Search: “concepts”

Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning

arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo

researcharxiv-cs-ai

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LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation

DGX agent

arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi

model-releasesarxiv-cs-ai

26 Jun 2026

Low Resource Multimodal Translation of Nepali Spoken Words into Emotion-Conditioned Sign Language Avatars

model-releasesarxiv-cs-ai

arXiv:2606.26107v1 Announce Type: cross Abstract: Sign language communication systems, that integrate emotional expression remain underexplored, particularly for low-resource languages. This pilot stu

Radical AI Interpretability

arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanis

safetyarxiv-cs-ai

Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN

arXiv:2606.27305v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise ex

Term-Centric Hierarchy Induction from Heterogeneous Corpora

model-releasesarxiv-cs-cl

arXiv:2606.26963v1 Announce Type: new Abstract: Organizing knowledge from diverse text sources into interpretable hierarchies is crucial for tasks such as policy analysis, innovation monitoring, and e

Theory-Scale Auto-Formalization of Logics for Computer Science

arXiv:2606.26525v1 Announce Type: new Abstract: Auto-formalization is critical for scalable formal verification, but existing progress largely focuses on isolated statements, while theory-scale auto-f

AMVICC: A Novel Benchmark for Cross-Modal Failure Mode Profiling for VLMs and IGMs

arXiv:2601.17037v2 Announce Type: replace Abstract: We investigate visual reasoning limitations of both multimodal large language models (MLLMs) and image generation models (IGMs) by creating a novel

Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

arXiv:2606.25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying pro

ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction

arXiv:2603.14915v2 Announce Type: replace Abstract: A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure,

Safe Learning Control with Optimality and Stability Guarantees

arXiv:2501.15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to

Steering Vision-Language Models with Joint Sparse Autoencoders

arXiv:2606.25657v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) have shown promise for analyzing language models, but applying them to vision-language models (VLMs) often yields representat

Agents

TheoremGraph: Bridging Formal and Informal Mathematics

arXiv:2606.25363v1 Announce Type: cross Abstract: Mathematical knowledge is organized around statements and their dependencies, but this structure is exposed unevenly: informal papers cite mostly at t

agentsarxiv-cs-ai

Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis

arXiv:2606.23292v2 Announce Type: replace-cross Abstract: Bathroom use is a critical safety challenge for older adults because wet surfaces, constrained layouts, limited support, and frequent posture

safetyarxiv-cs-ai

Two-dimensional Hyperbolic RNN Neural Quantum State

arXiv:2606.25600v1 Announce Type: cross Abstract: In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2

Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety

arXiv:2606.25034v1 Announce Type: new Abstract: General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversaria

safetyarxiv-cs-cv

Local Ai

ActiveScope: Actively Seeking and Correcting Perception for MLLMs

arXiv:2606.24292v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in

local-aiarxiv-cs-cv

Agents

Explaining Failures of Cyber-Physical Systems with Actual Causality

arXiv:2606.24546v1 Announce Type: new Abstract: Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, face increasingly complex demands, and yet are expected to act reliably. The

agentsarxiv-cs-ro

KANLib -- A Modular, Extensible and Fast Kolmogorov-Arnold Network Implementation

model-releasesarxiv-cs-ai

arXiv:2606.17927v2 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear w

LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation

arXiv:2509.17773v2 Announce Type: replace Abstract: The rapid progress of image-guided video generation (I2V) has raised concerns about its potential misuse in misinformation and fraud, underscoring t

Macro Graph of Experts for Billion-Scale Multi-Task Recommendation

arXiv:2506.10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale gra

AI Agents Can Already Autonomously Perform Experimental High Energy Physics

arXiv:2603.20179v3 Announce Type: replace-cross Abstract: Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipelin

An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving

arXiv:2606.20640v1 Announce Type: cross Abstract: Autonomous vehicles offer the potential for safer and more efficient mobility, yet public trust remains limited due to the lack of transparency in the

Agents

Autonomous and Self-Adapting System for Synthetic Media Detection and Attribution

arXiv:2504.03615v2 Announce Type: replace Abstract: Rapid advances in generative AI have enabled the creation of highly realistic synthetic images, which, while beneficial in many domains, also pose s

agentsarxiv-cs-cv

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models

arXiv:2606.21645v1 Announce Type: cross Abstract: Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains under

Brain-Adapter: A Dual-Stream Vision-Language MIL Framework for Comprehensive 3D CT Diagnosis of Acute Intracranial Pathologies

arXiv:2606.23494v1 Announce Type: new Abstract: Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and

Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition

arXiv:2412.06190v2 Announce Type: replace Abstract: Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a w

Decoupling the Declarative from the Procedural in Vision-Language-Action Models

arXiv:2606.21496v1 Announce Type: cross Abstract: Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-spe

DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay

arXiv:2606.22950v1 Announce Type: new Abstract: Online learning is crucial for handling complex data streams in big data applications. Recent research has begun to focus on dynamic scenarios, i.e., no

EgoExo-Con: Exploring View-Invariant Video Temporal Understanding

arXiv:2510.26113v2 Announce Type: replace Abstract: Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introd

Tutorials

FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments

arXiv:2606.22838v1 Announce Type: new Abstract: In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-sca

tutorialsarxiv-cs-ro

Applications

Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs

arXiv:2606.21440v1 Announce Type: new Abstract: Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This o

applicationsarxiv-cs-lg

Gradient-Descent Steps to Success over Mean Accuracy: A Paradigm Shift for ML

arXiv:2606.22053v1 Announce Type: new Abstract: Traditional evaluation of machine learning (ML) models typically focuses on achieving the maximum possible accuracy irrespective of the computational co

GTA-Net: Cooperative Game Theory for Vision-Language Alignment in Chest X-Ray Report Generation

arXiv:2606.21915v1 Announce Type: new Abstract: Automated chest X-ray report generation requires precise cross-modal grounding to ensure clinically reliable descriptions. However, existing vision-lang

safetyarxiv-cs-cv

HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning

arXiv:2509.22645v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to endow models with the ability to continuously adapt to evolving data streams. Recent advances in pre-traine

Applications

Integrated cloud-based architecture for robot-robot and human-robot collaboration using ROS 2--MQTT in Mediterranean Greenhouses

arXiv:2606.22682v1 Announce Type: new Abstract: The imperative to develop more sustainable agriculture demands a transition from isolated automation toward the deployment of multi-robot systems (MRS)

applicationsarxiv-cs-ro

Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations

arXiv:2512.09021v3 Announce Type: replace-cross Abstract: Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the interga

Manipulider: A Multi-Engine Buoyancy-Controlled Robot for Thrusterless Underwater Gliding and Manipulation

arXiv:2606.21461v1 Announce Type: new Abstract: The Manipulider is a buoyancy-actuated underwater robot that enables thrusterless, glide-like locomotion and attitude-based manipulation, while providin

researcharxiv-cs-ro

MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models

arXiv:2606.21194v1 Announce Type: new Abstract: Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains

No Reference-Free Generalization in Quantum Machine Learning

arXiv:2606.22331v1 Announce Type: cross Abstract: Quantum machine learning is often motivated by the exponentially large state space of quantum systems, but this promise leaves a basic generalization

PenduMorph: Development and Motion Analysis of Pendulum-Actuated Rolling Reconfigurable Spherical Robot with Magnetic-Coupling

arXiv:2606.22491v1 Announce Type: new Abstract: This paper presents 'PenduMorph', a wireless reconfigurable rolling spherical robot designed as a modular platform for enclosed locomotion and inter-mod

researcharxiv-cs-ro

PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMs

arXiv:2606.20913v1 Announce Type: new Abstract: Medical vision-language models (VLMs) enable zero-shot clinical image classification, yet reliably detecting out-of-distribution (OOD) inputs at deploym

Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates

arXiv:2606.21889v1 Announce Type: new Abstract: We study fixed-precision ranking-and-selection in structured settings where the answer may be non-unique and where noisy estimates may temporarily admit

Applications

Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers

arXiv:2606.21289v1 Announce Type: new Abstract: Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistr

applicationsarxiv-cs-lg

SPiralRoll: A Novel Adjustable-Stiffness Underactuated 3-DoF Joint with Torsion Springs for Rolling Robots

arXiv:2606.22443v1 Announce Type: new Abstract: Compliant mechanisms are important in robotics because they can improve adaptability, safety, and energy efficiency while reducing hardware complexity.

safetyarxiv-cs-ro

Tutorials

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space

arXiv:2606.21705v1 Announce Type: new Abstract: Dataset distillation (DD) has proven to reduce training cost while preserving accuracy. While promising, the factors that make one distilled dataset mor

tutorialsarxiv-cs-cv

Local Ai

SuperCond-GNN: Scalable Graph Neural Network Surrogate for Superconducting Circuit Simulations

arXiv:2606.23548v1 Announce Type: cross Abstract: This paper presents SuperCond-GNN, a graph neural network-based surrogate model for predicting the voltage distribution in high-temperature supercondu

local-aiarxiv-cs-lg

Building Social World Models with Large Language Models