Knowledge catalogue
arXiv:2609.35043v1 Announce Type: new Abstract: In open-set recognition (OSR), a probe must either be identified as one of the known gallery classes or rejected as unknown, so three error types coexis
arXiv:2609.35652v1 Announce Type: new Abstract: Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility in
arXiv:2609.32690v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process t
arXiv:2609.31718v1 Announce Type: cross Abstract: Images often carry recommendation constraints that text metadata only hints at. A movie may need to look dark, a product may need a minimal style, and
arXiv:2609.34220v1 Announce Type: cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object deli
arXiv:2609.31960v1 Announce Type: new Abstract: Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to c
arXiv:2609.31975v1 Announce Type: new Abstract: This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With
arXiv:2601.19284v3 Announce Type: replace-cross Abstract: Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. T
arXiv:2609.34486v1 Announce Type: new Abstract: Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-base
arXiv:2609.34451v1 Announce Type: new Abstract: Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (M
arXiv:2609.33274v1 Announce Type: cross Abstract: Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is intro
arXiv:2603.15033v4 Announce Type: replace Abstract: Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove
arXiv:2609.31700v1 Announce Type: new Abstract: Compressed-domain video captioning avoids full video decoding by operating directly on I-frames, motion vectors and residuals, trading a small amount of
arXiv:2609.33431v1 Announce Type: new Abstract: We release MoGround, a vision-language dataset spanning four visual domains in which the answer to every question is guaranteed to be available from exa
arXiv:2609.33737v1 Announce Type: new Abstract: Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex int
arXiv:2609.34512v1 Announce Type: new Abstract: Image-aligned metric demonstrations often require dedicated tracking hardware and synchronization across devices. We present MonoEgo, a capture system t
arXiv:2609.32745v1 Announce Type: new Abstract: Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that stru
arXiv:2609.33207v1 Announce Type: cross Abstract: Spiking Vision Transformers (SViTs) are developed as an energy-efficient alternative to conventional ViTs for computer vision tasks at the edge. Howev
arXiv:2609.34280v1 Announce Type: cross Abstract: Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue pat
arXiv:2603.10408v2 Announce Type: replace Abstract: The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency
arXiv:2609.34190v1 Announce Type: new Abstract: Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimension
arXiv:2609.33232v1 Announce Type: new Abstract: Continuous-time sensing and monitoring with timely and accurate decision-making are critical for many real-world applications. In healthcare monitoring
arXiv:2609.33790v1 Announce Type: cross Abstract: Conventional reflectance photoplethysmography (PPG) typically reduces tissue optical responses to one or a few detector channels. Matrix Pinhole Image
arXiv:2609.35274v1 Announce Type: new Abstract: Recurrent and equilibrium graph neural networks (GNNs) often enforce a unique fixed point or use one training target per graph. Yet many combinatorial a
arXiv:2606.12876v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively
arXiv:2609.33741v1 Announce Type: cross Abstract: Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data
arXiv:2609.33138v1 Announce Type: new Abstract: Recovering physical properties of unknown objects through non-prehensile interaction is challenging because no single manipulation primitive reveals all
arXiv:2609.34833v1 Announce Type: new Abstract: Semantic mapping is fundamental to embodied navigation, yet existing methods are developed for indoor environments, where objects exhibit relatively lim
arXiv:2609.35429v1 Announce Type: cross Abstract: Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven a
arXiv:2609.33174v1 Announce Type: new Abstract: Advancing bipedal robots to navigate diverse terrains remains a significant challenge in robotics. Traditional locomotion controllers excel on specific
arXiv:2609.33347v1 Announce Type: new Abstract: World models can be studied as experimental systems with response laws of their own. We introduce MultiEcho, a framework for estimating these laws throu
arXiv:2609.32876v1 Announce Type: new Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slid
arXiv:2609.34915v1 Announce Type: new Abstract: Muon is increasingly used for language-model pretraining, yet its large-step dynamics are not captured by the classical edge-of-stability (EoS) picture
arXiv:2609.32861v1 Announce Type: new Abstract: Muon replaces the momentum buffer of each weight matrix by its orthogonal polar factor. We ask what this orthogonalization does near an optimum, where m
arXiv:2609.34836v1 Announce Type: cross Abstract: Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact e
arXiv:2609.33120v1 Announce Type: new Abstract: Electrical distribution grid operations require inference across heterogeneous networks from sparse, noisy, and incomplete time series measurements. In
arXiv:2609.31669v1 Announce Type: new Abstract: We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixe
arXiv:2609.35291v1 Announce Type: cross Abstract: Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning language models on narrow tas
arXiv:2609.33450v1 Announce Type: new Abstract: Extracting who is where, on which team, wearing which number from a broadcast frame is typically done by stitching a detector, an OCR engine, and classi
arXiv:2609.34167v1 Announce Type: new Abstract: Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost. To in
arXiv:2609.34684v1 Announce Type: cross Abstract: Accurately decoding object states from the internal representations of vision-language-action (VLA) models does not establish that the predictions res
arXiv:2609.34276v1 Announce Type: new Abstract: Frontier models can now perform well on individual embodied navigation tasks through multi-round multimodal reasoning with simple tools. Across successi
arXiv:2609.34969v1 Announce Type: new Abstract: Recent zero-shot Vision-and-Language Navigation (VLN) methods increasingly rely on multimodal large language models (MLLMs) to reason over visual observ
arXiv:2607.10490v2 Announce Type: replace-cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages
arXiv:2609.32487v1 Announce Type: new Abstract: Deep learning is commonly interpreted at two levels: the macroscopic, through aggregate trends in loss summarized by scaling laws, and the microscopic,
arXiv:2609.35752v1 Announce Type: new Abstract: We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains. The harmonic measure of a dom
arXiv:2609.32887v1 Announce Type: cross Abstract: A clear link is established between conventional numerical methods and neural network approximations for solving one-dimensional scalar conservation l
arXiv:2609.32289v1 Announce Type: cross Abstract: Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov an
arXiv:2602.16954v3 Announce Type: replace Abstract: While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained setti
arXiv:2609.33533v1 Announce Type: cross Abstract: Transformers have emerged as powerful architectures for learning solution operators of physical systems. Empirically the prediction error has been obs
arXiv:2511.19573v2 Announce Type: replace Abstract: Neural solvers provide fast solutions to graph combinatorial optimization problems, but training alone does not guarantee solution quality. Exact me
arXiv:2609.33880v1 Announce Type: cross Abstract: Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where
arXiv:2609.35338v1 Announce Type: new Abstract: Mechanistic discovery in neuronal microenvironments requires interventions and measurements that separate competing explanations of solute transport and
arXiv:2609.35445v1 Announce Type: new Abstract: Prioritizing central nervous system (CNS) interventions requires predicting how a dose, route, and schedule act on a partially observed microenvironment
arXiv:2609.33898v1 Announce Type: new Abstract: Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements o
arXiv:2609.34745v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on st
arXiv:2508.09655v3 Announce Type: replace Abstract: This work has been submitted for possible publication. Copyright may be transferred without notice, after which this version may no longer be access
arXiv:2609.33704v1 Announce Type: cross Abstract: Graph learning from edge-detecting queries concerns the reconstruction of an unknown edge set on a known vertex set. Each query reports whether a spec
arXiv:2609.31699v1 Announce Type: cross Abstract: A microphone on the airframe of a small multi-rotor UAV is dominated by rotor ego-noise, so spoken flight commands arrive at negative signal-to-noise
arXiv:2510.13602v3 Announce Type: replace-cross Abstract: Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cach