Knowledge catalogue
arXiv:2605.14255v1 Announce Type: cross Abstract: Industrial visual inspection systems increasingly rely on deep classifiers whose heatmap explanations may appear visually plausible while failing to i
arXiv:2605.14689v1 Announce Type: new Abstract: Deep learning has profoundly impacted domains such as computer vision and natural language processing by uncovering complex patterns in vast datasets. H
arXiv:2605.14242v1 Announce Type: cross Abstract: The monitoring of fetal heart rate (FHR) and the assessment of its variability are crucial for preventing fetal compromise and adverse outcomes. Howev
arXiv:2605.05686v2 Announce Type: replace Abstract: Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM). We
arXiv:2506.01015v2 Announce Type: replace Abstract: Segment Anything Model 2 (SAM2) exhibits strong generalisation for promptable segmentation in video clips; however, its integration with the audio m
arXiv:2605.15082v1 Announce Type: cross Abstract: We study a prototypical situation when a learned predictor can discover useful low-dimensional structure in data, while using fewer samples than are n
arXiv:2605.14883v1 Announce Type: cross Abstract: Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages. Oculomotor dysfunction is a well-e
arXiv:2605.14106v1 Announce Type: new Abstract: We investigate whether behavior cloning is sufficient to produce active perception in a structured object-finding task. A low-cost robot arm equipped wi
arXiv:2603.28205v2 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) faces critical challenges due to representation entanglement and false-negative collisions in real-valued emb
arXiv:2605.14773v1 Announce Type: cross Abstract: Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly f
arXiv:2605.14169v1 Announce Type: new Abstract: Memory systems are critical for role-playing agents (RPAs) to maintain long-horizon consistency. However, existing RPA memory methods (e.g., profiling)
arXiv:2602.04265v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for enhancing reasoning in Large Language Models (LL
arXiv:2605.13887v1 Announce Type: cross Abstract: Transformer-based Spiking Neural Networks (SNNs) integrate SNNs with global self-attention and have demonstrated impressive performance. However, exis
arXiv:2605.14569v1 Announce Type: new Abstract: Reconstructing dynamic visual experiences as videos from functional magnetic resonance imaging (fMRI) is pivotal for advancing the understanding of neur
arXiv:2605.14810v1 Announce Type: new Abstract: In obstacle avoidance navigation of unmanned aerial vehicles (UAVs), variations in obstacle scale have received strangely less attention than obstacle n
arXiv:2512.11484v1 Announce Type: cross Abstract: This paper reveals and exploits a critical security vulnerability: the electromagnetic (EM) side channel of capacitive touchscreens leaks sufficient i
arXiv:2605.15141v1 Announce Type: new Abstract: Real-time interactive video generation requires low-latency, streaming, and controllable rollout. Existing autoregressive (AR) diffusion distillation me
arXiv:2605.15133v1 Announce Type: new Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variet
arXiv:2602.09969v2 Announce Type: replace Abstract: We study a canonical multi-task demand-learning problem motivated by retail pricing, where a firm seeks to estimate heterogeneous linear price-respo
arXiv:2602.04473v2 Announce Type: replace Abstract: Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform
arXiv:2202.05568v2 Announce Type: replace-cross Abstract: PAC-Bayes generalisation bounds are derived via change-of-measure inequalities that transfer concentration properties from a reference measure
arXiv:2605.14461v1 Announce Type: new Abstract: Existing object removal tools often rely on manual masks or text prompts, making precise removal difficult for non-expert users in complex scenes and of
arXiv:2511.14751v2 Announce Type: replace Abstract: We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the
arXiv:2605.14988v1 Announce Type: new Abstract: Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relation
arXiv:2605.15062v1 Announce Type: new Abstract: Background. RGB-trained capsule-endoscopy classifiers underperform on small-vessel vascular findings by conflating hemoglobin contrast with bile and ill
arXiv:2410.19653v3 Announce Type: replace Abstract: This paper introduces multimodal conformal regression. Traditionally confined to scenarios with solely numerical input features, conformal predictio
arXiv:2602.03814v2 Announce Type: replace Abstract: Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases, motivating ada
arXiv:2605.13884v1 Announce Type: cross Abstract: We propose uncommon self-knowledge (USK) as a candidate criterion for consciousness: synergistic information a system carries about itself that exists
arXiv:2605.14816v1 Announce Type: new Abstract: We describe the first experiment of conversion of Lexicon-Grammar tables for French verbs into the Lexical Markup Framework (LMF) format. The Lexicon-Gr
arXiv:2605.14310v1 Announce Type: new Abstract: Streaming video understanding with large vision-language models (VLMs) requires a compact memory that can support future reasoning over an ever-growing
arXiv:2605.14900v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs beco
arXiv:2605.13910v1 Announce Type: cross Abstract: We present a covariance-aware sampler that improves the quality of pixel-space Diffusion Model (DM) sampling in the few-step regime. We hypothesize th
arXiv:2605.14274v1 Announce Type: new Abstract: Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical
arXiv:2504.18544v3 Announce Type: replace-cross Abstract: Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review
arXiv:2605.15079v1 Announce Type: new Abstract: Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON-LD-based format that makes dataset discovery,
arXiv:2605.14480v1 Announce Type: new Abstract: Purpose: This study investigates the transcription principles underlying Huitongguanxi Huayiyiyu (HHY), a series of multilingual glossaries compiled by
arXiv:2605.14759v1 Announce Type: new Abstract: De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative model
arXiv:2605.14326v1 Announce Type: new Abstract: Remote sensing image generation provides a reliable data foundation for remote sensing large models and downstream tasks. However, existing controllable
arXiv:2508.01916v3 Announce Type: replace-cross Abstract: Understanding internal representations of neural models is a core interest of mechanistic interpretability. Due to its large dimensionality, t
arXiv:2603.00574v2 Announce Type: replace-cross Abstract: Adapting pretrained multi-modal models to evolving test-time distributions, known as multi-modal test-time adaptation, presents a significant
arXiv:2605.15009v1 Announce Type: new Abstract: The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based di
arXiv:2605.14270v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-to-image generation, yet they frequently suffer from concept omiss
arXiv:2605.14787v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) is a multimodal retrieval task where a query consists of a reference image and a textual modification, and the goal is
arXiv:2605.14621v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding
arXiv:2510.07060v2 Announce Type: replace Abstract: Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care abou
arXiv:2605.14632v1 Announce Type: new Abstract: Forecasting multivariate hidden Markov processes is challenging due to nonlinear and nonstationary observations, latent state transitions, and cross-seq
arXiv:2505.17353v2 Announce Type: replace-cross Abstract: Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a
arXiv:2605.15100v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable abilities in reasoning. However, maximizing their potential through inference-time scaling fac
arXiv:2605.14391v1 Announce Type: new Abstract: Learned image compression (LIC) increasingly requires reconstructions that balance distortion fidelity and perceptual realism across a wide range of bit
arXiv:2605.14104v1 Announce Type: new Abstract: Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing
arXiv:2605.14323v1 Announce Type: cross Abstract: We investigate the temporal concatenation of sub-policies in Markov Decision Processes (MDP) with time-varying reward functions. We introduce General
arXiv:2605.14742v1 Announce Type: new Abstract: Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing m
EBM are so back! @ylecun has been pointing here for years: AI reasoning needs systems that check structure before they answer. Aleph from @logic_int now leads the major formal reasoning benchmarks – l
Energy-Based Models (EBMs) have demonstrated renewed competitiveness in formal reasoning tasks, with Aleph achieving leading performance on major benchmarks in this domain. This represents a significa
arXiv:2605.14629v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for real-time, photorealistic scene reconstruction, offering significant speed-ups o
arXiv:2605.14553v1 Announce Type: cross Abstract: Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently
arXiv:2602.02427v2 Announce Type: replace Abstract: Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleadin
arXiv:2605.14833v1 Announce Type: new Abstract: Current language model systems remain fundamentally stateless across sessions, limiting their ability to personalize interactions over time. While retri
arXiv:2605.14411v1 Announce Type: cross Abstract: Quadruped robots are often designed with rigid feet to simplify control and maintain stable contact during locomotion. While this approach is straight
arXiv:2605.14521v1 Announce Type: new Abstract: Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference