Knowledge catalogue
arXiv:2609.34370v1 Announce Type: new Abstract: Spexis is a multi-GPU LLM inference framework that improves the efficiency of pipeline and tensor parallelism through speculative parallelism. Rather th
arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu
arXiv:2609.34977v1 Announce Type: new Abstract: Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computation
arXiv:2511.09484v3 Announce Type: replace-cross Abstract: Learning agile robotic policies requires large-scale demonstrations, but translating abundant human motion data to robots is bottlenecked by t
arXiv:2609.35268v1 Announce Type: new Abstract: Reinforcement learning (RL) with sparse rewards is challenging because delayed outcomes provide little guidance about which intermediate computations ca
arXiv:2609.35097v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN fore
arXiv:2609.34153v1 Announce Type: new Abstract: Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge
arXiv:2609.34875v1 Announce Type: new Abstract: Reliable positioning, navigation, and timing support intelligent transportation, autonomous systems, and space-air-ground integrated networks. However,
arXiv:2609.35497v1 Announce Type: new Abstract: Long video understanding relies on video memory to overcome the context limits of multimodal large language models. Existing methods follow a build-then
arXiv:2506.07932v2 Announce Type: replace-cross Abstract: We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained encoders and decoders to compre
arXiv:2609.34019v1 Announce Type: new Abstract: In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. The
arXiv:2609.35501v1 Announce Type: cross Abstract: Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hyp
arXiv:2609.32615v1 Announce Type: new Abstract: Training neural networks directly in a low-rank parameterization is an appealing route to reducing memory, compute, and storage simultaneously during bo
arXiv:2609.32672v1 Announce Type: new Abstract: Predicting whether a trained model will generalize under distribution shift remains difficult, especially when target-domain data are unavailable. We in
arXiv:2609.31645v1 Announce Type: new Abstract: Automated incident management in large-scale microservice systems relies on learning robust representations from multimodal observability data, includin
arXiv:2609.33408v1 Announce Type: new Abstract: In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90% missing data. An attention-based base
arXiv:2606.15197v3 Announce Type: replace Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments. Traditional learning-based automated optimiz
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arXiv:2609.34242v1 Announce Type: cross Abstract: AI agents require memory that preserves information across user-agent exchanges, user-to-user conversations, and group conversations with or without a
arXiv:2609.33761v1 Announce Type: new Abstract: Generative models for multivariate weather series are routinely evaluated with pooled distributional metrics computed after marginal calibration. We sho
arXiv:2609.34756v1 Announce Type: cross Abstract: Adapting pretrained models to downstream tasks with limited data has become a central paradigm in modern deep learning. Yet, despite its widespread pr
arXiv:2609.35758v1 Announce Type: cross Abstract: We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects
arXiv:2609.31712v1 Announce Type: new Abstract: Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) wi
arXiv:2609.32864v1 Announce Type: new Abstract: Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term poi
arXiv:2609.33546v1 Announce Type: new Abstract: Current grasp diffusion models provide rich priors for generation, yet their object-centric approach can violate the kinematic and collision constraints
arXiv:2609.34056v1 Announce Type: new Abstract: Reasoning models often act as if they pursue goals, but their efforts are not always directed toward what users intend, sometimes leading them to pursue
arXiv:2609.32362v1 Announce Type: new Abstract: Image steganography refers to embedding secret messages within cover images while maintaining imperceptibility. Recent advances in deep learning - prima
arXiv:2607.18580v2 Announce Type: replace Abstract: Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that mus
arXiv:2609.35002v1 Announce Type: new Abstract: Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whethe
arXiv:2503.21436v2 Announce Type: replace Abstract: The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowle
arXiv:2609.33627v1 Announce Type: new Abstract: Despite recent progress in agentic multi-shot video generation, producing coherent and consistent long-form stories remains challenging. Existing agenti
arXiv:2609.32519v1 Announce Type: cross Abstract: Model steering can strengthen a target behavior while degrading other useful behaviors. We introduce Supervised Transcoder Replacement (STR) to reduce
arXiv:2609.32367v1 Announce Type: new Abstract: We introduce STRIDE (State-Transition Representation via Increment Dynamics and Evolution), which defines states through derivative fingerprints and lea
