Search: “models”

Human-Centered Learning Mechanics: A Dynamical Framework for Entropy-Regulated Representation Learning

arXiv:2605.22940v1 Announce Type: cross Abstract: Deep learning is increasingly viewed as a dynamical process in parameter space, yet many existing theories still treat training as a closed optimizati

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction

DGX agent

arXiv:2605.23187v1 Announce Type: new Abstract: Existing object navigation benchmarks usually tell an embodied agent which object category to find, such as microwave or chair. Human-facing embodied AI

model-releasesarxiv-cs-cv

25 May 2026

Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures

arXiv:2511.03882v2 Announce Type: replace-cross Abstract: Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether thi

L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark

arXiv:2605.22893v1 Announce Type: cross Abstract: We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to fost

LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics

arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Rese

LLM Sparsity Prior for Robust Feature Selection

arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information for high-dimensional variable selection. However,

Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations

arXiv:2601.14300v3 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite rece

MARS: Magnitude-Aware Rank Statistics

arXiv:2605.23563v1 Announce Type: new Abstract: Comprehensive evaluation of machine learning models is the key to make sure that they perform as robustly and consistently as desired. In order to summa

Applications

MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders

arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision

applicationsarxiv-cs-ai

MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection

arXiv:2602.13480v2 Announce Type: cross Abstract: Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull de

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer

arXiv:2605.23871v1 Announce Type: cross Abstract: We develop a gradient flow on the space of probability measures defined on matrix-valued parameters induced by regularized Muon, an analytically smoot

Tutorials

Nonlinear Transformations Against Unlearnable Datasets

arXiv:2406.02883v2 Announce Type: replace Abstract: Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studie

tutorialsarxiv-cs-lg

NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion

arXiv:2511.11051v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) fusion enables the composition of subject and style representations for controllable generation without retraining. Howev

PhotoFlow: Agentic 3D Virtual Photography Missions

arXiv:2605.23771v1 Announce Type: cross Abstract: Virtual photography asks an agent to enter a prepared 3D scene with no preselected camera pose or reference image, infer a suitable shot from scene in

Hardware

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion

arXiv:2605.23902v1 Announce Type: new Abstract: Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space

hardwarearxiv-cs-cv

Pooling and Semantic Shift: The Fundamental Challenges in Long Text Embedding and Retrieval

arXiv:2603.21437v2 Announce Type: replace Abstract: Transformer-based embedding models frequently exhibit geometric pathologies, such as anisotropy and length-induced representation collapse, which ca

researcharxiv-cs-cl

ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation

arXiv:2605.04118v2 Announce Type: replace-cross Abstract: Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain diffic

Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology

arXiv:2605.22886v1 Announce Type: cross Abstract: AI-native wireless receivers based on deep learning exhibit remarkable performance under stationary channel conditions, yet their resilience to distri

Revitalizing Dense Material Segmentation: Stabilized Vision Transformers and the Generalization Paradox

arXiv:2605.23747v1 Announce Type: new Abstract: Material segmentation, the pixel-wise classification of physical surface properties, remains a challenging problem in computer vision, requiring physico

Applications

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting

arXiv:2509.15105v3 Announce Type: replace Abstract: Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across divers

applicationsarxiv-cs-lg

The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management

arXiv:2605.23071v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on long-context processing, but expanding context windows introduces substantial computational and financ

researcharxiv-cs-cl

Tutorials

Uncovering the Latent Potential of Deep Intermediate Representations

arXiv:2605.23033v1 Announce Type: cross Abstract: Foundational Models pretrained on huge amount of data learn representations that evolve across depth, forming a hierarchy of embeddings with distinct

tutorialsarxiv-cs-ai

Local Ai

ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling

arXiv:2601.21198v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) architectures substantially bolster the expressive power of large-language models, their prohibitive memory foo

local-aiarxiv-cs-ai

DeepSeek says it will lower V4 Pro API prices by 75% to 0.435/1M input and 0.87/1M output tokens, making permanent the discount prices set to expire on May 31 (Bloomberg)

Bloomberg: DeepSeek says it will lower V4 Pro API prices by 75% to 0.435/1M input and 0.87/1M output tokens, making permanent the discount prices set to expire on May 31 — DeepSeek said it will make p

model-releasestechmeme

24 May 2026

Sooo many people dont know this but Hermes creator @NousResearch has its own sub program... the FREE account is HUGE because it gives you ac…

Sooo many people dont know this but Hermes creator @NousResearch has its own sub program... the FREE account is HUGE because it gives you access to all of their FREE models there are paid subs and if

researchnous-research--x

24 May 2026

Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks

arXiv:2605.21534v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) approximate multivariate functions using learnable univariate edge functions, typically parameterized by B-spline ba

Automatic Contextual Audio Denoising

arXiv:2605.22262v1 Announce Type: cross Abstract: Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, t

Tutorials

Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs

arXiv:2605.22611v1 Announce Type: new Abstract: Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiot

tutorialsarxiv-cs-lg

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

arXiv:2605.22593v1 Announce Type: new Abstract: While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-struc

Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs

arXiv:2605.21994v1 Announce Type: new Abstract: GraphRAG conditions language models on subgraphs retrieved from knowledge graphs, encoded via message-passing GNNs. Because these encoders entangle node

Safety

From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching

arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over ti

safetyarxiv-cs-lg

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains chall

IKNO: Infinite-order Kernel Neural Operators

arXiv:2605.22182v1 Announce Type: new Abstract: Neural operators have achieved significant success in modern scientific computing due to their flexibility and strong generalization capabilities. Exist

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approcimations

arXiv:2605.22557v1 Announce Type: new Abstract: We introduce an abstract neural flow framework for neural networks and neural operators. The framework contains two continuous-depth models, namely neur

On Statistical Estimation of Edge-Reinforced Random Walks

arXiv:2503.06115v2 Announce Type: replace-cross Abstract: Reinforced random walks (RRWs), including vertex-reinforced random walks (VRRWs) and edge-reinforced random walks (ERRWs), model random walks

One LR Doesn't Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs

arXiv:2605.22297v1 Announce Type: new Abstract: Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all laye

Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery

arXiv:2605.21522v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) govern nearly all cellular processes, yet computational methods for identifying binding partners typically produce

Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective

arXiv:2605.21692v1 Announce Type: new Abstract: Characterizing precisely the asymptotic generalization error of neural networks using parameters that can be estimated efficiently is a crucial problem

Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability

arXiv:2605.22142v1 Announce Type: new Abstract: Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly mode

The case against me below is completely intellectually dishonest, filled with lies and misrepresentations, wrong about almost literally ever…

model-releasesgary-marcus--x

The case against me below is completely intellectually dishonest, filled with lies and misrepresentations, wrong about almost literally everything it says—a textbook example of propaganda: - I didn’t

The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning

arXiv:2605.22498v1 Announce Type: new Abstract: Scientific machine learning often requires combining known physics with unknown parameters or correction terms learned from data. Existing approaches ei

this has been my experience as well. there definitely were improvements, specifically wrt shell based computer use, but also regressions, es…

model-releasesjeremy-howard--x

this has been my experience as well. there definitely were improvements, specifically wrt shell based computer use, but also regressions, especially in the last 3 version bumps of flicker and gerperte

Towards Explainability of SLMs by investigating Token Level Activation

arXiv:2605.22377v1 Announce Type: new Abstract: Transformer-based language models such as BERT having 110M+ parameters have revolutionized natural language understanding, yet their internal mechanisms

When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning

arXiv:2605.21606v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a student on its own rollouts using a privileged teacher, but its standard objective weights all generated tok

3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes

arXiv:2605.22328v1 Announce Type: new Abstract: Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR

Safety

A KL-regularization Framework for Learning to Plan with Adaptive Priors

arXiv:2510.04280v2 Announce Type: replace-cross Abstract: Effective exploration remains a central challenge in model-based reinforcement learning (MBRL), particularly in high-dimensional continuous co

safetyarxiv-cs-ro

A Task-Agnostic Algebraic Integrity Metric for Event-Camera Streams Toward SOTIF-Compliant Perception using Pearson Correlation Coefficient

arXiv:2605.21500v1 Announce Type: cross Abstract: Event cameras have emerged as a high-bandwidth, low-latency sensing modality for safety-critical perception in automated driving systems (ADS), offeri

Anthropic says Claude Mythos Preview has been used to find more than 10,000 high- or critical-severity vulnerabilities since the launch of Project Glasswing (Anthropic)

Anthropic: Anthropic says Claude Mythos Preview has been used to find more than 10,000 high- or critical-severity vulnerabilities since the launch of Project Glasswing — Last month, we launched Projec

model-releasestechmeme