Knowledge catalogue
arXiv:2605.16519v1 Announce Type: new Abstract: Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent chal
arXiv:2603.04870v2 Announce Type: replace Abstract: Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in re
arXiv:2605.17262v1 Announce Type: new Abstract: Despite extensive efforts on egocentric video datasets and benchmarks, understanding users' internal states, which is crucial for enabling seamless AI a
arXiv:2605.16745v1 Announce Type: new Abstract: This paper addresses the challenge of integrating 3D meshes as a native modality within Multimodal Large Language Models (MLLMs). Diffusion-based large
arXiv:2605.18144v1 Announce Type: new Abstract: Nanomedicine research spans delivery chemistry, immunology, imaging, biomaterials, and disease-specific translational science, yet its conceptual design
arXiv:2510.08886v3 Announce Type: replace Abstract: Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale di
arXiv:2505.20650v5 Announce Type: replace-cross Abstract: Accurate interpretation of numerical data in financial reports is critical for markets and regulators. Although XBRL (eXtensible Business Repo
arXiv:2603.17588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently.
arXiv:2605.18365v1 Announce Type: new Abstract: Generating geometrically consistent videos remains an open challenge: text-to-video diffusion models trained on web-scale data treat geometry only impli
arXiv:2512.23752v5 Announce Type: replace-cross Abstract: Recent work has shown that small transformers trained in controlled 'wind-tunnel'' settings can implement exact Bayesian inference, and that t
arXiv:2605.18543v1 Announce Type: new Abstract: Autonomous ground vehicles operating in shallow water or flood-prone terrains require dynamic models that account for hydrodynamic forces. However, the
arXiv:2605.16345v1 Announce Type: cross Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divide
arXiv:2411.03936v2 Announce Type: replace Abstract: Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires emplo
A developer created a terminal user interface (TUI) tool that enables local large language models (LLMs) running on Ollama or cloud platforms to debate each other as a method for detecting and reducin
arXiv:2603.20873v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with privat
arXiv:2409.15980v2 Announce Type: replace-cross Abstract: Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accura
arXiv:2601.02353v3 Announce Type: replace Abstract: Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performan
arXiv:2605.18483v1 Announce Type: cross Abstract: Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of
arXiv:2510.16814v2 Announce Type: replace-cross Abstract: Archaeological predictive modelling estimates where undiscovered sites are likely to occur by combining known locations with environmental, cu
arXiv:2605.17780v1 Announce Type: new Abstract: Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent 'black-box
arXiv:2605.16962v1 Announce Type: cross Abstract: Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the
arXiv:2605.18262v1 Announce Type: new Abstract: Accurate pedestrian trajectory prediction is crucial for autonomous systems operating in complex environments, such as modular buses and delivery robots
arXiv:2605.16363v1 Announce Type: new Abstract: Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective
arXiv:2605.17860v1 Announce Type: cross Abstract: While modern Automatic Speech Recognition (ASR) systems achieve high accuracy on benchmark corpora, their performance often degrades when there is rea
arXiv:2510.16609v3 Announce Type: replace-cross Abstract: Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model's paramet
arXiv:2605.18537v1 Announce Type: cross Abstract: This paper introduces the Manifold Probe, a supervised method for discovering representation manifolds in superposition. The method generalizes linear
arXiv:2605.16407v1 Announce Type: cross Abstract: We present a framework for verifying the deterministic structured computations surrounding a large language model rather than the model itself, extend
arXiv:2605.18552v1 Announce Type: new Abstract: Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoi
arXiv:2605.18333v1 Announce Type: cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in mult
arXiv:2605.17566v1 Announce Type: new Abstract: With the rapid progress of multimodal foundation models and predictive pre-training, an important open question is how to equip 3D point clouds with a p
arXiv:2605.17787v1 Announce Type: new Abstract: It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptive optimizers such as Adam in pre-training Large La
