Knowledge catalogue
arXiv:2512.16001v2 Announce Type: replace-cross Abstract: Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often ex
arXiv:2604.21462v1 Announce Type: new Abstract: In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label.
arXiv:2603.27189v2 Announce Type: replace-cross Abstract: Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeabilit
arXiv:2604.21380v1 Announce Type: cross Abstract: Since software performance requirements are documented in natural language, quantifying them into mathematical forms is essential for software enginee
arXiv:2509.21361v2 Announce Type: replace-cross Abstract: Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1)
arXiv:2604.21921v1 Announce Type: new Abstract: We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representati
arXiv:2602.00931v2 Announce Type: replace-cross Abstract: Large language model reasoning is often treated as a monolithic capability, relying on binary preference supervision that fails to capture par
arXiv:2303.03237v4 Announce Type: replace-cross Abstract: Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statis
arXiv:2604.21241v1 Announce Type: cross Abstract: Vision--Language--Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidanc
arXiv:2411.14748v2 Announce Type: replace-cross Abstract: A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited avail
arXiv:2506.21546v4 Announce Type: replace-cross Abstract: Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding
arXiv:2509.13576v2 Announce Type: replace-cross Abstract: Sparse-View CT (SVCT) reconstruction enhances temporal resolution and reduces radiation dose, yet its clinical use is hindered by artifacts du
arXiv:2604.21469v1 Announce Type: new Abstract: Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one
arXiv:2604.21286v1 Announce Type: cross Abstract: Cacioli (2026) showed that the K-way energy probe on standard discriminative predictive coding networks reduces approximately to a monotone function o
arXiv:2604.21131v1 Announce Type: cross Abstract: AI-agent guardrails are memoryless: each message is judged in isolation, so an adversary who spreads a single attack across dozens of sessions slips p
arXiv:2603.26791v2 Announce Type: replace-cross Abstract: Assessing a cited paper's impact is typically done by analyzing its citation context in isolation within the citing paper. While this focuses
arXiv:2604.21416v1 Announce Type: cross Abstract: Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to misclassif
arXiv:2604.20935v1 Announce Type: cross Abstract: Wastewater treatment plants (WWTPs) need digital-twin-style decision support tools that can simulate plant response under prescribed control plans, to
arXiv:2601.22703v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc d
arXiv:2604.21627v1 Announce Type: new Abstract: Advancing face morphing attack techniques is crucial to anticipate evolving threats and develop robust defensive mechanisms for identity verification sy
arXiv:2604.21428v1 Announce Type: new Abstract: Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across
arXiv:2604.21354v1 Announce Type: new Abstract: Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In
arXiv:2604.21006v1 Announce Type: new Abstract: We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research.
arXiv:2604.20861v1 Announce Type: cross Abstract: Generative Recommendation (GR) has demonstrated remarkable performance in next-token prediction paradigms, which relies on Semantic IDs (SIDs) to comp
arXiv:2604.21572v1 Announce Type: new Abstract: This work focuses on per-video unsupervised action segmentation, which is of interest to applications where storing large datasets is either not possibl
arXiv:2502.03484v2 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activiti
arXiv:2602.23408v2 Announce Type: replace-cross Abstract: The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the
arXiv:2604.20852v1 Announce Type: cross Abstract: Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are imple
arXiv:2501.02576v2 Announce Type: replace Abstract: Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference spe
arXiv:2604.20893v1 Announce Type: new Abstract: Wrist exoskeletons play a vital role in rehabilitation and assistive applications, yet conventional actuation mechanisms such as electric motors or pneu
arXiv:2604.20857v1 Announce Type: cross Abstract: Recent advances in autonomous ``AI scientist'' systems have demonstrated the ability to automatically write scientific manuscripts and codes with exec
arXiv:2604.21152v1 Announce Type: cross Abstract: As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. Howev
arXiv:2604.21421v1 Announce Type: cross Abstract: Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA.
arXiv:2604.20985v1 Announce Type: cross Abstract: In machine learning applications, privacy requirements during inference or deployment time could change constantly due to varying policies, regulation
arXiv:2604.21518v1 Announce Type: cross Abstract: Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from se
arXiv:2604.21651v1 Announce Type: cross Abstract: Denoising of periodic signals and accurate waveform estimation are core tasks across many signal processing domains, including speech, music, medical
arXiv:2604.21909v1 Announce Type: new Abstract: Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how
arXiv:2604.21712v1 Announce Type: new Abstract: 3D human mesh recovery from monocular RGB images aims to estimate anatomically plausible 3D human models for downstream applications, but remains challe
arXiv:2509.22321v2 Announce Type: replace Abstract: An associative memory (AM) enables cue-response recall, and associative memorization has recently been noted to underlie the operation of modern neu
arXiv:2604.21814v1 Announce Type: cross Abstract: Capsule endoscopy (CE) enables non-invasive gastrointestinal screening, but current CE research remains largely limited to frame-level classification
arXiv:2602.11871v2 Announce Type: replace Abstract: Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offer
arXiv:2604.21276v1 Announce Type: cross Abstract: As pretrained large language models replace task-specific decoders in speech recognition, a critical question arises: do their text-derived priors mak
arXiv:2507.04023v3 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while gene
arXiv:2604.20909v1 Announce Type: new Abstract: Downhole drilling telemetry presents a fundamental labeling asymmetry: surface sensor data are generated continuously at 1~Hz, while labeled downhole me
arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite
arXiv:2604.20928v1 Announce Type: cross Abstract: Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault
arXiv:2604.21187v1 Announce Type: cross Abstract: Ramsey-good graphs are graphs that contain neither a clique of size s nor an independent set of size t. We study doubly saturated Ramsey-good graphs,
arXiv:2601.13690v2 Announce Type: replace Abstract: Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high ma
arXiv:2604.20993v1 Announce Type: new Abstract: Spreading of liquid droplets on solid substrates constitutes a classic multiphysics problem with widespread applications ranging from inkjet printing, s
arXiv:2604.21473v1 Announce Type: cross Abstract: In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast
arXiv:2604.21598v1 Announce Type: cross Abstract: Multi-agent frameworks are widely used in autonomous code generation and have applications in complex algorithmic problem-solving. Recent work has add
arXiv:2604.21631v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) achieves real-time photorealistic rendering, its performance degrades significantly when training images contain tran
arXiv:2604.21070v1 Announce Type: new Abstract: Summarizing long, domain-specific documents with large language models (LLMs) remains challenging due to context limitations, information loss, and hall
arXiv:2604.21464v1 Announce Type: cross Abstract: Standard reinforcement learning (RL) optimizes policies for reward but imposes few constraints on how decisions evolve over time. As a result, policie
arXiv:2411.00171v2 Announce Type: replace Abstract: To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated prom
arXiv:2604.20949v1 Announce Type: new Abstract: Limit order books can transition rapidly from stable to stressed conditions, yet standard early-warning signals such as order flow imbalance and short-t
arXiv:2604.21387v1 Announce Type: new Abstract: Edge points on 3D point clouds can clearly convey 3D geometry and surface characteristics, therefore, edge detection is widely used in many vision appli
arXiv:2507.05385v4 Announce Type: replace Abstract: We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text
arXiv:2604.21707v1 Announce Type: new Abstract: Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challeng
arXiv:2604.21480v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic