Knowledge catalogue
arXiv:2610.09077v1 Announce Type: new Abstract: Diffusion-based image super-resolution (SR) can create visually plausible detail that is not supported by the low-resolution input. We introduce DISRQAD
arXiv:2610.10520v1 Announce Type: new Abstract: GNN-to-MLP distillation aims to retain the predictive accuracy of a message-passing teacher while deploying a graph-free MLP at inference. Existing meth
arXiv:2610.10065v1 Announce Type: new Abstract: Efficient and distributed coordination of mobile robots is one of the main challenges in multi-robot systems. Topological constraints, often expressed a
arXiv:2602.06597v2 Announce Type: replace Abstract: While generative modeling facilitates probabilistic time series forecasting, incorporating heterogeneous exogenous information remains challenging.
arXiv:2610.09695v1 Announce Type: cross Abstract: Visual foundation models (VFMs) are increasingly integrated into end-to-end autonomous driving for their powerful representations, yet it remains uncl
arXiv:2610.09928v1 Announce Type: new Abstract: Visual generative models are trained to capture the probability distributions of natural images, yet whether their native priors reflect the regularitie
arXiv:2610.09344v1 Announce Type: new Abstract: General image editors are asked to make a photo look as if it were taken at f/1.4, yet it is rarely checked whether the blur they add follows thin-lens
arXiv:2610.10178v1 Announce Type: new Abstract: Vision-Language-Action models are designed to generalise across environments and task descriptions, raising the question of whether their action generat
arXiv:2610.09205v1 Announce Type: new Abstract: Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene. A new object may cast no shadow, a mirror may
arXiv:2610.10378v1 Announce Type: new Abstract: Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution,
arXiv:2610.09126v1 Announce Type: new Abstract: Accurate thermal modeling is essential in metal additive manufacturing (AM) for understanding the process-structure-property chain. Physics-informed neu
arXiv:2610.09457v1 Announce Type: new Abstract: Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the
arXiv:2610.09654v1 Announce Type: new Abstract: Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and
arXiv:2610.09827v1 Announce Type: new Abstract: Long inputs and extended generation increase the storage and access costs of the key-value (KV) cache. Low-bit quantization reduces storage and memory t
arXiv:2610.09720v1 Announce Type: new Abstract: Online reconstruction of dynamic 4D scenes from long, unposed streaming videos requires both continuous processing and photorealistic rendering, which e
arXiv:2610.09976v1 Announce Type: new Abstract: AI-generated text (AIGT) detection can be sensitive to the decoding choices of the source large language model (LLM). We observe that perturbing next-to
arXiv:2610.09715v1 Announce Type: new Abstract: We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on
arXiv:2610.10438v1 Announce Type: new Abstract: Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and
arXiv:2606.17739v2 Announce Type: replace-cross Abstract: Robotics are expected to support environmental monitoring and disaster detection, where decisions must be made under uncertainty, resource lim
arXiv:2610.10213v1 Announce Type: new Abstract: A natural strategy for inverse problems with scarce labelled data is to transfer relational structure learned from abundant forward-simulation data. We
arXiv:2610.09059v1 Announce Type: new Abstract: Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixe
arXiv:2610.09821v1 Announce Type: new Abstract: Generative 3D Gaussian head avatars provide high-quality, efficient rendering, but synthesising the Gaussian representation remains computationally expe
arXiv:2610.09250v1 Announce Type: new Abstract: Best-of-N (BoN) is a common inference-time alignment method that selects the highest-scoring response among N samples from a reference model. Evaluating
arXiv:2603.18166v1 Announce Type: cross Abstract: Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works
arXiv:2610.09993v1 Announce Type: new Abstract: Spatial defect signatures on wafer bin maps (WBMs) trace yield loss to specific process faults, yet supervised classifiers recognize only the defect typ
arXiv:2610.10068v1 Announce Type: new Abstract: Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level informat
arXiv:2610.09919v1 Announce Type: new Abstract: Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk nea
arXiv:2602.10216v2 Announce Type: replace Abstract: A single text prompt passed to a diffusion model yields a wide range of visual outputs determined solely by a stochastic process, leaving users with
arXiv:2610.09275v1 Announce Type: new Abstract: Although spiking neural networks (SNNs) provide an energy-efficient alternative to artificial neural networks (ANNs), their application to remote sensin
arXiv:2610.09187v1 Announce Type: new Abstract: Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival. W
arXiv:2610.09304v1 Announce Type: cross Abstract: Facial movements convey subtle and important information that is critical for human social communication. Optical methods for face capture are difficu
arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than
arXiv:2609.27599v2 Announce Type: cross Abstract: Generating controllable and human-like emphasis remains an open challenge in text-to-speech, even when explicit emphasis control signals are provided
arXiv:2605.06380v2 Announce Type: replace Abstract: The topology of a classifier's decision regions determines how inputs with the same predicted label can be connected and deformed without changing t
arXiv:2610.09781v1 Announce Type: new Abstract: Turning a CAD design into an assembly plan is still largely done by hand, requiring engineers to reason about geometric feasibility, tool access, stabil
arXiv:2610.10297v1 Announce Type: cross Abstract: While energy efficiency is a critical objective for legged-robot locomotion control, achieving low energy consumption while maintaining robust perform
arXiv:2610.10533v1 Announce Type: new Abstract: Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language model
arXiv:2610.09706v1 Announce Type: new Abstract: Region-based neural style transfer enables fine-grained artistic control by allowing independent stylization of semantic image regions. However, composi
arXiv:2610.09828v1 Announce Type: new Abstract: Compact mobile robots must recover scene geometry under changing lighting and surface texture while working within tight payload and cost limits. We pre
arXiv:2610.09757v1 Announce Type: new Abstract: Mid-prefill pruning can reduce the sequence processed by deeper transformer layers, but attention concentration alone does not certify that discarded co
arXiv:2506.11481v2 Announce Type: replace Abstract: Scene Change Detection (SCD) evaluates changes using predefined query-reference (i.e., present-past) image pairs. However, this formulation overlook
arXiv:2609.35308v2 Announce Type: replace Abstract: Large language models treat conversation history as unverified context, so false premises injected into prior turns can be adopted as fact, a failur
arXiv:2510.19773v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attac
arXiv:2610.10321v1 Announce Type: cross Abstract: Road safety programs count the coded fields of police crash records, while the officer's narrative, which often records factors the fields omit, is ra
arXiv:2610.10222v1 Announce Type: new Abstract: Differentially private time-series generators commonly produce fixed-length synthetic windows, whereas downstream forecasting models often require long
arXiv:2610.09280v1 Announce Type: new Abstract: This work examines the transfer of a co-evolved communication mechanism between two robotic agents from a discrete two-dimensional (2D) simulator to a t
arXiv:2610.09272v1 Announce Type: new Abstract: Attention routing requires a signal that predicts the value of attention on the current prefix. We evaluate whether hidden-state extrapolation error, cu
arXiv:2610.09427v1 Announce Type: new Abstract: World-Action Models (WAMs) utilize future visual prediction as an intermediate reasoning process to guide action generation. However, existing WAMs typi
arXiv:2610.10496v1 Announce Type: cross Abstract: Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, and the networks applied to them are typi
arXiv:2610.10038v1 Announce Type: cross Abstract: Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained. We apply ONE-NAS, an online neuroev
arXiv:2610.09563v1 Announce Type: new Abstract: Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control
arXiv:2610.09196v1 Announce Type: new Abstract: Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that pr
arXiv:2610.10395v1 Announce Type: new Abstract: Transformers can execute algorithms on data given in their input. We ask whether they can do the same for causal discovery. We study a standard continuo
arXiv:2610.09929v1 Announce Type: new Abstract: Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction b
arXiv:2610.10091v1 Announce Type: new Abstract: Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature)
arXiv:2610.09372v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers replace the feed-forward block of a Transformer with E expert networks, and each token is routed to k of these experts.
arXiv:2610.10390v1 Announce Type: new Abstract: Vision-language-action~(VLA) models have emerged as a promising paradigm for autonomous driving. However, existing VLA models still suffer from a fundam
arXiv:2610.09984v1 Announce Type: cross Abstract: While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier
arXiv:2610.09288v1 Announce Type: cross Abstract: Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology re
arXiv:2610.10510v1 Announce Type: new Abstract: Tactile sim-to-real learning must bridge simulated contact and device-specific sensor responses while preserving information needed for control. We prop