All entries

A Comparative Study of UMAP and Other Dimensionality Reduction Methods

arXiv:2603.02275v2 Announce Type: replace Abstract: Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies U

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones

DGX agent

arXiv:2508.11696v3 Announce Type: replace Abstract: A deep learning real-time smoking detection system for CCTV surveillance of fire exit areas is proposed due to critical safety requirements. The dat

safetyarxiv-cs-cv

4 May 2026

A Dirac-Frenkel-Onsager principle: Instantaneous residual minimization with gauge momentum for nonlinear parametrizations of PDE solutions

model-releasesarxiv-cs-lg

arXiv:2605.00284v1 Announce Type: new Abstract: Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the param

A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers

arXiv:2605.00307v1 Announce Type: cross Abstract: Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrat

local-aiarxiv-cs-cv

A Novel Patch-Based TDA Approach for Computed Tomography Imaging

arXiv:2512.12108v5 Announce Type: replace Abstract: The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds fo

researcharxiv-cs-cv

A Policy-Driven DRL Framework for System-Level Tradeoff Control in NR-U/Wi-Fi Coexistence

arXiv:2605.00457v1 Announce Type: cross Abstract: The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a system-level resource coordination problem, where heterogeneous channel access m

A Unified Deep Learning Framework for Motion Correction in Medical Imaging

arXiv:2409.14204v4 Announce Type: replace-cross Abstract: Deep learning has shown significant value in medical image registration for motion correction, however, current techniques are either limited

local-aiarxiv-cs-cv

A unified perspective on fine-tuning and sampling with diffusion and flow models

arXiv:2605.00229v1 Announce Type: cross Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base

A11y-Compressor: A Framework for Enhancing the Efficiency of GUI Agent Observations through Visual Context Reconstruction and Redundancy Reduction

arXiv:2605.00551v1 Announce Type: new Abstract: AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tr

Hardware

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments

arXiv:2605.00650v1 Announce Type: new Abstract: Fine-tuning LLMs is necessary for various dedicated downstream tasks, but classic backpropagation-based fine-tuning methods require substantial GPU memo

hardwarearxiv-cs-lg

Tutorials

Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement

arXiv:2507.18064v2 Announce Type: replace Abstract: Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic

tutorialsarxiv-cs-cv

Adaptive Dual-Teacher Distillation with Subnetwork Rectification for Bridging Semantic Gaps in Black-Box Domain Adaptation

model-releasesarxiv-cs-cv

arXiv:2603.22908v3 Announce Type: replace Abstract: Assuming that neither source data nor source model parameters are accessible, black-box domain adaptation (BBDA) represents a highly practical yet c

Adaptive Equilibrium: Dynamic Weighting Framework for Generalized Interruption of DeepFake Models

arXiv:2605.00443v1 Announce Type: cross Abstract: The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation o

safetyarxiv-cs-cv

Adaptive Geodesic Conformal Prediction for Egocentric Camera Pose Estimation

arXiv:2605.00233v1 Announce Type: new Abstract: Egocentric pose estimation for Augmented Reality (AR) and assistive devices requires not just accurate predictions but guaranteed uncertainty regions. C

researcharxiv-cs-cv

Adaptive Node Feature Selection For Graph Neural Networks

arXiv:2510.03096v2 Announce Type: replace Abstract: We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during trai

Adaptive Norm-Based Regularization for Neural Networks

arXiv:2605.00171v1 Announce Type: cross Abstract: In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regulariz

Adaptive Querying with AI Persona Priors

arXiv:2605.00696v1 Announce Type: cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within

researcharxiv-cs-cl

Tutorials

Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay

arXiv:2605.00374v1 Announce Type: new Abstract: Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overloo

tutorialsarxiv-cs-lg

ADVICE: Answer-Dependent Verbalized Confidence Estimation

arXiv:2510.10913v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has enabled them to communicate their confidence in natural language, improving transparency and rel

researcharxiv-cs-cl

Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts

arXiv:2605.00604v1 Announce Type: new Abstract: Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment

Agents

Affordance Agent Harness: Verification-Gated Skill Orchestration

arXiv:2605.00663v1 Announce Type: cross Abstract: Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occlu

agentsarxiv-cs-cv

Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines

arXiv:2605.00410v1 Announce Type: new Abstract: A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings,

safetyarxiv-cs-cl

Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?

model-releasesarxiv-cs-lg

arXiv:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs

AgentFloor: How Far Up the tool use Ladder Can Small Open-Weight Models Go?

arXiv:2605.00334v1 Announce Type: cross Abstract: Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical

Agents

Agentic AI for Substance Use Education: Integrating Regulatory and Scientific Knowledge Sources

arXiv:2605.00383v1 Announce Type: new Abstract: The delivery of traditional substance education has remained problematic due to challenges in scalability, personalization, and the currency of informat

agentsarxiv-cs-cl

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs

arXiv:2605.00539v1 Announce Type: new Abstract: Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective f

AIDA-ReID: Adaptive Intermediate Domain Adaptation for Generalizable and Source-Free Person Re-Identification

arXiv:2605.00111v1 Announce Type: new Abstract: Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain s

researcharxiv-cs-cv

AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G

arXiv:2605.00020v1 Announce Type: new Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer desig

local-aiarxiv-cs-lg

Aitchison Embeddings for Learning Compositional Graph Representations

arXiv:2605.00716v1 Announce Type: new Abstract: Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embedd

Alethia: A Foundational Encoder for Voice Deepfakes

arXiv:2605.00251v1 Announce Type: cross Abstract: Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, dow

AlphaInventory: Evolving White-Box Inventory Policies via Large Language Models with Deployment Guarantees

model-releasesarxiv-cs-lg

arXiv:2605.00369v1 Announce Type: new Abstract: We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent adva

Agents

An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving

arXiv:2605.00291v1 Announce Type: new Abstract: The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the

agentsarxiv-cs-cv

Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework

arXiv:2605.00270v1 Announce Type: new Abstract: Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to

applicationsarxiv-cs-cl

Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning

arXiv:2605.00667v1 Announce Type: new Abstract: Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominen

Autoformalizing Memory Specifications with Agents

arXiv:2605.00058v1 Announce Type: cross Abstract: The primary goal of Design Verification (DV) is to ensure that a proposed chip design implementation (either in code, or physical form) exactly matche

applicationsarxiv-cs-lg

BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Bangladeshi Social Interaction

arXiv:2603.15949v3 Announce Type: replace Abstract: Large Language Models have demonstrated strong multilingual fluency, yet fluency alone does not guarantee socially appropriate language use. In high

Batch Normalization for Neural Networks on Complex Domains

arXiv:2605.00467v1 Announce Type: new Abstract: Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of pri

Bayesian Optimization in Linear Time

arXiv:2605.00237v1 Announce Type: new Abstract: Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be ma

local-aiarxiv-cs-lg

Being-H0.7: A Latent World-Action Model from Egocentric Videos

arXiv:2605.00078v1 Announce Type: cross Abstract: Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to a

applicationsarxiv-cs-cv

Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

arXiv:2605.00674v1 Announce Type: new Abstract: Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating

Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots

arXiv:2605.00545v1 Announce Type: new Abstract: Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as

applicationsarxiv-cs-lg

Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe

arXiv:2605.00607v1 Announce Type: new Abstract: Probing is widely used to study which features can be decoded from language model representations. However, the common decoding probe approach has two l

researcharxiv-cs-cl

Beyond Heuristics: Learnable Density Control for 3D Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2605.00408v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has demonstrated impressive real-time rendering performance, its efficacy remains constrained by a reliance on heuris

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

arXiv:2508.06361v4 Announce Type: replace Abstract: Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A signifi

Beyond Suffixes: Token Position in GCG Adversarial Attacks on Large Language Models

arXiv:2602.03265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have seen widespread adoption across multiple domains, creating an urgent need for robust safety alignment mechanisms.

Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

model-releasesarxiv-cs-cv

arXiv:2605.00310v1 Announce Type: new Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution

Bias in Large Language Models: Origin, Evaluation, and Mitigation

arXiv:2411.10915v2 Announce Type: replace Abstract: Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This

safetyarxiv-cs-cl

Binomial flows: Denoising and flow matching for discrete ordinal data

arXiv:2605.00360v1 Announce Type: new Abstract: Flow-based generative modeling in continuous spaces exploit Tweedie's formula to express the denoiser (learned in training) as a score function (used in

applicationsarxiv-cs-lg