arXiv:2603.26852v2 Announce Type: replace-cross Abstract: We propose novel algorithms for sequence prediction based on ideas from stringology. These algorithms are time and space efficient and satisfy
arXiv:2609.34025v1 Announce Type: cross Abstract: We present a new class of near-linear algorithms for efficiently integrating general tensor fields defined on trees with distance dependent kernels, t
arXiv:2602.05522v2 Announce Type: replace Abstract: Robust 3D point cloud classification is often pursued by scaling up backbones or relying on specialized data augmentation. We instead ask whether st
arXiv:2609.35502v1 Announce Type: new Abstract: Multimodal prediction relies on diverse forms of evidence: information repeated across modalities, cues specific to a single source, and complex cross-m
arXiv:2609.34831v1 Announce Type: new Abstract: Neural Stochastic Differential Equations (Neural SDEs) provide flexible continuous-time generative models, but generic neural drift and diffusion networ
arXiv:2609.33203v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis. However, unlike U-Net based diffusio
arXiv:2609.32766v1 Announce Type: new Abstract: Modern machine learning spans supervised, transfer, continual, meta-learning, and related regimes that often reuse the same hypothesis classes, architec
arXiv:2609.35120v1 Announce Type: new Abstract: Low-cost handheld ultrasound devices can be widely deployed compared to professional hospital ultrasound machines. However, their images suffer from com
arXiv:2609.35005v1 Announce Type: cross Abstract: Keyword Spotting (KWS) is becoming increasingly important as voice-controlled devices grow more widespread. While voice interaction with smartphones a
arXiv:2609.35177v1 Announce Type: new Abstract: Estimating integrals of black-box, high-dimensional functions, from expectations and kernel mean embeddings to the softmax kernel in self-attention, is
arXiv:2609.34502v1 Announce Type: new Abstract: We present SubjectAnchor, a Subject-Aware Memory-to-Video paradigm for multi-shot storytelling in which the current shot is generated by conditioning on
arXiv:2609.34884v1 Announce Type: new Abstract: Post-training quantization reduces the deployment cost of vision-language models (VLMs), but preserving multimodal capabilities at low bit widths remain
arXiv:2609.34695v1 Announce Type: new Abstract: Reward shaping is fundamental to modern robotic control with deep reinforcement learning (RL), yet practitioners still rely heavily on heuristic princip
arXiv:2609.31775v1 Announce Type: cross Abstract: Artificial intelligence (AI)-based weather prediction is approaching the skill of physical numerical weather prediction (NWP) systems at a fraction of
arXiv:2609.34598v1 Announce Type: new Abstract: Video temporal grounding (VTG) aims to localize the video interval corresponding to a language query. Recent large vision-language models (LVLMs) show g
arXiv:2609.32320v1 Announce Type: new Abstract: Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, re
arXiv:2609.35725v1 Announce Type: new Abstract: The decomposition of 3D point clouds into interpretable geometric primitives remains a longstanding challenge in Computer Vision and Computer Graphics.
arXiv:2609.33237v1 Announce Type: new Abstract: Paired video-action demonstrations enable autonomous surgical behavior, but such data is scarce: robots perform roughly 1% of surgeries, while video-onl
arXiv:2609.34733v1 Announce Type: new Abstract: Dynamic surgical scene modeling is essential for robotic perception, simulation, and decision support. Although existing neural rendering methods enable
arXiv:2609.33781v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) enhances reasoning in large language models (LLMs) through outcome-level feedback, yet recent appr
arXiv:2609.32863v1 Announce Type: new Abstract: Vehicle-to-Vehicle (V2V) cooperative perception enhances autonomous driving by enabling vehicles to share information beyond their direct line of sight.
arXiv:2609.33053v1 Announce Type: new Abstract: Cable-suspended hoisting is widely used to move heavy or bulky payloads that cannot be handled conveniently by rigid pick-and-place systems, for example
arXiv:2609.31725v1 Announce Type: new Abstract: Video Object Segmentation (VOS) aims to accurately segment target objects from consecutive video frames and track the changes of the objects in each fra
arXiv:2609.35113v1 Announce Type: new Abstract: Symbolic regression (SR) seeks concise and interpretable mathematical expressions from data for scientific equation discovery. Existing SR benchmarks fa
arXiv:2605.06140v2 Announce Type: replace Abstract: Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rota
arXiv:2602.15040v2 Announce Type: replace-cross Abstract: Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remain
arXiv:2609.31634v1 Announce Type: new Abstract: This paper develops a theorem-level pipeline in symmetry-quotient settings: quotient linear stability implies quotient flatness, quotient flatness impli