Search: “models”

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

arXiv:2606.10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervas

agentsarxiv-cs-ai

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Multi-UAV Active Sensing with Information Gain-based Planning and Belief Fusion

DGX agent

arXiv:2606.10986v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments. Their performa

applicationsarxiv-cs-ro

10 Jun 2026

Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming

arXiv:2606.10675v1 Announce Type: new Abstract: We present a method for accurate multilingual word-level forced alignment, consisting of an alignment encoder and a learned alignment decoder. The encod

safetyarxiv-cs-cl

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504.03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles. How

ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction

arXiv:2601.06997v2 Announce Type: replace-cross Abstract: Autonomous high-fidelity object reconstruction is fundamental for creating digital assets and bridging the simulation-to-reality gap in roboti

agentsarxiv-cs-cv

OMG: Omni-Modal Motion Generation for Generalist Humanoid Control

arXiv:2606.10340v1 Announce Type: new Abstract: Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy rewar

researcharxiv-cs-ro

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation

arXiv:2606.10126v1 Announce Type: cross Abstract: Personalized persuasive text generation can improve relevance and engagement, but demographic conditioning may also introduce unequal framing across g

Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization

arXiv:2606.10902v1 Announce Type: cross Abstract: Subject Customization is a foundational task in modern image generation. By providing a few reference images and a text prompt, users can generate ima

applicationsarxiv-cs-ai

QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

arXiv:2606.09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resour

Quantifying Perception-Based Student Success with Generative AI: An Exploratory Monte Carlo Simulation

arXiv:2507.01062v4 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) tools such as ChatGPT have attracted growing attention in higher education, particularly in relatio

applicationsarxiv-cs-ai

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

arXiv:2606.10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with t

Local Ai

Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features

arXiv:2606.10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 6

local-aiarxiv-cs-lg

This is awesome! I am spending a lot of time on diffusion LLMs these days, so this is perfect timing. I feel like there are so many underexp…

This is awesome! I am spending a lot of time on diffusion LLMs these days, so this is perfect timing. I feel like there are so many underexplored research questions around text diffusion. Weight avail

researchdair-ai--x

Local Ai

Time-frequency localization of bird calls in dense soundscapes

arXiv:2606.10407v1 Announce Type: cross Abstract: Passive acoustic monitoring enables large-scale observation of wildlife, but most bioacoustic classifiers only predict species presence in a time wind

local-aiarxiv-cs-cv

Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

arXiv:2603.28054v2 Announce Type: replace Abstract: In this paper, we introduce GhostWriteBench, a dataset for LLM authorship attribution. It comprises long-form texts (50K+ words per book) generated

researcharxiv-cs-cl

Why are AI research restrictions treated differently from every other safeguard? @theemozilla and @karan4d, co-founders of Nous Research: 'O…

Why are AI research restrictions treated differently from every other safeguard? @theemozilla and @karan4d, co-founders of Nous Research: 'On the bio stuff... just saying no to the user and being hone

safetynous-research--x

A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

arXiv:2601.02424v2 Announce Type: replace-cross Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and ph

A Mixed Diet Makes DINO An Omnivorous Vision Encoder

arXiv:2602.24181v2 Announce Type: replace-cross Abstract: Pre-trained vision encoders like DINOv2 have demonstrated exceptional performance on unimodal tasks. However, we observe that their features a

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

arXiv:2508.00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ens

agentsarxiv-cs-cv

A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis

arXiv:2606.08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept

Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation

arXiv:2606.08480v1 Announce Type: cross Abstract: Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

arXiv:2412.06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) h

An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats

arXiv:2606.09686v1 Announce Type: cross Abstract: Numeric format proliferation in machine learning hardware -- FP8 (E4M3 and E5M2), BF16, MXFP4, microscaling block formats, and dozens of research vari

An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification

arXiv:2606.09123v1 Announce Type: cross Abstract: Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classificatio

Assessing Sample Quality in Conditional Generation under Compositional Shift

arXiv:2606.09601v1 Announce Type: new Abstract: Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observ

Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning

arXiv:2606.07551v1 Announce Type: cross Abstract: The use of social robots in home environments is on the rise. This exploratory study applies regularization techniques (e.g., Lasso and Ridge regressi

researcharxiv-cs-ro

Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels

arXiv:2606.07713v1 Announce Type: cross Abstract: The attention mechanism is the dominant computational bottleneck in modern transformer-based AI. Its standard implementation incurs quadratic memory t

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution

arXiv:2606.08800v1 Announce Type: new Abstract: In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practiti

Can LLMs extract scientific consensus? A case study in high-temperature superconductivity

arXiv:2606.07570v1 Announce Type: cross Abstract: Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolvin

applicationsarxiv-cs-lg

CLONE: A 3DGS-Based Closed-Loop Differentiable Optimization Framework for Single-Image Normal Estimation

arXiv:2508.05950v2 Announce Type: replace-cross Abstract: We propose CLONE, a 3DGS-based Closed-Loop differentiable Optimization framework for single-image Normal Estimation. The core idea is to const

Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes, Ablation Disposes

arXiv:2606.09607v1 Announce Type: cross Abstract: Interpretability increasingly treats groups of components, not individual units, as the basic object, and proposes to find them by clustering co-activ

Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning

arXiv:2606.08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. High

Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework

arXiv:2606.07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stim

Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring

arXiv:2606.08249v1 Announce Type: cross Abstract: Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observati

agentsarxiv-cs-lg

DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features

arXiv:2606.07756v1 Announce Type: new Abstract: Accurate distance estimation for small drones in long-range imagery is important for tracking and situational awareness, yet remains challenging due to

applicationsarxiv-cs-cv

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

arXiv:2406.07318v3 Announce Type: replace Abstract: The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems

researcharxiv-cs-cv

Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems

arXiv:2606.07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite r

Local Ai

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture

arXiv:2602.05175v2 Announce Type: replace Abstract: Deep neural networks demonstrate impressive performance in visual recognition but remain highly vulnerable to imperceptible adversarial attacks. Exi

local-aiarxiv-cs-cv

Tutorials

Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning

arXiv:2606.08013v1 Announce Type: new Abstract: Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Lear

tutorialsarxiv-cs-lg

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

arXiv:2606.08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectra

Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training

arXiv:2606.08898v1 Announce Type: cross Abstract: In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibilit

Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction

arXiv:2606.05556v2 Announce Type: replace Abstract: This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting

Geometry-Driven Flow Analysis of Brain Sulcal Pattern

arXiv:2606.08404v1 Announce Type: new Abstract: Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However

researcharxiv-cs-cv

Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks

arXiv:2606.08978v1 Announce Type: new Abstract: Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs

applicationsarxiv-cs-lg

Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection

arXiv:2606.08403v1 Announce Type: cross Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-

Hierarchical Projection for Adaptive Knowledge Transfer

arXiv:2606.08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related informa

safetyarxiv-cs-lg

Hyperflux: Pruning Reveals Importance

arXiv:2504.05349v4 Announce Type: replace-cross Abstract: Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most methods focus on empirical r

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation

arXiv:2509.10334v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained device

applicationsarxiv-cs-ai