Knowledge catalogue
arXiv:2610.06288v1 Announce Type: new Abstract: Wi-Fi channel state information (CSI) enables contactless presence detection and gesture recognition. Its high-dimensional complex-valued time series re
arXiv:2610.05310v1 Announce Type: cross Abstract: Frozen speech foundation models (SFMs) make downstream adaptation efficient: the backbone can stay fixed while a task learns layer fusion and a lightw
arXiv:2512.13492v2 Announce Type: replace Abstract: Native 4K (2176imes3840) video generation remains a critical challenge due to the quadratic computational explosion of full-attention as spatiotempo
arXiv:2502.09625v2 Announce Type: replace-cross Abstract: To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks t
arXiv:2610.05025v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist
arXiv:2610.04957v1 Announce Type: new Abstract: Floorplanning arranges the blocks of a chip and decides their shapes under objectives that press blocks together, short wirelength and a small outline,
arXiv:2610.04327v1 Announce Type: new Abstract: After a disaster, building damage is assessed from overhead tiles and ground-level photographs, and most methods fuse the two views symmetrically, trust
arXiv:2610.04925v1 Announce Type: new Abstract: Time-series forecasting informs critical decisions in energy dispatch, industrial operations, and environmental monitoring; understanding the patterns m
arXiv:2511.10542v2 Announce Type: replace-cross Abstract: Using 2.6 billion geolocated social-media posts (2014-2022) and a fine-tuned generative language model, we construct county-level indicators o
arXiv:2610.06182v1 Announce Type: new Abstract: Lloyd's algorithm and the discrete local (D-local) optimization method (Li et al., 2025) for k-means provide only weak local-optimality guarantees, and
arXiv:2610.05488v1 Announce Type: new Abstract: Softmax attention gives every token a nonzero weight, which in trained models concentrates into attention sinks and massive activations that widen the d
arXiv:2306.10426v3 Announce Type: replace Abstract: As robustness verification methods are becoming more precise, training certifiably robust neural networks is becoming ever more relevant. To this en
arXiv:2602.20297v2 Announce Type: replace-cross Abstract: We study gap-dependent regret for reinforcement learning with linear function approximation. While prior works have established gap-dependent
arXiv:2111.14522v4 Announce Type: replace-cross Abstract: Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works poi
arXiv:2610.05329v1 Announce Type: new Abstract: Large language models (LLMs) are typically pretrained in high precision but increasingly deployed with low-precision post-training quantization (PTQ). R
arXiv:2610.05484v1 Announce Type: cross Abstract: Recent Test-Time Training (TTT) architectures compress context into fast weights that are updated online and queried as memory. Existing TTT designs k
arXiv:2610.05425v1 Announce Type: new Abstract: Automated checks of radiology reports may rely on AI-generated checklists that leave findings unmentioned. We used reinforcement learning to train a vis
arXiv:2610.04076v1 Announce Type: new Abstract: Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel v
arXiv:2610.05180v1 Announce Type: new Abstract: Learning dexterous manipulation benefits from human demonstration datasets that capture diverse and natural hand-object interactions. In particular, con
arXiv:2610.05000v1 Announce Type: new Abstract: Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive
arXiv:2610.06271v1 Announce Type: new Abstract: Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-
arXiv:2610.04007v1 Announce Type: new Abstract: We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under
arXiv:2610.06540v1 Announce Type: new Abstract: Spatio-temporal graph models typically encode each snapshot with a GNN and then connect the resulting representations through a temporal module. This sp
arXiv:2610.05818v1 Announce Type: new Abstract: Vision-language-action models (VLAs) act on instructions without being able to refuse, so screening harmful requests falls to monitors. We test whether
arXiv:2610.06285v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) are neural classifiers that allow to explain their decisions via high-level concepts, potentially enabling understandin
arXiv:2610.05550v1 Announce Type: new Abstract: The component that dominates a latent world model's prediction error need not be the one whose repair most improves action selection. We show this by co
arXiv:2610.04874v1 Announce Type: cross Abstract: In this note, we prove that the noncommutative AM-GM inequality holds if nge 2lceil m/2 rceil^2. The motivation comes from counterexamples constructed
arXiv:2610.03831v1 Announce Type: new Abstract: Capable systems often show jagged competence: low average error alongside failures on particular inputs. We ask where it comes from in a task built from
arXiv:2610.04104v1 Announce Type: new Abstract: Semantic Knowledge-driven Contrastive Learning (SKCL) uses a language model to decide which classes are related, and pulls each image towards the protot
arXiv:2610.04147v1 Announce Type: new Abstract: A decade after its inception, Inception crop has become the standard crop-based data augmentation method for training deep vision models. Not only is it
arXiv:2610.04869v1 Announce Type: new Abstract: An interaction has two parts: someone decides to act, and then chooses whom to act on. Temporal link prediction has concentrated on the second, and we s
arXiv:2610.05601v1 Announce Type: new Abstract: Existing attacks on unlearned diffusion models assume that the erased concepts are known in advance and focus on recovering them. In practice, however,
arXiv:2610.02461v1 Announce Type: new Abstract: Accurate thermal analysis of heterogeneous 2.5D/3D-IC packages is essential yet computationally prohibitive. A single full-package FEM simulation can ta
arXiv:2610.03332v1 Announce Type: new Abstract: Accurate three-dimensional (3D) reconstruction of individual dendrites in serial block-face scanning electron microscopy (SBF-SEM) is essential for quan
arXiv:2610.02529v1 Announce Type: new Abstract: Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral i
arXiv:2610.02634v1 Announce Type: cross Abstract: Magnetic resonance elastography (MRE) is a noninvasive imaging modality for quantifying the viscoelastic properties of soft tissues from shear wave pr
arXiv:2610.02245v1 Announce Type: cross Abstract: Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic
arXiv:2610.02342v1 Announce Type: new Abstract: Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties.
arXiv:2610.02668v1 Announce Type: new Abstract: Automated liquid handlers execute digital protocols, but operators must assemble the deck and confirm that the physical setup matches the intended exper
arXiv:2504.03432v4 Announce Type: replace-cross Abstract: Solving (Stampacchia) variational inequalities (SVIs) is a foundational problem at the heart of optimization. However, this expressivity comes
arXiv:2508.11902v4 Announce Type: replace Abstract: This study examines how much handwritten-character information is retained by a deliberately simple first-order edge representation. Instead of lear
arXiv:2610.02763v1 Announce Type: new Abstract: Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detec
arXiv:2511.18618v2 Announce Type: replace-cross Abstract: In our daily lives, newspapers are an essential information source that impacts how the public talks about present-day issues. However, effect
arXiv:2603.19655v2 Announce Type: replace Abstract: This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from pre
arXiv:2610.03034v1 Announce Type: cross Abstract: Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and qualit
arXiv:2610.03413v1 Announce Type: new Abstract: Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over
arXiv:2610.02831v1 Announce Type: new Abstract: Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local
arXiv:2610.03647v1 Announce Type: cross Abstract: Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that t
arXiv:2610.02421v1 Announce Type: new Abstract: Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased single-hair follicular units, and altered hair-shaft dia
arXiv:2602.09009v2 Announce Type: replace-cross Abstract: Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep la
arXiv:2610.02576v1 Announce Type: new Abstract: Systematic reviews condense clinical trials into evidence tables, yet clinicians can interrogate these tables only through database queries, and many qu
arXiv:2610.02253v1 Announce Type: new Abstract: The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization
arXiv:2610.03483v1 Announce Type: cross Abstract: We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tra
arXiv:2610.02673v1 Announce Type: cross Abstract: A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observation
arXiv:2505.13012v3 Announce Type: replace-cross Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy
arXiv:2610.03531v1 Announce Type: new Abstract: Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings whe
arXiv:2406.19892v5 Announce Type: replace Abstract: This article presents multilingual deep learning models for identifying web registers -- text varieties such as news reports and discussion forums -
arXiv:2610.02528v1 Announce Type: new Abstract: We introduce an autoregressive differentiable method to solve 0-1 integer programs. We fix an arbitrary order of the binary variables and we train a tra
arXiv:2607.09277v2 Announce Type: replace Abstract: Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require mol
arXiv:2610.02524v1 Announce Type: new Abstract: Unstructured pruning at extreme sparsity often suffers from representational collapse, causing sharp drops in accuracy. To address this, we study Backbo