Query observability metrics using the Vercel CLI
The Vercel CLI now includes functionality to query and monitor observability metrics directly from the command line, allowing developers to inspect performance data without leaving their terminal envi
Knowledge catalogue
The Vercel CLI now includes functionality to query and monitor observability metrics directly from the command line, allowing developers to inspect performance data without leaving their terminal envi
arXiv:2605.00901v1 Announce Type: new Abstract: The use of CT imaging is important for screening, diagnosis, therapy planning, and prognosis of lung cancers. Unfortunately, due to differences in imagi
arXiv:2605.01502v1 Announce Type: new Abstract: Epistemic uncertainty estimation is essential for identifying regions where deep learning system outputs may be unreliable. However, existing approaches
arXiv:2605.02184v1 Announce Type: new Abstract: Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) and high-resolution panchromati
arXiv:2605.02003v1 Announce Type: new Abstract: Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used
arXiv:2605.02693v1 Announce Type: cross Abstract: Across many scientific disciplines, multiple observations are collected from the same experimental units, and in modern datasets these observations of
arXiv:2512.13727v2 Announce Type: replace Abstract: Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand c
arXiv:2510.23199v3 Announce Type: replace-cross Abstract: We consider the best arm identification problem, where the goal is to identify the arm with the highest mean reward from a set of K arms under
arXiv:2602.04737v2 Announce Type: replace Abstract: This paper proposes a suite of rationality measures and associated theory for reinforcement learning agents, a property increasingly critical yet ra
arXiv:2605.01991v1 Announce Type: cross Abstract: Learning, prediction, and compression are intimately connected: a model that accurately predicts the next symbol in a sequence can be coupled with a s
arXiv:2605.01393v1 Announce Type: new Abstract: Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries th
arXiv:2605.01104v1 Announce Type: cross Abstract: Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing
arXiv:2509.10746v3 Announce Type: replace Abstract: Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for cl
arXiv:2605.01072v1 Announce Type: cross Abstract: We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based
arXiv:2605.00971v1 Announce Type: cross Abstract: Background: Sensitivity of AI-assisted lung nodule detection systems is known to vary with CT acquisition parameters including radiation dose, reconst
arXiv:2605.02552v1 Announce Type: new Abstract: Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tumor
arXiv:2603.05140v2 Announce Type: replace-cross Abstract: We characterise the computational power of recurrent graph neural networks (GNNs) in terms of arithmetic circuits over the real numbers. Our n
.@Redisinc is returning to Interrupt! Say hello to their team at the expo hall to learn about their fast memory layer for chatbots and AI agents as well as their ready-to-use tools for building AI app
arXiv:2605.02469v1 Announce Type: new Abstract: Online reinforcement learning with verifiable rewards (RLVR) turns checkable outcomes into a scalable training signal, but it keeps rollout generation,
arXiv:2605.01827v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently demonstrated their proficiency in holistic visual comprehension. However, most of them struggle to tackle r
arXiv:2511.17340v3 Announce Type: replace Abstract: Generative image models can produce convincingly real images, with plausible shapes, textures, layouts and lighting. However, one domain in which th
arXiv:2602.04514v3 Announce Type: replace Abstract: The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional represen
arXiv:2605.01913v1 Announce Type: cross Abstract: Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable t
arXiv:2605.01450v1 Announce Type: new Abstract: Recent frameworks like ToFu and TEMPEH provide an automated alternative to classical registration pipelines by predicting 3D meshes in dense semantic co
arXiv:2605.02801v1 Announce Type: new Abstract: As large language model (LLM) agents evolve from isolated tool users into coordinated teams, reinforcement learning (RL) must optimize not only individu
arXiv:2510.22907v2 Announce Type: replace Abstract: Coding agents fail when text-level guesses outrun program facts: they hallucinate APIs, drift to the wrong symbol, and apply edits without evidence
arXiv:2605.02266v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However,
arXiv:2605.01474v1 Announce Type: new Abstract: Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LL
