Knowledge catalogue
arXiv:2601.09696v2 Announce Type: replace Abstract: LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patien
arXiv:2606.00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an an
arXiv:2411.05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI. Instead of model-native feature importance
arXiv:2303.04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffe
arXiv:2606.00078v1 Announce Type: cross Abstract: Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints.
arXiv:2606.00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embed
arXiv:2606.02498v1 Announce Type: new Abstract: This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We p
arXiv:2606.01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrati
arXiv:2511.21140v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as scalable evaluators of model responses in lieu of human annotators. However, imperfect sensiti
arXiv:2505.15354v2 Announce Type: replace Abstract: Time series forecasting models often produce systematic, predictable errors even in critical domains such as energy, finance, and healthcare. We int
arXiv:2606.01221v1 Announce Type: cross Abstract: Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performan
arXiv:2606.00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary tran
arXiv:2311.00260v2 Announce Type: replace-cross Abstract: In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for
arXiv:2605.26102v2 Announce Type: replace Abstract: In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions.
arXiv:2603.24324v4 Announce Type: replace-cross Abstract: Designing effective auxiliary rewards for cooperative multi-agent systems remains challenging, as misaligned incentives can induce suboptimal
arXiv:2504.16129v5 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) have demonstrated strong capabilities on complex agentic tasks requiring multifac
This post compiles recent AI news and developments from Microsoft and NVIDIA, likely covering topics such as NVIDIA's Cosmos 3 model and Nemotron 3 framework, along with other significant updates in t
arXiv:2603.06989v3 Announce Type: replace Abstract: This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthe
arXiv:2606.00891v1 Announce Type: new Abstract: Multi-modal Domain Generalization (MMDG) seeks to leverage complementary modalities to enhance model robustness on unseen domains. Despite extensive pro
arXiv:2606.02491v1 Announce Type: new Abstract: We present MORPHOS, a novel autoregressive framework that generates dynamic 3D assets from videos across diverse representations, including meshes, 3D G
arXiv:2602.23204v3 Announce Type: replace Abstract: In this work, we introduce the first framework for Motion-aware Event Suppression, which learns to filter events triggered by IMOs and ego-motion in
arXiv:2601.16884v3 Announce Type: replace Abstract: We study multigrade deep learning (MGDL) as a principled framework for structured error refinement in deep neural networks. While the approximation
arXiv:2606.00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoust
arXiv:2601.21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided test
arXiv:2606.00379v1 Announce Type: new Abstract: We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse vi
arXiv:2606.01192v1 Announce Type: new Abstract: Evaluating the performance of visual perception systems for autonomous driving is essential to ensure reliable operation across diverse environmental sc
arXiv:2606.01179v1 Announce Type: cross Abstract: Entropy production governs irreversibility and uncertainty in both physical and information-theoretic systems. While Physics-Informed Neural Networks
arXiv:2606.02008v1 Announce Type: cross Abstract: Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample comp
arXiv:2606.00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzi
arXiv:2606.01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures. Single-run feature selection produces feature sets
arXiv:2510.24870v2 Announce Type: replace Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a preval
This likely describes a framework or system for deploying AI agents that autonomously improve their performance over time through continuous learning and adaptation in enterprise environments. The sel
arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient
arXiv:2411.17790v3 Announce Type: replace-cross Abstract: Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reli
In the era of artificial intelligence, some companies have struggled to adopt artificial intelligence and others have pivoted to an AI framework that has yielded positive results. Snowflake Inc. this
arXiv:2606.01701v1 Announce Type: new Abstract: Geometric partitioning has attracted increasing attention by its remarkable motion field description capability in the hybrid video coding framework. Ho
The Quarq agent is constructed using LangGraph, a framework that simplifies the development of complex memory systems. Quarq has achieved a top ranking on the LongMemEval benchmark, demonstrating the
arXiv:2606.00592v1 Announce Type: new Abstract: Effective visual communication stems from the harmony of multiple design principles, such as readability, contrast, alignment, overlap, and coherence, w
arXiv:2606.01380v1 Announce Type: new Abstract: In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inver
arXiv:2602.12724v3 Announce Type: replace Abstract: This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL
President Donald Trump signed an executive order Tuesday creating a 'voluntary framework' for AI companies to share their frontier models with the federal government before they're released 'to promot
arXiv:2606.02038v1 Announce Type: cross Abstract: Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis
arXiv:2606.01595v1 Announce Type: new Abstract: Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather
arXiv:2606.00724v1 Announce Type: cross Abstract: Diffusion Large Language Models (DLMs) have demonstrated significant advantages across various tasks. However, constrained by their multi-step iterati
arXiv:2606.02060v1 Announce Type: new Abstract: Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis. Evaluation based on final ans
arXiv:2511.05613v2 Announce Type: replace-cross Abstract: Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risk
arXiv:2606.00086v1 Announce Type: new Abstract: Inverse kinematics (IK) is a fundamental problem in robotics, requiring the generation of joint configurations that satisfy target end-effector poses. E
arXiv:2602.01399v2 Announce Type: replace-cross Abstract: The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal i
arXiv:2605.31147v1 Announce Type: cross Abstract: User Experience Research (UXR) Points of View (POVs) distil complex and often fragmented research evidence into actionable perspectives that guide how
arXiv:2602.00521v2 Announce Type: replace Abstract: While LLM-as-a-Judge is widely used in automated evaluation, existing validation practices primarily operate at the level of observed outputs, offer
arXiv:2605.31532v1 Announce Type: cross Abstract: Constitutive laws for inelastic materials must satisfy strict thermodynamic admissibility requirements, yet current data-driven approaches sacrifice i
arXiv:2605.31250v1 Announce Type: cross Abstract: We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target
arXiv:2605.31143v1 Announce Type: cross Abstract: Rising household debt and cost-of-living pressures in the United Kingdom have intensified the role of AI-driven financial technologies in mediating cr
arXiv:2504.10564v3 Announce Type: replace-cross Abstract: We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates conti
arXiv:2603.08651v2 Announce Type: replace Abstract: We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning
arXiv:2605.30465v1 Announce Type: new Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. W
arXiv:2605.31005v1 Announce Type: new Abstract: We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by
This post likely discusses how the LangChain framework (which Harrison Chase co-founded) can assist developers in constructing more effective AI agents by providing tools, abstractions, and patterns f
arXiv:2605.30380v1 Announce Type: new Abstract: Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-
arXiv:2411.00759v5 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unl