Search: “applications”

When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics

arXiv:2607.28256v1 Announce Type: new Abstract: Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment

researcharxiv-cs-ro

31 Jul 2026

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Research

Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction

DGX agent

arXiv:2607.26562v1 Announce Type: cross Abstract: We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-

researcharxiv-cs-lg

30 Jul 2026

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

arXiv:2607.26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by

safetyarxiv-cs-cl

Tutorials

Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization

arXiv:2511.07210v3 Announce Type: replace Abstract: Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat t

tutorialsarxiv-cs-cv

Tutorials

Challenges and proposed solutions in modeling multimodal medical data: A systematic review

arXiv:2505.06945v5 Announce Type: replace Abstract: Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, ge

tutorialsarxiv-cs-lg

CinemaTraj: Composing Atomic Camera Trajectories for 3D Scenes with LLM Agents

arXiv:2607.26910v1 Announce Type: new Abstract: Automatically generating cinematically expressive camera trajectories through 3D scenes from natural language descriptions is a challenging task of high

safetyarxiv-cs-cv

Crossing-Free Probabilistic K-Line Forecasts Without Retraining

arXiv:2607.26792v1 Announce Type: cross Abstract: Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC). However, it introduces two

Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation

arXiv:2607.26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicab

Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach

arXiv:2607.27105v1 Announce Type: cross Abstract: Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learn

Domain adaptation for handwriting trajectory reconstruction from IMU sensors

arXiv:2607.26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses

DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization

arXiv:2601.04641v2 Announce Type: replace-cross Abstract: The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict

researcharxiv-cs-cl

Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces

arXiv:2505.16035v3 Announce Type: replace Abstract: We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers. Our

Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures

arXiv:2411.17136v2 Announce Type: replace-cross Abstract: Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. Wit

Local Ai

From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models

arXiv:2607.26921v1 Announce Type: new Abstract: YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc pr

local-aiarxiv-cs-cv

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

arXiv:2607.26571v1 Announce Type: new Abstract: The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprin

Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

model-releasesgoogle-deepmind

Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic app

Hardware

Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation

arXiv:2607.26646v1 Announce Type: new Abstract: We address the problem of reconstructing a high-fidelity, freely navigable 3D scene from a single 360^irc panorama, without per-scene optimization or mu

hardwarearxiv-cs-cv

InferScale: GPU-Native KV Injection for Personalized LLM Serving

arXiv:2607.27090v1 Announce Type: cross Abstract: Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histori

LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving

arXiv:2607.26491v1 Announce Type: cross Abstract: The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and t

Mechanistic interpretability streamlined for everyday users like us😎 🧠

model-releasesr-localllama

Context: I want to give the community an Open Research (well open under Apache 2.0 clause) - tool that allows everyday users like us to look deeper into the local models we use consistently. Mechanist

Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network

model-releasesarxiv-cs-cv

arXiv:2607.26733v1 Announce Type: new Abstract: Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruct

P.A.I. — Sleek Native Desktop AI Overlayer for Local Ollama Models 🤖⚡

Greetings Community! 👋 I hope everyone is doing well! I'm Tauhid — Senior EEE student from a Bangladeshi University Today I'd like to share an open-source project I’ve been developing called P.A.I. (P

model-releasesr-ollama

PIKS: Universal Physics-Informed Kernel Methods

arXiv:2607.27062v1 Announce Type: cross Abstract: Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physi

Progressive Multimodal Alignment for Continual Instruction Tuning

model-releasesarxiv-cs-cv

arXiv:2607.26947v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cro

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607.26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applica

Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data

arXiv:2607.26219v1 Announce Type: cross Abstract: Regression estimates from observational data can depend on specification under multicollinearity, while sequential sums of squares (SS) depend on term

Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret

arXiv:2607.26370v1 Announce Type: cross Abstract: We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior

Skillful forecasting of offshore winds from satellite scatterometer constellations

arXiv:2607.27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of off

Spline-Based Boundary Representations for Sparse View Reconstruction and Simulation Using Isogeometric Analysis

arXiv:2607.26234v1 Announce Type: new Abstract: Image-based reconstruction aims to recover three-dimensional geometry from images. Recent advances have enabled the recovery of visually detailed models

researcharxiv-cs-cv

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

model-releasesarxiv-cs-cl

arXiv:2503.10647v2 Announce Type: replace Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dim

Agents

Top-k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection

arXiv:2607.26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of k arms and observes their d-dimensional rewar

agentsarxiv-cs-lg

Walk through Paintings: Egocentric World Models from Internet Priors

arXiv:2601.15284v2 Announce Type: replace Abstract: What if a video generation model could not only imagine a plausible future, but the correct one -- accurately reflecting how the world changes with

researcharxiv-cs-cv

WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2607.26203v1 Announce Type: new Abstract: Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. Despite its imp

A Riemannian View on Active Subspaces

arXiv:2607.25163v1 Announce Type: cross Abstract: Active subspaces provide an explainable, eigenvalue-ordered principle for studying how scalar-valued quantities of interest change the most, on averag

AI Deployment and Cyber Governance Failures in Public-Sector Organizations: A Typological Analysis

arXiv:2607.25368v1 Announce Type: new Abstract: The intersection of artificial intelligence adoption, cybersecurity governance, and public sector institutional constraints has not been examined as a u

researcharxiv-cs-ai

Tools

AI Worming through Word

AI Worming through Word Neat new prompt injection variant by Håkon Måløy, who found a way to upgrade prompt injection attacks against Microsoft Word to full self-replicating worms: An attacker places

toolssimon-willison

An Information-Theoretic Approach to Identifying Formulaic Clusters in Textual Data

arXiv:2503.07303v3 Announce Type: replace Abstract: Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship, and cultural context. Formulai

researcharxiv-cs-cl

Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?

arXiv:2607.25529v1 Announce Type: new Abstract: As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability. Yet existing

researcharxiv-cs-ai

Agents

Automatic Knowledge Graph Construction and Query for Earthquake Catalogs

arXiv:2607.24984v1 Announce Type: cross Abstract: In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based

agentsarxiv-cs-ai

Behavior-Driven Explainability

arXiv:2607.24881v1 Announce Type: new Abstract: As system complexity has vastly increased, it has become significantly more challenging for a single person or a team to fully understand all aspects of

Contrastive Weak-to-strong Generalization

arXiv:2510.07884v3 Announce Type: replace-cross Abstract: Weak-to-strong generalization provides a promising paradigm for scaling large language models (LLMs) by training stronger models on samples fr

safetyarxiv-cs-ai

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization

arXiv:2607.25480v1 Announce Type: new Abstract: Constrained online convex optimization requires minimizing regret against adversarial convex costs while satisfying a convex constraint at every round,

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

arXiv:2607.25227v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly

Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?

model-releasesarxiv-cs-ai

arXiv:2607.26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current benchmarks primarily measure end-task success o

Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models

arXiv:2607.25078v1 Announce Type: new Abstract: Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical c

researcharxiv-cs-cv

Agents

Distributing Security Controls Through Harness Engineering

arXiv:2607.25890v1 Announce Type: new Abstract: AI coding agents are being adopted at historic speed, yet security and risk concerns remain the primary barrier to scaling agentic AI across organizatio

agentsarxiv-cs-ai

Tutorials

From Idea to Classroom in Days: Using 'Vibe Coding' to Create a Programming Process Visualizer from IDE Activity Logs

arXiv:2607.24757v1 Announce Type: cross Abstract: This paper reports on the rapid development and classroom deployment of a Thonny log visualizer built using AI-assisted ``vibe coding'' to make studen

tutorialsarxiv-cs-ai

Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

arXiv:2602.15021v2 Announce Type: replace-cross Abstract: Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to mode