arXiv:2605.01833v1 Announce Type: cross Abstract: We address the challenge of remote control where one or more actors, lacking direct reward access, are steered by a controller over a communication-co
arXiv:2605.02589v1 Announce Type: new Abstract: Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visualiz
arXiv:2605.01483v1 Announce Type: new Abstract: A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of se
arXiv:2605.00915v1 Announce Type: new Abstract: Standard representation probing for visual models relies on mathematically permutation-invariant operations like Global Average Pooling (GAP) or CLS tok
arXiv:2605.02283v1 Announce Type: new Abstract: Vision foundation models have attracted significant attention for their ability to leverage large-scale unlabeled visual data. This advantage is particu
arXiv:2605.02627v1 Announce Type: new Abstract: Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromat
arXiv:2605.01325v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works hav
arXiv:2605.01403v1 Announce Type: new Abstract: Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interact
arXiv:2605.00254v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of
arXiv:2605.02604v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) adapts source models to target domains without accessing source data, addressing privacy and transmission issues. H
arXiv:2605.00893v1 Announce Type: new Abstract: Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and fact
arXiv:2605.01965v1 Announce Type: new Abstract: A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from
arXiv:2605.02623v1 Announce Type: new Abstract: Video Moment Retrieval (VMR) aims to localize temporal segments in videos that correspond to a natural language query, but typically assumes only a sing
arXiv:2310.11420v2 Announce Type: replace Abstract: We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functiona
arXiv:2605.02505v1 Announce Type: new Abstract: Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as wh
Health and Human Services Secretary Robert F. Kennedy Jr. announced a plan to reduce 'overprescribing' of psychiatric medications and support alternative treatment options, while claiming antidepressa
arXiv:2605.01240v1 Announce Type: new Abstract: Self-supervised pretraining is promising for large-scale neuroimaging, yet the impact of region-aware masking and hybrid sequence modeling remains under
arXiv:2603.02856v2 Announce Type: replace Abstract: Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environme
arXiv:2506.19133v3 Announce Type: replace Abstract: Euclidean representations distort data with intrinsic non-Euclidean structure. While Riemannian representation learning offers a solution by embeddi
arXiv:2605.01831v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback has become the standard paradigm for language model alignment, where reward models directly determine alignme
arXiv:2602.10101v2 Announce Type: replace Abstract: 3D spatial perception is fundamental to generalizable robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging. De
arXiv:2512.24129v2 Announce Type: replace Abstract: On the way toward full autonomy, sharing roads between automated and autonomous vehicles in so-called mixed traffic is unavoidable. Moreover, even i
arXiv:2605.02538v1 Announce Type: cross Abstract: Despite the advancement in robotic grasping and dexterity through haptic information, affective social touch, such as handshaking or reassuring stroki
arXiv:2605.02135v1 Announce Type: new Abstract: Desktop organization remains challenging for service robots because of heterogeneous objects and diverse manipulation objectives, such as collection and
arXiv:2605.02370v1 Announce Type: new Abstract: This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial m
arXiv:2605.02015v1 Announce Type: new Abstract: Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traf
arXiv:2605.02701v1 Announce Type: cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show th
arXiv:2605.01868v1 Announce Type: new Abstract: Conformal prediction (CP) constructs prediction sets with marginal coverage guarantees under the assumption that the calibration and test distributions
arXiv:2605.00869v1 Announce Type: cross Abstract: Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health m
arXiv:2605.01552v1 Announce Type: new Abstract: In this paper, we introduce a challenging task: extracting a fundamental matrix from a single motion blurred image. For a camera moving in 3D during exp
arXiv:2605.01752v1 Announce Type: new Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adve
arXiv:2605.01339v1 Announce Type: new Abstract: Learning-based approaches to verifying unknown Markov decision processes (MDPs) often employ uncertain MDPs. These models use, for example, confidence i