Search: “automated”

arXiv:2606.29928v1 Announce Type: cross Abstract: Multimodal Large Models have significantly advanced automated breast ultrasound diagnosis. However, most existing frameworks utilize opaque, end-to-en

applicationsarxiv-cs-ai

Lateral String Stability for Vehicle Platoons

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arXiv:2606.29677v1 Announce Type: new Abstract: Connected and automated vehicle (CAV) platooning promises gains in energy efficiency and traffic throughput and, most critically, in safety. These safet

safetyarxiv-cs-ro

30 Jun 2026

Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion

arXiv:2603.05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep gen

MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems

arXiv:2606.30602v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows. However, their inter-agent communication channels introduc

Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring

arXiv:2606.29447v1 Announce Type: new Abstract: Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datas

Primary ICD Category Prediction using LLM-based Probing

arXiv:2606.28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrat

Why Trust Your Agent? Empirical Security Gains from TRiSM-Guided Agentic Workflows in Healthcare

arXiv:2606.28666v1 Announce Type: cross Abstract: Agent-based AI has enabled the automation of tasks by exposing application tools and resources to large language models (LLMs). However, to improve sc

Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection

arXiv:2606.30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection. Recent work has shown that LLMs a

Tutorials

Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

arXiv:2606.27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, dron

tutorialsarxiv-cs-ai

29 Jun 2026

'Generate' the Future of Work through AI: Empirical Evidence from Online Labor Markets

arXiv:2308.05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functi

researcharxiv-cs-ai

29 Jun 2026

Local Ai

SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds

arXiv:2606.27491v1 Announce Type: new Abstract: Automated instance segmentation of forest LiDAR point clouds is increasingly critical as forest monitoring moves toward scalable, detailed, 3D measureme

local-aiarxiv-cs-cv

29 Jun 2026

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606.26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each cand

Agents

EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling

arXiv:2606.26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structur

agentsarxiv-cs-ai

Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

arXiv:2606.20390v2 Announce Type: replace Abstract: Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variabili

HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models

model-releasesarxiv-cs-cl

arXiv:2606.27187v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing ha

Tutorials

Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis

arXiv:2606.26169v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the

tutorialsarxiv-cs-ai

Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling

arXiv:2606.26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large

A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection

arXiv:2606.24944v1 Announce Type: cross Abstract: Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C

Agents

A Probabilistic Framework for LLM-Based Model Discovery

arXiv:2602.18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progres

agentsarxiv-cs-lg

Adaptive-Horizon Conflict-Based Search for Closed-Loop Multi-Agent Path Finding

arXiv:2602.12024v3 Announce Type: replace Abstract: Multi-Agent Path Finding (MAPF) is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are

safetyarxiv-cs-ro

Agentic evolution of physically constrained foundation models

arXiv:2606.25532v1 Announce Type: cross Abstract: Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hal

Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation

arXiv:2606.25254v1 Announce Type: cross Abstract: Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the

safetyarxiv-cs-cv

GUI agent: Guided Exploration of User-Sensitive Screens

arXiv:2606.25705v1 Announce Type: new Abstract: LLM agents are increasingly being used to automate tasks for users within an open GUI environment. They inevitably encounter screens containing user-sen

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

arXiv:2606.26002v1 Announce Type: new Abstract: We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural netwo

LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag

safetyarxiv-cs-cl

MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification

arXiv:2512.10187v3 Announce Type: replace Abstract: LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs

Local Ai

Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving

arXiv:2606.25224v1 Announce Type: new Abstract: Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their

local-aiarxiv-cs-ro

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

arXiv:2606.25989v1 Announce Type: new Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Exis

safetyarxiv-cs-cv

Applications

Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow

arXiv:2606.18876v2 Announce Type: replace Abstract: Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low-cost devices hinders automated an

applicationsarxiv-cs-cv

The Tatoxa System for Text Detoxification in Low-Resource Languages: The Case of Tatar

arXiv:2606.26015v1 Announce Type: new Abstract: Text detoxification, the automated detection and mitigation of abusive and harmful content, is essential for ensuring the safety of online communities a

safetyarxiv-cs-cl

A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization

arXiv:2606.23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehou

safetyarxiv-cs-lg

A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading

arXiv:2606.24168v1 Announce Type: cross Abstract: Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requ

AerialFusionMapNet: Online HD Map Construction with Aerial-Onboard BEV Fusion

arXiv:2606.24784v1 Announce Type: new Abstract: High-resolution aerial imagery has recently emerged as a complementary modality for automated driving perception and has shown potential to improve bird

AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming

arXiv:2606.24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, th

Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code

arXiv:2606.23690v1 Announce Type: cross Abstract: Autoregressive (AR) language models have driven significant progress in automated software engineering, enabling powerful code generation and assistan

researcharxiv-cs-ai

BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding

arXiv:2601.04524v2 Announce Type: replace Abstract: Understanding biomedical experiments provides a foundation for downstream tasks, e.g., laboratory automation, and facilitates effective cross-discip

researcharxiv-cs-ai

Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators

arXiv:2509.03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from 'self-preference bias': a tendency to favor thei

Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs

model-releasesarxiv-cs-cl

arXiv:2505.18542v4 Announce Type: replace Abstract: Extracting structured procedural knowledge from unstructured business documents is a critical yet unresolved bottleneck in process automation. While

Importance of Intent-Sharing for V2X-based Maneuver Coordination

arXiv:2606.24203v1 Announce Type: cross Abstract: This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Succe

researcharxiv-cs-ro

Light-weight Pronunciation Assessment via Discrete Speech Token Surprisal

arXiv:2606.19910v2 Announce Type: replace Abstract: Training automated pronunciation assessment often relies on labeled learner errors or non-native corpora that are costly to collect. We propose a li

safetyarxiv-cs-cl

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show

Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification

arXiv:2606.23744v1 Announce Type: cross Abstract: Deep learning has become an important tool in computational pathology, enabling automated analysis of histopathological images. While convolutional ne

VisCritic: Visual State Comparison as Process Reward for GUI Agents

arXiv:2606.24525v1 Announce Type: new Abstract: GUI agents powered by vision-language models show strong potential for automating digital tasks, yet frequently fail in long-horizon scenarios due to th

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

Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring

arXiv:2606.21613v1 Announce Type: new Abstract: Scaling wildlife monitoring for real-world conservation deployments requires automated analysis of smart sensors that operate under severe annotation sc

safetyarxiv-cs-cv

Deep Learning-Based Sign Language Recognition from Videos and Cross-Lingual Translation to Indian Vernaculars

arXiv:2606.22494v1 Announce Type: cross Abstract: Sign language is a primary mode of communication for the global deaf and hard-of-hearing community, yet automated tools that recognize sign gestures f

DPLAN: Minimal Connectivity to Floorplan Generation

arXiv:2606.21159v1 Announce Type: cross Abstract: Automated floor plan generation is an important problem in computational architectural design. The goal is to construct a floor plan from user-defined

researcharxiv-cs-ro

Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation

arXiv:2606.22918v1 Announce Type: new Abstract: Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that prov