Knowledge catalogue
arXiv:2509.10818v2 Announce Type: replace Abstract: When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence.
arXiv:2608.17341v1 Announce Type: new Abstract: AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represen
arXiv:2608.17330v1 Announce Type: new Abstract: Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may
arXiv:2603.26764v2 Announce Type: replace Abstract: Portable CT scanners may support earlier stroke assessment, but reduced photon counts introduce noise that affects image quality and may alter autom
arXiv:2409.14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embedd
arXiv:2608.17151v1 Announce Type: new Abstract: Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context
arXiv:2504.20131v4 Announce Type: replace-cross Abstract: We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capab
arXiv:2608.17695v1 Announce Type: new Abstract: Flow matching models for video generation achieve impressive performance but suffer from high computational overhead due to iterative denoising. In fact
arXiv:2608.17753v1 Announce Type: new Abstract: Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hou
arXiv:2608.16959v1 Announce Type: cross Abstract: Breast cancer is one of the most common types of cancer among women around the world. Rapid detection and early treatment can hinder its progress to m
arXiv:2608.17635v1 Announce Type: new Abstract: Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to
arXiv:2608.17244v1 Announce Type: cross Abstract: This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and hete
arXiv:2601.07398v2 Announce Type: replace-cross Abstract: Polarization research has demonstrated how people cluster in homogeneous groups with opposing opinions. However, this effect emerges not only
arXiv:2608.18009v1 Announce Type: new Abstract: Answering questions accurately and efficiently in embodied scenarios presents significant challenges due to limited computational and memory resources f
arXiv:2608.17402v1 Announce Type: new Abstract: Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling
arXiv:2506.10775v3 Announce Type: replace Abstract: In monotone classification, the input is a multi-set P of points in R^d, each associated with a hidden label from {-1, 1}. The goal is to identify a
arXiv:2608.17848v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical
arXiv:2603.09872v2 Announce Type: replace Abstract: Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they
arXiv:2608.17741v1 Announce Type: new Abstract: OWL 2 DL ontologies, grounded in the description logic SROIQ, express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) l
arXiv:2608.17487v1 Announce Type: new Abstract: Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convol
arXiv:2608.17394v1 Announce Type: new Abstract: Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in re
arXiv:2608.17450v1 Announce Type: cross Abstract: Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intr
arXiv:2509.05574v3 Announce Type: replace-cross Abstract: We prove that, for many standard link invariants, both the proportion of distinct invariant values and the detection probability among prime a
arXiv:2408.13323v3 Announce Type: replace-cross Abstract: Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we constr
arXiv:2608.17374v1 Announce Type: cross Abstract: Motivated by a conjecture of Vaikuntanathan and Zamir, we study the pseudo-mixing of Kac's walk on SO(n): whether short trajectories are indistinguish
arXiv:2608.17455v1 Announce Type: cross Abstract: Purcell's swimmer is a well-known planar model of a swimming microorganism, governed by low Reynolds number hydrodynamics, which is comprised of three
arXiv:2608.18040v1 Announce Type: cross Abstract: Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. Wh
arXiv:2406.08534v4 Announce Type: replace-cross Abstract: This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockyard
arXiv:2603.23047v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document questio
arXiv:2608.17926v1 Announce Type: new Abstract: Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre
arXiv:2608.17213v1 Announce Type: new Abstract: This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical pr
arXiv:2608.17171v1 Announce Type: new Abstract: Many table-centric NLP tasks such as NL2SQL first retrieve relevant tables from large collections using keyword search. Recent work uses LLMs to generat
arXiv:2608.17174v1 Announce Type: new Abstract: Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes
arXiv:2311.06273v1 Announce Type: cross Abstract: The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing i
arXiv:2608.17781v1 Announce Type: new Abstract: ML systems increasingly condition decisions on downstream model identity, but this is useful only if model-specific differences form reusable structure
arXiv:2608.17351v1 Announce Type: new Abstract: Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target doma
arXiv:2608.18055v1 Announce Type: cross Abstract: Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan
arXiv:2608.17224v1 Announce Type: new Abstract: Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, ma
arXiv:2608.17947v1 Announce Type: new Abstract: Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than indivi
arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction
arXiv:2608.17182v1 Announce Type: cross Abstract: We propose a remeshing-enhanced method for generatively deforming shapes with visual losses. It is intuitive that sufficiently drastic deformations of
arXiv:2608.17981v1 Announce Type: new Abstract: We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across
arXiv:2608.17324v1 Announce Type: new Abstract: The continuously deformable geometry of modular robots makes it difficult to define a fixed representation for reconfiguration planning and analysis. Th
arXiv:2608.17623v1 Announce Type: new Abstract: Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical C
arXiv:2608.18021v1 Announce Type: new Abstract: WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size
arXiv:2608.17320v1 Announce Type: new Abstract: Brachiation is a form of locomotion in which primates move primarily using their arms, enabling traversal in environments without footholds. However, th
arXiv:2608.17803v1 Announce Type: new Abstract: Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a
arXiv:2608.17553v1 Announce Type: new Abstract: Cameras are ubiquitous sensors in robotics due to their compact form factor and the perceptual richness captured through visual information. Monocular S
arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complic
arXiv:2409.06311v2 Announce Type: replace Abstract: This work investigates the potential of seam carving as a feature pooling technique within Convolutional Neural Networks (CNNs) for image classifica
arXiv:2608.17426v1 Announce Type: cross Abstract: We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires both achie
arXiv:2608.17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal prediction reg
arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic mat
arXiv:2608.17822v1 Announce Type: cross Abstract: Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments
arXiv:2401.02020v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-atten
arXiv:2608.17598v1 Announce Type: new Abstract: Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliab
arXiv:2608.17443v1 Announce Type: new Abstract: Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structur
arXiv:2608.16963v1 Announce Type: new Abstract: Learning analytics often treats unsupervised clusters of intelligent tutoring system (ITS) logs as learner types that should predict learning. We test t
arXiv:2608.17421v1 Announce Type: new Abstract: Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level
arXiv:2205.11930v3 Announce Type: replace-cross Abstract: Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotator