Knowledge catalogue
arXiv:2604.25661v1 Announce Type: new Abstract: Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS) is an image-guided robotic intervention that enhances the accuracy and reproducibility of co
arXiv:2604.25292v1 Announce Type: new Abstract: This paper addresses the problem of traffic congestion management in fixed-wing unmanned aerial vehicle (UAV) corridors by further developing a recently
arXiv:2602.10718v3 Announce Type: replace-cross Abstract: While FP8 attention has shown substantial promise in innovations like FlashAttention-3, its integration into the decoding phase of the DeepSee
arXiv:2602.22243v2 Announce Type: replace Abstract: The online fusion and tracking of static objects from heterogeneous sensor detections is a fundamental problem in robotics, autonomous systems, and
arXiv:2411.16073v3 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-Transformer (Soft-TF), a parameter-efficient framework fo
arXiv:2604.25061v1 Announce Type: cross Abstract: Custom policy-learning pipelines in Spark fail for two coupled systems reasons: rowwise Python execution makes inference impractical, and driver-side
arXiv:2604.25272v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this work, we study a bandit problem where the payoffs
arXiv:2512.09923v2 Announce Type: replace Abstract: Radiance field representations have recently been explored in the latent space of VAEs that are commonly used by diffusion models. This direction of
arXiv:2604.04973v2 Announce Type: replace-cross Abstract: This paper presents StrADiff, a Structured Source-Wise Adaptive Diffusion Framework for unsupervised blind source separation under linear and
arXiv:2604.24947v1 Announce Type: new Abstract: With the rise of mobile video consumption on diverse handheld display resolutions and orientation modes, altering videos to aspect ratios poses challeng
arXiv:2604.25783v1 Announce Type: new Abstract: Subliminal learning describes a student language model inheriting a behavioral bias by fine-tuning on seemingly innocuous data generated by a biased tea
arXiv:2604.25467v1 Announce Type: new Abstract: Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client
arXiv:2604.25779v1 Announce Type: new Abstract: In the MNIST auxiliary logit distillation experiment, a student can acquire an unintended teacher trait despite distilling only on no-class logits throu
arXiv:2506.23690v2 Announce Type: replace Abstract: Diffusion-based video motion customization facilitates the acquisition of human motion representations from a few video samples, while achieving arb
arXiv:2601.12052v2 Announce Type: replace Abstract: Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud remo
arXiv:2604.24934v1 Announce Type: new Abstract: Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms t
arXiv:2604.25904v1 Announce Type: new Abstract: Identity teacher forcing (ITF) enables stable training of deterministic recurrent surrogates for chaotic dynamical systems and has been highly effective
arXiv:2603.19124v2 Announce Type: replace Abstract: This paper presents the design, modeling, and fabrication of 3D-printed, tendon-actuated continuum robots featuring a flexible, tapered backbone con
arXiv:2604.25096v1 Announce Type: new Abstract: There is growing concern that AI chatbots might fuel delusional beliefs in users. Some have suggested that humans and chatbots mutually reinforce false
arXiv:2604.25491v1 Announce Type: new Abstract: Current watermark removal methods are evaluated on two axes: attack success rate and perceptual quality. We show this is insufficient. While state-of-th
arXiv:2604.00485v2 Announce Type: replace Abstract: Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differ
arXiv:2604.25150v1 Announce Type: new Abstract: Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We
arXiv:2406.04855v3 Announce Type: replace Abstract: We present a comprehensive corpus of Russian primary and secondary legislation adopted between 1991 and 2025, comprising 304,382 texts (194,425,905
arXiv:2604.25359v1 Announce Type: new Abstract: Large Language Models are increasingly being deployed to extract structured data from unstructured and semi-structured sources: parsing invoices, medica
arXiv:2604.25530v1 Announce Type: new Abstract: Recent knowledge distillation (KD) methods for semantic segmentation introduce increasingly complex hand-crafted objectives, yet are typically evaluated
arXiv:2604.25634v1 Announce Type: cross Abstract: We report a striking statistical regularity in frontier LLM outputs that enables a CPU-only scoring primitive running at 2.6 microseconds per token, w
arXiv:2604.25299v1 Announce Type: new Abstract: Diffusion models have achieved success in high-fidelity data synthesis, yet their capacity for more complex, structured reasoning like text following ta
arXiv:2510.16340v2 Announce Type: replace Abstract: Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensi
arXiv:2604.25757v1 Announce Type: cross Abstract: Open, unclassified research on secure autonomy is constrained by limited access to operational platforms, contested communications infrastructure, and
arXiv:2604.25895v1 Announce Type: cross Abstract: Preference-based alignment methods, most prominently Reinforcement Learning with Human Feedback (RLHF), use the judgments of human annotators to shape
arXiv:2604.24811v1 Announce Type: new Abstract: Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a con
arXiv:2604.25545v1 Announce Type: new Abstract: Visual state-space models (SSMs) have shown strong potential for medical image segmentation, yet their effectiveness is often limited by two practical i
arXiv:2406.06587v2 Announce Type: replace Abstract: Aligning large language models (LLMs) behaviour with human intent is critical for future AI. An important yet often overlooked aspect of this alignm
arXiv:2604.25902v1 Announce Type: new Abstract: Distributional and neural approaches to natural language semantics have been built almost exclusively on conventional linear algebra: vectors, matrices,
arXiv:2604.25720v1 Announce Type: cross Abstract: Despite strong performance of deep learning models in retinal disease detection, most systems produce static predictions without clinical reasoning or
arXiv:2604.25649v1 Announce Type: new Abstract: Deep learning models are used in critical applications, in which mistakes can have serious consequences. Therefore, it is crucial to understand how and
arXiv:2604.25316v1 Announce Type: new Abstract: Domain adaptation (DA) addresses the challenge of transferring a machine learning model trained on a source domain to a target domain with a different d
arXiv:2604.25208v1 Announce Type: new Abstract: Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illum
arXiv:2604.25131v1 Announce Type: new Abstract: Recent self-supervised pre-training methods for electroencephalogram (EEG) have shown promising results. However, the pre-trained models typically requi
arXiv:2604.24878v1 Announce Type: new Abstract: We provide a systematic recipe for translating ReLU approximation results to softmax attention mechanism. This recipe covers many common approximation t
arXiv:2604.25898v1 Announce Type: new Abstract: Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on prev
arXiv:2511.07743v3 Announce Type: replace Abstract: Ultrasound imaging is a cornerstone of non-invasive clinical diagnostics, yet its limited field of view poses challenges for novel view synthesis. W
arXiv:2604.24883v1 Announce Type: cross Abstract: We study the zero-temperature phase diagram of the 2D spin-imbalanced Fermi gas with short-ranged attractive interactions using the recently developed
arXiv:2511.14183v3 Announce Type: replace Abstract: Digital images are often degraded by soft effects such as lens flare, haze, shadows, and reflections, which reduce aesthetics even though the underl
arXiv:2604.25776v1 Announce Type: new Abstract: Critical analyses of emotion recognition technology have raised ethical concerns around task validity and potential downstream impacts, urging researche
arXiv:2410.09635v2 Announce Type: replace Abstract: Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, acc
arXiv:2604.25334v1 Announce Type: new Abstract: Imbalanced classification remains a pervasive challenge in machine learning, particularly when minority samples are too scarce to provide a robust discr
arXiv:2409.11063v3 Announce Type: replace-cross Abstract: Variational principles play a central role in classical mechanics, providing compact formulations of dynamics and direct access to conserved q
arXiv:2604.25897v1 Announce Type: cross Abstract: Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic. Expected-quality objectives ignore tail outcomes
arXiv:2604.24885v1 Announce Type: new Abstract: We introduce an efficient, resolution-agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios,
arXiv:2604.24953v1 Announce Type: new Abstract: While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Curr
arXiv:2604.24894v1 Announce Type: cross Abstract: We propose VISION-SLS, a method for nonlinear output-feedback control from high-resolution RGB images which provides robust constraint satisfaction gu
arXiv:2604.25570v1 Announce Type: new Abstract: Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token inte
arXiv:2604.25235v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability.
arXiv:2604.03472v2 Announce Type: replace Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without huma
arXiv:2511.21517v2 Announce Type: replace Abstract: Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bi
arXiv:2512.12072v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. Howeve
arXiv:2604.25591v1 Announce Type: cross Abstract: Recent audio-aware large language models (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but the
arXiv:2604.25132v1 Announce Type: new Abstract: Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an
arXiv:2604.25872v1 Announce Type: new Abstract: Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intend