Knowledge catalogue
arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large a
arXiv:2512.22671v2 Announce Type: replace Abstract: Structured width pruning of GLU-MLP layers, guided by the Maximum Absolute Weight (MAW) criterion, reveals a systematic dichotomy in how reducing th
arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications.
arXiv:2604.01496v2 Announce Type: replace-cross Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight fronti
Today, we’re thrilled to announce that Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model yet, is now generally available. Designed for ultra-low latency, high-volume tas
arXiv:2605.04604v1 Announce Type: cross Abstract: High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum c
arXiv:2605.03383v1 Announce Type: new Abstract: Lithology classification aims to infer subsurface rock types from well-logging signals, supporting downstream applications like reservoir characterizati
arXiv:2603.26178v2 Announce Type: replace Abstract: We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning through expl
arXiv:2605.04474v1 Announce Type: new Abstract: Geometry is central to PDE-governed systems, motivating shape optimization and inversion. Classical pipelines conduct costly forward simulation with geo
arXiv:2605.03410v2 Announce Type: new Abstract: Out-of-distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment
arXiv:2605.04502v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) often struggle to train reliably on stiff and oscillatory dynamical systems due to poor optimization conditioni
arXiv:2605.04581v1 Announce Type: new Abstract: Light field (LF) image super-resolution benefits from Epipolar Plane Images (EPIs), whose line slopes explicitly encode disparity. However, existing Tra
arXiv:2605.04525v1 Announce Type: new Abstract: Recent advances in generative models have shown promise in generating behavior plans for long-horizon, sparse reward tasks. While these approaches have
arXiv:2602.23010v3 Announce Type: replace-cross Abstract: We present Helmlab, a family of two purpose-built color spaces for UI design systems sharing a common 11-stage analytical structure: MetricSpa
arXiv:2605.04103v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and effi
Hermes is an AI model developed by Nous Research that focuses on instruction-following and reasoning capabilities. Based on Nous Research's focus, this likely covers the model's architecture, performa
Nous Research announced improvements or updates to their Hermes Agent system, highlighting enhanced capabilities or performance. The post was shared on X (formerly Twitter), suggesting it covers recen
arXiv:2605.04191v1 Announce Type: cross Abstract: Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing or
arXiv:2605.04257v1 Announce Type: new Abstract: Cold spraying is an increasingly common approach for repairing and manufacturing components due to its solid-state manufacturing capabilities. However,
arXiv:2605.04853v1 Announce Type: new Abstract: We propose HIN-LRI, a hybrid framework that augments a classical numerical solver with a neural operator trained to correct the solver's structured trun
arXiv:2605.04977v1 Announce Type: new Abstract: This companion paper reports the ICPR 2026 TVRID competition on privacy-aware top-view person re-identification. We present the competition setting, the
arXiv:2507.15736v2 Announce Type: replace Abstract: Innovation is a key driving force of human civilization. As the body of knowledge has grown considerably, bridging knowledge across different discip
arXiv:2605.04506v1 Announce Type: new Abstract: We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior
arXiv:2605.04262v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and
arXiv:2605.04825v1 Announce Type: new Abstract: Factorization machine with quadratic-optimization annealing (FMQA) is a black-box optimization method that combines a factorization machine (FM) surroga
arXiv:2605.04475v1 Announce Type: new Abstract: Reliable autonomous driving requires scene understanding that is semantically consistent across heterogeneous sensors and verifiable at the reasoning st
arXiv:2605.04355v1 Announce Type: new Abstract: Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often
arXiv:2605.04671v1 Announce Type: new Abstract: Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy.
arXiv:2605.04932v1 Announce Type: cross Abstract: We study long-horizon deployment of a frozen predictor under dynamic covariate shift. A time-domain Poincare inequality reduces temporal risk volatili
arXiv:2605.04425v1 Announce Type: new Abstract: Vision-language models such as CLIP achieve strong visual-textual alignment, but often suffer from overfitting and limited interpretability when adapted
arXiv:2605.05125v1 Announce Type: new Abstract: Target trial emulation (TTE) enables causal questions to be studied with observational data when randomized controlled trials (RCTs) are infeasible. Yet
arXiv:2602.04129v2 Announce Type: replace Abstract: Heterogeneous multi-robot systems are increasingly used in long-horizon missions requiring coordinated planning across diverse capabilities. However
arXiv:2605.04917v1 Announce Type: new Abstract: Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal
arXiv:2510.08431v3 Announce Type: replace Abstract: Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diff
arXiv:2512.04388v5 Announce Type: replace Abstract: Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In
arXiv:2605.05026v1 Announce Type: new Abstract: Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underly
arXiv:2605.05187v1 Announce Type: new Abstract: This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and
arXiv:2605.05134v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination. While existing detection method
arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed
arXiv:2511.05080v4 Announce Type: replace Abstract: The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer sol
arXiv:2605.04180v1 Announce Type: new Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, a
arXiv:2605.03675v1 Announce Type: new Abstract: Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over
arXiv:2602.02282v2 Announce Type: replace Abstract: Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-
Multi-Token Prediction (MTP) for LLaMA.cpp! Running Gemma4 local model 1.5x faster. We patched LLaMA.cpp. Quantized Gemma 4 assistant models into GGUF format. We ran tests on a MacBook Pro M5Max. Gemm
arXiv:2605.04208v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages, yet their performance on low-resource
arXiv:2605.04566v1 Announce Type: cross Abstract: Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies
arXiv:2605.05185v1 Announce Type: new Abstract: Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence v
arXiv:2412.08893v3 Announce Type: replace Abstract: Optimal control and sequential decision making are widely used in many complex tasks. Optimal control over a sequence of natural images is a first s
arXiv:2605.05084v1 Announce Type: new Abstract: Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address this b
arXiv:2605.04665v1 Announce Type: new Abstract: When the substantive content of a request is rewritten, do large language models still answer in the format the original task asked for? We find that th
arXiv:2605.04003v1 Announce Type: cross Abstract: High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledg
arXiv:2605.05049v1 Announce Type: cross Abstract: Frontier models increasingly adopt Mixture-of-Experts (MoE) architectures to achieve large-model performance at reduced cost. However, training MoE mo
arXiv:2502.12370v3 Announce Type: replace Abstract: Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting
arXiv:2605.05040v1 Announce Type: new Abstract: On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a st
arXiv:2601.09195v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional S
arXiv:2605.03546v1 Announce Type: cross Abstract: Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain,
arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient
arXiv:2605.04497v1 Announce Type: new Abstract: Shapley values are a standard tool for explaining predictions of tree ensembles, with Path-Dependent SHAP being the most widely used variant. Despite su
arXiv:2605.04847v1 Announce Type: new Abstract: Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge. In graph settings
This discussion examines the practical performance gains of quantization techniques when deployed in production environments, particularly addressing whether theoretical speedups translate to real-wor