arXiv:2603.04639v2 Announce Type: replace-cross Abstract: Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipula
arXiv:2605.18719v1 Announce Type: new Abstract: Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, e
arXiv:2510.26635v3 Announce Type: replace-cross Abstract: Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for
arXiv:2605.18101v1 Announce Type: cross Abstract: Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studie
arXiv:2605.17789v1 Announce Type: cross Abstract: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings. Th
arXiv:2505.19155v2 Announce Type: replace-cross Abstract: Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence l
arXiv:2605.18736v1 Announce Type: new Abstract: Diffusion models have been shown to implicitly generate visual content autoregressively in the frequency domain, where low-frequency components are gene
arXiv:2605.18267v1 Announce Type: new Abstract: Normalizing flows (NFs) provide exact likelihoods and deterministic invertible sampling, but have historically lagged behind diffusion models for large-
arXiv:2511.19320v2 Announce Type: replace Abstract: Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Bindi
arXiv:2511.19953v2 Announce Type: replace Abstract: Accurate nuclear instance segmentation is a pivotal task in computational pathology, supporting data-driven clinical insights and facilitating downs
arXiv:2605.16361v1 Announce Type: cross Abstract: We present TailedTS, a large-scale benchmark dataset derived from Wikipedia hourly page view observations throughout 2024, specifically designed to te
arXiv:2602.21185v2 Announce Type: replace Abstract: Uniform-state discrete diffusion models excel at few-step generation and guidance due to their ability to self-correct, making them preferred over a
arXiv:2605.18079v1 Announce Type: cross Abstract: Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconn
arXiv:2605.17606v1 Announce Type: new Abstract: In wide neural networks, the Neural Tangent Kernel (NTK) remains approximately constant during training, providing a powerful theoretical tool for study
arXiv:2605.17500v1 Announce Type: cross Abstract: Generative text-to-image models are typically trained on large-scale web-scraped datasets that include diverse visual content such as copyrighted and
arXiv:2605.16909v1 Announce Type: new Abstract: Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate
Today we release Contrastive Neuron Attribution (CNA), a method for steering LLM behavior by identifying and ablating sparse circuits in the MLP basis without training a sparse autoencoder, modifying
arXiv:2605.17064v1 Announce Type: new Abstract: Large language models optimized for instruction following and agentic tasks remain poorly aligned with the requirements of high-quality creative writing
arXiv:2605.16638v1 Announce Type: new Abstract: Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this paradig
arXiv:2605.17942v1 Announce Type: new Abstract: Feed-forward 3D reconstruction has recently demonstrated strong generalization across diverse scenes, yet its performance in UAV imagery remains underex
arXiv:2605.18022v1 Announce Type: cross Abstract: Highly over-parameterized models can simultaneously memorize noisy labels and generalize well, yet how these behaviors coexist remains poorly understo
arXiv:2605.18749v1 Announce Type: cross Abstract: Modern audio generation predominantly relies on latent-space compression, introducing additional complexity and potential information loss. In this wo
arXiv:2510.16252v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for web agents demands environments that are both effective for evaluation and efficient enough for large-scale on
We’re releasing Nemotron-Labs-Diffusion - the first Tri-mode LM family (3B/8B/14B) that switches between 1⃣Autoregressive, 2⃣Diffusion, and 3⃣Self-Speculation decoding by simply changing the attention
arXiv:2601.16398v2 Announce Type: replace-cross Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of
arXiv:2605.16877v1 Announce Type: new Abstract: Zero-shot textual explanations aim to make image classifiers more transparent by probing their internal representations, without relying on task-specifi
arXiv:2601.03707v2 Announce Type: replace Abstract: Existing UAV vision-and-language navigation (VLN) benchmarks rarely provide realistic aerial scenes, natural process-level instructions, and suffici
arXiv:2605.15793v1 Announce Type: new Abstract: Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose
arXiv:2308.06822v3 Announce Type: replace-cross Abstract: Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model w