Knowledge catalogue
arXiv:2508.07559v3 Announce Type: replace-cross Abstract: We study the complexity of approximating high-dimensional second-order elliptic PDEs with homogeneous boundary conditions on the unit hypercub
arXiv:2608.01886v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequ
arXiv:2608.00602v1 Announce Type: cross Abstract: Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous latera
arXiv:2608.01857v1 Announce Type: new Abstract: The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as ri
arXiv:2607.10729v2 Announce Type: replace Abstract: Molecular property models are commonly evaluated by holding out Bemis-Murcko scaffolds, yet a scaffold identifier is only one notion of chemical unf
arXiv:2608.01302v1 Announce Type: new Abstract: State-of-the-art RGB-Event detectors improve the detection of small, fast-moving objects by combining complementary features from RGB and Event data, ye
arXiv:2608.00562v1 Announce Type: new Abstract: Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional qualit
arXiv:2608.01090v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains
arXiv:2601.02837v2 Announce Type: replace Abstract: Infrared small target detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, extremely small target sizes, and complex cl
arXiv:2608.00682v1 Announce Type: new Abstract: Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and do
arXiv:2608.01023v1 Announce Type: new Abstract: We present Caliber, an output-perturbation defense against model extraction that formulates noise selection as a calibration problem: how much the defen
arXiv:2608.02469v1 Announce Type: new Abstract: AnimalCLEF26 addresses discovery-oriented animal re-identification, where systems must both attach query images to known individuals and discover unseen
arXiv:2608.01021v1 Announce Type: cross Abstract: In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples coul
arXiv:2608.01322v1 Announce Type: new Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an 'economically linked' company -- is
arXiv:2608.02135v1 Announce Type: cross Abstract: Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and t
arXiv:2608.02172v1 Announce Type: cross Abstract: Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static
arXiv:2608.01725v1 Announce Type: cross Abstract: Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per tas
arXiv:2608.01570v1 Announce Type: new Abstract: Clinic notes and structured electronic health record (EHR) medication history often contain different medication information. Same-visit disagreement be
arXiv:2602.10880v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have shown promise in generating plotting code from chart images, yet achieving structural fidelity remains challengin
arXiv:2608.01457v1 Announce Type: cross Abstract: Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard
arXiv:2608.01014v1 Announce Type: new Abstract: Preference optimization improves mathematical reasoning in large language models (LLMs), but reliable chosen-rejected pairs usually require verified ans
arXiv:2608.01172v1 Announce Type: new Abstract: We propose a workspace-fibered decomposition framework for motion planning in nR planar redundant manipulators operating in cluttered environments. Rath
arXiv:2608.00263v1 Announce Type: new Abstract: Contact planning in robotics requires models that are both computationally efficient and physically accurate. Sphere-based robot representations satisfy
arXiv:2608.01460v1 Announce Type: new Abstract: Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs)
arXiv:2608.02331v1 Announce Type: new Abstract: The same body posture can convey entirely different emotions depending on its surrounding context, yet most methods for recognising bodily emotions trea
arXiv:2607.15856v2 Announce Type: replace Abstract: Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. This stud
arXiv:2608.00674v1 Announce Type: new Abstract: Recent subject-to-image models have achieved impressive progress in personalized image generation, yet they still struggle to preserve fine-grained subj
arXiv:2608.02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several
arXiv:2509.09474v2 Announce Type: replace Abstract: We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules. Motivated by recent wor
arXiv:2608.01055v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-obj
arXiv:2608.00261v1 Announce Type: new Abstract: Do frontier vision-language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that p
arXiv:2608.02486v1 Announce Type: new Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or
arXiv:2608.01588v1 Announce Type: new Abstract: Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time
arXiv:2608.02032v1 Announce Type: new Abstract: Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attenti
arXiv:2203.00070v3 Announce Type: replace-cross Abstract: We develop a framework to study situations where decision makers face alternatives sequentially. Within this framework, we focus on endogenous
arXiv:2608.02408v1 Announce Type: new Abstract: Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction f
arXiv:2505.20894v2 Announce Type: replace Abstract: Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding
arXiv:2608.02099v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time novel view synthesis, yet existing 3DGS accelerators suffer from poor ar
arXiv:2608.02044v1 Announce Type: new Abstract: Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut.
arXiv:2608.01976v1 Announce Type: cross Abstract: Given an algebraic statistical model, a challenging problem is classifying the data according to the number of positive critical points of the likelih
arXiv:2608.00617v1 Announce Type: new Abstract: Many physical attributes are irreversible: ice melts but does not re-freeze, paper chars but does not un-burn. Do video generators respect this? We show
arXiv:2608.01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message
arXiv:2608.01560v1 Announce Type: new Abstract: Scaling the corpus is the default remedy when a contrastive representation lacks an attribute. We report a case where it does nothing, and identify what
arXiv:2608.00110v1 Announce Type: new Abstract: 3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient
arXiv:2606.15553v2 Announce Type: replace Abstract: Despite the significant training acceleration and promising performance, Representation Autoencoders (RAEs) are mainly criticized for poor distillat
arXiv:2607.15933v2 Announce Type: replace Abstract: The effectiveness of modern visual representation learning and autoregressive models critically depends on vector quantization (VQ), which discretiz
arXiv:2608.01357v1 Announce Type: new Abstract: Traditional approximation theory measures convergence rates in terms of the number of parameters or degrees of freedom. However, practical computation o
arXiv:2608.01631v1 Announce Type: new Abstract: KV cache compression is commonly evaluated by final-answer accuracy, implicitly assuming that preserving the answer also preserves the reasoning that su
arXiv:2608.01545v1 Announce Type: cross Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of
arXiv:2608.00486v1 Announce Type: new Abstract: Accurate prediction of object trajectories during manipulation is essential for closing the perception-action loop. Progress is limited on two fronts: a
arXiv:2608.02093v1 Announce Type: new Abstract: More than 70% of the people that fell overboard cruise ships in the period 2010-2019 lost their lives. This paper presents a strategy for reliably predi
arXiv:2608.02580v1 Announce Type: new Abstract: Learning generalizable robot manipulation policies requires large-scale and diverse demonstration data. Egocentric human manipulation videos offer rich
arXiv:2506.04831v3 Announce Type: replace-cross Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions,
arXiv:2510.09379v2 Announce Type: replace Abstract: While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as sta
arXiv:2608.00060v1 Announce Type: new Abstract: Here we introduce ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction), a physics-guided Bayesian formulation. An evidenti
arXiv:2608.00401v1 Announce Type: new Abstract: Aerial robots generate structured aeroacoustic fields during flight, yet these signals have been underexplored as a source of onboard relative perceptio
arXiv:2604.00933v2 Announce Type: replace Abstract: Text-to-image diffusion models achieve high visual fidelity, yet fine-grained affective control remains difficult because textual emotion cues often
arXiv:2608.00071v1 Announce Type: new Abstract: The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to trad
arXiv:2509.11924v2 Announce Type: cross Abstract: Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different m
arXiv:2608.02395v1 Announce Type: new Abstract: Organisms contain diverse, sensorimotor parts across size scales and rapidly adapt to new environments, while machines contain only inert materials at s