Knowledge catalogue
Compute has always been core to the @VerneRobotics story. Getting there meant negotiating long, expensive contracts with big neoclouds, and projecting compute needs years into the future. Verne wasn't
arXiv:2608.22930v1 Announce Type: new Abstract: Agentic AI is a fascinating new tool for software development. It is a huge step forward compared to 'conventional' AI assisted coding, which in turn wa
arXiv:2411.09686v4 Announce Type: replace-cross Abstract: Regressing a function F on R^d without incurring the statistical and computational curse of dimensionality requires exploitable structure. Com
arXiv:2608.23153v1 Announce Type: new Abstract: In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, crea
arXiv:2608.21485v1 Announce Type: new Abstract: Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes una
arXiv:2608.21914v1 Announce Type: new Abstract: We align three upper ontologies that sit under UK and NATO defence data infrastructure: the Information Exchange Standard (IES), the Higher Quality Data
arXiv:2608.21441v1 Announce Type: cross Abstract: Automated training of surgeons is one of the most crucial factors that significantly minimize surgical training risks and expenses. With recent advanc
arXiv:2608.22100v1 Announce Type: new Abstract: Construction robotics and automation offer promising means of improving productivity, alleviating workforce shortages, and reducing workers' exposure to
arXiv:2608.21690v1 Announce Type: new Abstract: LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier in
arXiv:2608.22367v1 Announce Type: new Abstract: Diffusion multimodal large language models (dMLLMs) frequently produce long-form outputs marred by semantic drift and repetition, with quality generally
arXiv:2608.22679v1 Announce Type: new Abstract: Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as
arXiv:2602.17174v3 Announce Type: replace-cross Abstract: Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and ope
arXiv:2608.22533v1 Announce Type: new Abstract: Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and ve
arXiv:2608.22804v1 Announce Type: new Abstract: Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased
arXiv:2608.23343v1 Announce Type: new Abstract: Image acquisition with a camera involves several degradations due to the optical system, sensor, or low-level processing steps. We address blind deblurr
arXiv:2608.23551v1 Announce Type: cross Abstract: Recent advances in continuous diffusion and flow-based language models (LMs) have achieved performance competitive with discrete LMs. However, existin
arXiv:2608.21821v1 Announce Type: new Abstract: When Wikipedia's language editions describe the same concept, how differently do they frame it? Prior work measures coverage gaps between editions; we m
arXiv:2509.12811v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown remarkable prowess in text generation, yet producing long-form, factual documents grounded in extensive exte
arXiv:2608.23526v1 Announce Type: new Abstract: World models can predict video without learning dynamics that they reliably preserve. We test whether a frozen DreamerV3 trained only on pendulum video
arXiv:2608.23152v1 Announce Type: new Abstract: Counterspeech effectively neutralizes the impact of online hate. Although prior work explores automated counterspeech generation, it largely emphasizes
arXiv:2608.21740v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are typically trained with behavior cloning (BC) on expert demonstrations. However, BC provides only positive superv
arXiv:2608.22221v1 Announce Type: new Abstract: We study counterfactual protocol evaluation: whether data collected under a realised observation protocol determine the predictive value of alternatives
arXiv:2608.22092v1 Announce Type: new Abstract: Reinforcement-learning models commonly predict complete future states, observations, or feature occupancies, even though action selection depends only o
arXiv:2608.23164v1 Announce Type: cross Abstract: Counterfactual (CF) explanations for time-series classifiers are usually evaluated one example at a time: what minimal edit flips this single window's
arXiv:2608.23244v1 Announce Type: cross Abstract: Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central limitation is that standard LLMs repres
arXiv:2608.23391v1 Announce Type: cross Abstract: Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation
arXiv:2608.23390v1 Announce Type: cross Abstract: English Wikipedia is often treated as the default encyclopedic source, yet non-English Wikipedia editions can contain richer locally grounded informat
arXiv:2608.22420v1 Announce Type: cross Abstract: Decoding perceived speech from non-invasive brain recordings has garnered significant attention in recent years due to its wide range of potential app
arXiv:2608.22074v1 Announce Type: cross Abstract: Identifying atomic defects at elevated temperature is difficult because thermal fluctuations blur the local symmetry that both geometric heuristics an
arXiv:2411.05625v2 Announce Type: replace-cross Abstract: We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variabl
arXiv:2607.27836v2 Announce Type: replace Abstract: Large language model unlearning is consistently fragile under relearn attacks. On TOFU, fine-tuning on twenty forget examples substantially recovers
arXiv:2608.22059v1 Announce Type: cross Abstract: Pretrained image encoders are central to medical image classification, where expert annotation is costly and task-specific cohorts are often limited.
