Knowledge catalogue
arXiv:2607.19360v1 Announce Type: new Abstract: Large language models (LLMs) often answer queries by mapping individual observations to more general rule-like structures. However, it remains unclear h
arXiv:2607.19718v1 Announce Type: new Abstract: The HIPE-2026 shared task introduces person-place relation extraction from multilingual historical newspapers as a new evaluation track, classifying the
arXiv:2607.20411v1 Announce Type: cross Abstract: We prove strong laws of large numbers for locally Lipschitz functions in the Lipschitz pseudometric. Our results hold under either a topological or a
arXiv:2607.19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-t
arXiv:2607.20410v1 Announce Type: new Abstract: Value alignment of Large Language Models (LLMs) has been shown to be culturally biased toward Western norms. This results in the mishandling of local va
arXiv:2607.19866v1 Announce Type: new Abstract: Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applicat
arXiv:2607.18882v1 Announce Type: new Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.
arXiv:2607.20153v1 Announce Type: new Abstract: We study Gaussian averaging as a smooth surrogate for quantized neural models. Under bounded local oscillation, we derive a local dimension-dependent bo
arXiv:2607.19365v1 Announce Type: new Abstract: When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural langua
arXiv:2607.19914v1 Announce Type: new Abstract: We study finite-horizon MDP planning under root-based (resolute) risk objectives that apply a rank-dependent functional to the distribution of total ret
arXiv:2607.19777v1 Announce Type: new Abstract: Text-driven 3D scene editing with 3D Gaussian Splatting (3DGS) typically applies a 2D diffusion editor to views rendered from fixed training cameras, li
arXiv:2607.20357v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost,
arXiv:2607.19911v1 Announce Type: new Abstract: Long-range vehicle trajectories provide important spatio-temporal evidence for traffic safety analysis, autonomous driving evaluation, and data-driven t
arXiv:2607.19750v1 Announce Type: cross Abstract: In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition,
arXiv:2607.19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for t
arXiv:2607.19064v2 Announce Type: replace-cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale
arXiv:2607.14595v2 Announce Type: replace Abstract: Large-scale video diffusion models deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational
arXiv:2607.19426v1 Announce Type: cross Abstract: Single-cell datasets are increasingly costly to store, audit, and reuse for model training. Dimensionality reduction and dataset distillation can redu
arXiv:2607.19444v1 Announce Type: new Abstract: Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operat
arXiv:2607.19343v1 Announce Type: new Abstract: Video models absorb rich priors over how the visual world moves, interacts, and responds to contact, making them promising substrates for robotic world
arXiv:2512.05518v2 Announce Type: replace-cross Abstract: Open-source Large Language Models (LLMs) play a critical role in the democratization of AI, yet their 'open' nature introduces more avenues fo
arXiv:2607.15273v2 Announce Type: replace Abstract: MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient genera
arXiv:2604.03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even whe
arXiv:2607.19235v1 Announce Type: cross Abstract: Theory of Mind (ToM), the ability to infer other's beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challen
arXiv:2506.06488v3 Announce Type: replace Abstract: A key tool in developing safe AI models is data auditing, i.e., using statistical tools to determine whether harmful content may have been used in t
arXiv:2512.13998v4 Announce Type: replace-cross Abstract: Music Emotion Recognition (MER) is constrained by limited expert annotations and the need to establish robustness across heterogeneous corpora
arXiv:2607.19857v1 Announce Type: cross Abstract: Language-guided aerial perception aims to understand user-specified tiny targets in complex unmanned aerial vehicle (UAV) scenes. In real UAV deployme
arXiv:2607.19397v1 Announce Type: new Abstract: Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the
arXiv:2607.13315v2 Announce Type: replace Abstract: Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual sett
arXiv:2607.19530v1 Announce Type: new Abstract: Many Blind and Low-Vision (BLV) people rely on guide dogs for moment-to-moment navigation, such as staying on path and avoiding obstacles and pedestrian
arXiv:2607.18673v2 Announce Type: new Abstract: Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for
arXiv:2607.19027v1 Announce Type: new Abstract: Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and it
arXiv:2607.19371v1 Announce Type: new Abstract: Large language model (LLM)-based Socratic tutors increasingly guide students through multi-turn questioning, but they can suffer from scaffolding collap
arXiv:2607.19456v1 Announce Type: cross Abstract: We derive four memory-optimal inference artifacts for transformer attention using the Mathematics of Arrays (MoA), each following directly from the fo
arXiv:2607.19826v1 Announce Type: new Abstract: Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' parad
arXiv:2512.05462v2 Announce Type: replace-cross Abstract: Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations (MLOps
arXiv:2607.19479v1 Announce Type: cross Abstract: Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present
arXiv:2607.19935v1 Announce Type: new Abstract: Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Sub
arXiv:2607.17967v2 Announce Type: replace Abstract: Monocular geometry estimation has recently achieved impressive performance across diverse scenes. However, state-of-the-art models still face notabl
arXiv:2607.19714v1 Announce Type: new Abstract: Limbless robots offer exceptional mobility in confined and cluttered environments due to their slender bodies and their ability to exploit body-terrain
arXiv:2607.18789v1 Announce Type: new Abstract: Text-to-video generation has advanced significantly over the past five years through scaling of model size, data, and compute. Unlike model architecture
arXiv:2607.19886v1 Announce Type: new Abstract: Thermal-to-visible face translation presents fundamental challenges including geometric discontinuities, semantic attribute mismatches, and identity deg
arXiv:2607.19686v1 Announce Type: new Abstract: Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDM
arXiv:2607.20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ioni
arXiv:2607.17332v2 Announce Type: replace Abstract: Incorporating prior maps significantly enhances the accuracy and robustness of pose estimation in visual-inertial odometry (VIO). However, the large
arXiv:2607.20151v1 Announce Type: cross Abstract: Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from t
arXiv:2607.20284v1 Announce Type: new Abstract: The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU
arXiv:2607.19910v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs
arXiv:2601.13715v2 Announce Type: replace Abstract: Glass surface ubiquitous in both daily life and professional environments presents a potential threat to vision-based systems, such as robot and dro
arXiv:2607.19378v1 Announce Type: new Abstract: Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields an
arXiv:2607.19695v1 Announce Type: new Abstract: Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode
arXiv:2508.00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learn
arXiv:2607.19388v1 Announce Type: new Abstract: This work extends our one-dimensional single-sweep neural-operator studies to two dimensions. We consider one-group transport with isotropic scattering.
arXiv:2607.14186v4 Announce Type: replace-cross Abstract: Scaling executable agent training data for LLM post-training is bottlenecked by substrate-bound methods that tie task generation to predefined
arXiv:2607.19356v1 Announce Type: new Abstract: Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecution Utility a
arXiv:2607.18936v1 Announce Type: cross Abstract: We present NGPS (Next-Generation Positioning System), a visual geo-localization framework for high-altitude UAVs that provides GPS-free absolute posit
arXiv:2607.19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology. We
arXiv:2607.19288v1 Announce Type: new Abstract: Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing
arXiv:2607.20084v1 Announce Type: cross Abstract: Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negat
arXiv:2607.15702v2 Announce Type: replace-cross Abstract: We develop a non-asymptotic approximation, sampling, and finite-iteration optimization theory for variational physics-informed approximation o