive-only samples is a variant of PAC learning where the learner receives i.i.d. positive samples and aims to l
arXiv:2602.22507v2 Announce Type: replace-cross Abstract: Residential floor plan generation requires not only geometric fidelity but also spatial configurational logic: shared living spaces should be
arXiv:2605.12026v1 Announce Type: new Abstract: We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline
arXiv:2605.12292v1 Announce Type: new Abstract: Benchmarking tabular learning has revealed the benefit of dedicated architectures, pushing the state of the art. But real-world tables often contain str
arXiv:2511.07767v2 Announce Type: replace Abstract: Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by repl
arXiv:2605.12064v1 Announce Type: new Abstract: Existing deep learning-based methods can capture shared features from optical and synthetic aperture radar (SAR) images for spatial alignment. However,
arXiv:2605.11055v1 Announce Type: new Abstract: The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agr
arXiv:2602.11126v2 Announce Type: replace Abstract: Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, incl
arXiv:2605.12462v1 Announce Type: cross Abstract: Extreme weather and volatile wholesale electricity markets expose residential consumers to catastrophic financial risks, yet demand response at the di
arXiv:2602.13004v2 Announce Type: replace Abstract: Granger causality recovers directed interactions from time-series data, but in many distributed systems, the data are vertically partitioned across
TST has two moving parts that can be ablated independently: the input-side averaging and the output-side multi-hot cross-entropy. Both, on their own, outperform the matched-FLOPs baseline. Their effec
arXiv:2605.11911v1 Announce Type: new Abstract: Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find w
arXiv:2605.10974v1 Announce Type: new Abstract: Certified verification of transformer attention requires bounding the softmax function over interval constraints on the pre-softmax scores. Existing ver
arXiv:2605.11551v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) samples is critical for safe deployment of neural networks in safety-critical applications. While maximum softmax
arXiv:2605.12021v1 Announce Type: new Abstract: Many image understanding tasks involve identifying what is present and where it appears. However, tasks that address where, such as object discovery, de
arXiv:2605.06314v2 Announce Type: replace Abstract: Benign overfitting is well-characterized in ell_2 geometries, but its behavior under the ell_1 implicit bias of greedy ensembles remains challenging
arXiv:2605.11743v1 Announce Type: new Abstract: Learning latent representations that capture both semantic and spatial information is central to efficient spatio-semantic reasoning. However, many exis
arXiv:2605.10981v1 Announce Type: new Abstract: Reference-free preference optimization has emerged as an efficient alternative to reinforcement learning from human feedback, with Simple Preference Opt
arXiv:2605.08550v1 Announce Type: new Abstract: The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynamics have pred
arXiv:2605.09416v1 Announce Type: new Abstract: Hardware-aware training (HAT) is widely used to improve the robustness of neural networks on non-ideal AI accelerators, such as analog in-memory computi
arXiv:2602.00834v4 Announce Type: replace-cross Abstract: Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-depend
arXiv:2510.19407v2 Announce Type: replace-cross Abstract: A sensor has the ability to probe its surroundings. However, uncertainties in its exact location can significantly compromise its sensing perf
arXiv:2605.10650v1 Announce Type: new Abstract: Proper weight initialization prior to training has historically been one of the key factors that helped kick off the deep learning revolution. Initializ
arXiv:2605.09699v1 Announce Type: cross Abstract: Modern computer vision systems increasingly encounter performance limitations in data-scarce domains, where collecting large-scale, high-quality label
arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti
arXiv:2605.08530v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental co
arXiv:2605.08488v1 Announce Type: cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization
arXiv:2605.08429v1 Announce Type: cross Abstract: Post-deployment monitoring of healthcare AI requires statistically valid, label-efficient methods, but gold-standard labels from clinician chart revie
arXiv:2605.10315v1 Announce Type: cross Abstract: Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate i
arXiv:2605.09707v1 Announce Type: cross Abstract: Training neural networks to satisfy universal constraints over continuous domains poses unique challenges. Common examples include Lyapunov Neural Net
arXiv:2605.09623v1 Announce Type: cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to DNN p
After studying 300 Leetcode Hards, solving every Jane Street puzzle from the Dwarkesh ads, and watching one Horace He lecture, he finally landed the $400k annualized Jane Street internship. Unfortunat
arXiv:2406.12708v3 Announce Type: replace Abstract: Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exp
arXiv:2605.08480v1 Announce Type: new Abstract: Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their abili
arXiv:2605.09115v1 Announce Type: cross Abstract: Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although
As enterprises push AI beyond the pilot stage, the cost and complexity of running inference at scale are forcing a fundamental rethink of how infrastructure is designed, governed and sourced, putting
arXiv:2605.09476v1 Announce Type: cross Abstract: Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including
arXiv:2605.10584v1 Announce Type: cross Abstract: With the detection of gravitational waves (GWs), multi-messenger astronomy has opened a new window for advancing our understanding of astrophysics, de
arXiv:2605.08329v1 Announce Type: new Abstract: Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and comp
arXiv:2605.08269v1 Announce Type: new Abstract: Wireless capsule endoscopy (WCE) enables painless visualization of the gastrointestinal tract, but its diagnostic potential is limited by incomplete muc
Google announced a slew of upcoming features for Android 17 operating system, including advanced capabilities for the company's Gemini AI assistant, redesigned emoji and video improvements. The event
arXiv:2605.09575v1 Announce Type: cross Abstract: Background: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment. M
arXiv:2510.09877v3 Announce Type: replace-cross Abstract: Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice o
arXiv:2605.08405v1 Announce Type: new Abstract: How do LLMs learn in-context? Is it by pattern-matching recent tokens, or by inferring latent structure? We probe this question using a toy graph random