arXiv:2608.22115v1 Announce Type: new Abstract: Collaborative multimodal inference improves edge perception by combining observations from distributed sensing devices, but transmitting high-dimensiona
arXiv:2608.22983v1 Announce Type: new Abstract: Robot manipulators are commonly engineered around a decoupled motion-generation stack: a planner computes a collision-free path and a lower-level contro
CUDA Python 1.0, released alongside CUDA 13.3, provides NVIDIA-maintained libraries and tools that expose the entire CUDA platform directly to Python through a single shared foundation. It adopts sema
arXiv:2608.23065v1 Announce Type: cross Abstract: Cultural understanding in video means more than recognizing what is visible; it requires grasping the symbolic and temporal significance of cultural c
arXiv:2608.23181v1 Announce Type: cross Abstract: As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention,
arXiv:2608.21462v1 Announce Type: cross Abstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alph
arXiv:2608.21529v1 Announce Type: cross Abstract: Timely and accurate assessment of property damage is critical following natural disasters. Traditional on-site inspections are labor-intensive, costly
Data center power startup Emerald AI Inc. today said that it has raised 150 million in new funding in an oversubscribed round at a valuation of 1.05 billion. Emerald will put the money into scaling up
arXiv:2603.00604v2 Announce Type: replace Abstract: High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to ob
arXiv:2608.21584v1 Announce Type: new Abstract: We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By
arXiv:2608.22361v1 Announce Type: cross Abstract: Finite datasets can share the same size and low-order statistics while differing strongly in structural complexity. We connect this dataset complexity
arXiv:2608.22277v1 Announce Type: new Abstract: Deep learning surrogates for forecasting chaotic dynamical systems suffer from catastrophic error accumulation over long-term autoregressive rollouts. T
arXiv:2608.22068v1 Announce Type: new Abstract: Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide opportunities to enhance energ
arXiv:2608.22289v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) increasingly require robust visual localization in GNSS-denied environments. A common solution estimates UAV poses by ma
arXiv:2608.22126v1 Announce Type: cross Abstract: Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based m
arXiv:2403.14830v2 Announce Type: replace-cross Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dim
arXiv:2503.14900v2 Announce Type: replace-cross Abstract: The past a few years have witnessed the great success of large language models, demonstrating powerful capabilities in comprehending textual d
arXiv:2608.22923v1 Announce Type: cross Abstract: Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiv
arXiv:2606.18402v2 Announce Type: replace-cross Abstract: Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and in
arXiv:2606.18395v2 Announce Type: replace-cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single n
arXiv:2608.22615v1 Announce Type: new Abstract: Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progr
arXiv:2608.21784v1 Announce Type: new Abstract: Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes eve
arXiv:2608.23095v1 Announce Type: new Abstract: Media bias detection relies on definitions and examples that specify what counts as bias, yet these specifications often vary across datasets or remain
arXiv:2201.02352v3 Announce Type: replace Abstract: Mobility, which is a basic property for a mechanism has to be analyzed to find the degrees of freedom. A quick method for calculation of degrees of
arXiv:2608.22770v1 Announce Type: new Abstract: Financial institutions need an independent way to detect missing, stale, and misclassified corporate-event records in vendor databases. We introduce Sea
arXiv:2608.22033v1 Announce Type: new Abstract: Stable quadrupedal locomotion on sparse terrain requires selecting state-relevant terrain evidence for precise foot placement. Model-based foothold plan
arXiv:2501.06002v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations
arXiv:2608.23114v1 Announce Type: cross Abstract: Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires r