Knowledge catalogue
arXiv:2605.14136v1 Announce Type: new Abstract: Recent text-to-video diffusion transformers generate visually compelling frames, yet still struggle with temporal coherence, often producing flickering,
arXiv:2603.12529v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables th
arXiv:2605.14896v1 Announce Type: cross Abstract: This paper presents a system for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge. The system achieved a Minimum Detection Cost Function
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How Chinese short dramas became AI content machines China’s sh
arXiv:2605.15011v1 Announce Type: new Abstract: Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmap
arXiv:2605.14819v1 Announce Type: new Abstract: While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity
Every year the World Health Organization publishes a global health statistics report. It features the numbers behind world health trends and, importantly, assesses whether we’re on track to reach ambi
There is a case to be made that the future of Mathematics is very bright. In my mind, proofs have always been a tool to achieve a goal. The goal was and still is to understand, and reading/writing pro
arXiv:2502.16060v5 Announce Type: replace-cross Abstract: Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokeni
arXiv:2605.15035v1 Announce Type: new Abstract: We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. To
arXiv:2605.14601v1 Announce Type: new Abstract: Three-dimensional object detection in panoramic imagery is crucial for comprehensive scene understanding, yet accurately mapping 2D features to 3D remai
arXiv:2605.14717v1 Announce Type: cross Abstract: Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directl
arXiv:2510.07118v3 Announce Type: replace Abstract: Instruction tuning is essential for aligning large language models (LLMs) to downstream tasks and commonly relies on large, diverse corpora. However
arXiv:2605.14570v1 Announce Type: new Abstract: Large Language Diffusion Models (LLDMs) are emerging as an alternative to autoregressive models, offering faster inference through higher parallelism. S
arXiv:2605.14156v1 Announce Type: new Abstract: While generative models have shown promise in pediatric sleep analysis, the latent structure of their multimodal embeddings remains poorly understood. T
arXiv:2605.14785v1 Announce Type: cross Abstract: Neural networks suffer from catastrophic forgetting in class-incremental learning (CIL) settings. Rehearsalnicode{x2013}replaying a subset of past sam
arXiv:2511.18820v2 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations, yet their success in simulating incomp
arXiv:2603.18943v2 Announce Type: replace Abstract: This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-indep
arXiv:2605.15186v1 Announce Type: cross Abstract: High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environ
arXiv:2605.14733v1 Announce Type: new Abstract: Self-evolution offers a promising path for improving reasoning models without relying on intensive human annotation. However, extending this paradigm to
arXiv:2605.14645v1 Announce Type: cross Abstract: With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant
arXiv:2605.14597v1 Announce Type: new Abstract: Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of
arXiv:2605.14041v1 Announce Type: cross Abstract: Deep learning excels at prediction but often lacks finite-sample guarantees and calibrated uncertainty; RKHS (Reproducing Kernel Hilbert Space)-based
arXiv:2605.13959v1 Announce Type: cross Abstract: Generative policies based on diffusion and flow matching have become a dominant paradigm for visuomotor robotic control. We show that replacing the st
arXiv:2605.14283v1 Announce Type: cross Abstract: Watermarking techniques for large language models (LLMs), which encode hidden information in the output so its source can be verified, have gained sig
arXiv:2605.14224v1 Announce Type: cross Abstract: We present an in-depth analysis of the Koopman semigroup via wavelet transform. Towards this goal, we start by introducing the wavelet-based observabl
arXiv:2605.14449v1 Announce Type: cross Abstract: Hallucination detection in large language models (LLMs) requires balancing accu racy, efficiency, and robustness to distribution shift. Black-box cons
arXiv:2605.15183v1 Announce Type: new Abstract: Mechanistic interpretability aims to break models into meaningful parts; verifying that two such parts implement the same computation is a prerequisite.
arXiv:2605.14115v1 Announce Type: new Abstract: Biomedical retrieval-augmented large language models (LLMs) often face evidence that is incomplete, misleading, or internally contradictory, yet evaluat
arXiv:2605.14368v1 Announce Type: cross Abstract: Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language d
arXiv:2605.14192v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in ret
arXiv:2603.09921v3 Announce Type: replace Abstract: Open-domain visual entity recognition (VER) seeks to associate images with entities in encyclopedic knowledge bases such as Wikipedia. Recent genera
arXiv:2605.13979v1 Announce Type: cross Abstract: Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for
arXiv:2605.13922v1 Announce Type: cross Abstract: During the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), ha
Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not cre
arXiv:2605.14893v1 Announce Type: cross Abstract: Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural
arXiv:2505.21238v3 Announce Type: replace Abstract: Novel view synthesis for underwater scene reconstruction presents unique challenges due to complex light-media interactions. Optical scattering and
arXiv:2605.13142v1 Announce Type: new Abstract: In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As
arXiv:2506.04165v3 Announce Type: replace Abstract: We consider the Top-K selection problem, which aims to identify the largest K elements in an array. Top-K selection arises in many machine learning
arXiv:2605.13687v1 Announce Type: cross Abstract: We introduce a family of synthetic languages with hierarchical structure -- generated by a broadcast process on trees -- for which the role of context
arXiv:2605.13367v1 Announce Type: cross Abstract: The literature on ontology-mediated query answering (OMQA) has been shaped by two key results: first-order rewritability for DL-Lite, and PTime-hardne
arXiv:2507.19247v5 Announce Type: replace-cross Abstract: Autoregressive language models achieve remarkable performance, yet a unified theory explaining their internal mechanisms, how training shapes
arXiv:2605.12706v1 Announce Type: new Abstract: RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the lim
arXiv:2605.12697v1 Announce Type: cross Abstract: Length-dependent logit rescaling is widely used to stabilize long-context self-attention, but existing analyses and methods suggest conflicting invers
arXiv:2504.03158v2 Announce Type: replace-cross Abstract: In this work, we propose a new particle-based variational inference (ParVI) method for accelerating the Energetic Variational Inference with I
arXiv:2605.13301v1 Announce Type: new Abstract: Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reachi
arXiv:2605.13010v1 Announce Type: cross Abstract: We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrai
arXiv:2605.13128v1 Announce Type: cross Abstract: This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networ
arXiv:2605.12929v1 Announce Type: cross Abstract: Retinal diagnosis is inherently bilateral: clinicians compare homologous structures across eyes (e.g., optic disc asymmetry), yet most deep models ope
APPLEBAUM: Russia's war in Ukraine is sometimes described, including recently by American Vice President, as if it were nothing more than territorial dispute, kind of scuffle over lines on map. But wh
arXiv:2605.13517v1 Announce Type: cross Abstract: Vector Quantized Variational Autoencoder (VQ-VAE) has become a fundamental framework for learning discrete representations in image modeling. However,
arXiv:2605.12575v1 Announce Type: cross Abstract: Whole-slide image (WSI) multiple instance learning (MIL) classifiers can achieve strong slide-level AUC while leaving the full-bag prediction opaque.
arXiv:2605.12845v1 Announce Type: cross Abstract: Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF
arXiv:2605.13450v1 Announce Type: new Abstract: Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for enhancing our scientific
arXiv:2605.12964v1 Announce Type: new Abstract: Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has s
arXiv:2605.13381v1 Announce Type: new Abstract: As AI-generated synthetic images become increasingly realistic, Vision Transformers (ViTs) have emerged as a cornerstone of modern deepfake detection. H
arXiv:2605.13214v1 Announce Type: cross Abstract: Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a clean model
arXiv:2605.13088v1 Announce Type: new Abstract: Dynamical modelling is central to many scientific domains, including pharmacometrics, systems biology, physiology, and epidemiology. In these settings,
arXiv:2510.01502v2 Announce Type: replace-cross Abstract: Current video foundation models, including the strongest self-supervised models such as V-JEPA2, fail to capture how humans organize social in
arXiv:2605.12894v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with a wide variety of people, including users who are uncle