Search: “automated”

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

model-releasesarxiv-cs-cl

arXiv:2510.18939v2 Announce Type: replace Abstract: Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling

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Research

LP Mining with LP2Graph: A Use Case for Railway Rescheduling

DGX agent

arXiv:2607.11980v1 Announce Type: new Abstract: Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field's modeling knowledge is sca

researcharxiv-cs-ai

15 Jul 2026

MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation

arXiv:2607.10079v2 Announce Type: replace Abstract: Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get

Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI

model-releasesarxiv-cs-cv

arXiv:2607.12874v1 Announce Type: new Abstract: Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone pr

NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

arXiv:2603.16307v2 Announce Type: replace Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and

Local Ai

PalmClaw: A Native On-Device Agent Framework for Mobile Phones

arXiv:2607.13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and it

local-aiarxiv-cs-ai

So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis

arXiv:2607.11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditiona

Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings

arXiv:2607.12678v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary geometric and s

The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning

arXiv:2607.12177v1 Announce Type: new Abstract: The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatial

Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

arXiv:2512.15748v2 Announce Type: replace-cross Abstract: Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology,

Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting

arXiv:2607.07858v1 Announce Type: new Abstract: Artificial intelligence (AI) is beginning to reshape actuarial practice, particularly in domains that require reasoning over unstructured documents, het

Applications

An Online Reference-Free Evaluation Framework for Flowchart Image-to-Code Generation

arXiv:2602.13376v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly used in document processing pipelines to convert flowchart images into structured code (e.g., M

applicationsarxiv-cs-ai

Hardware

ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions

arXiv:2607.08297v1 Announce Type: new Abstract: Background and Objective: Quantitative analysis of cell dynamics is central to modern biological research, providing critical insights into immune cell

hardwarearxiv-cs-cv

Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models

arXiv:2607.08317v1 Announce Type: new Abstract: Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipu

Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies

arXiv:2607.07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain

From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure

arXiv:2607.08288v1 Announce Type: cross Abstract: In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk as

Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination

arXiv:2607.08403v1 Announce Type: new Abstract: The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic pat

Applications

GradInf: Gradient Estimation as Probabilistic Inference

arXiv:2607.07840v1 Announce Type: cross Abstract: Gradient estimation -- the task of computing the gradient of the expected value of a probabilistic program -- has diverse applications in scientific c

applicationsarxiv-cs-lg

IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation

model-releasesarxiv-cs-cl

arXiv:2509.26076v2 Announce Type: replace Abstract: As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on researc

In vivo feasibility study of humanoid robots in surgery

arXiv:2607.07972v1 Announce Type: new Abstract: Recent advances in actuation, control and learning have rapidly pushed humanoid robots from a distant vision towards near-term real-world deployment. He

safetyarxiv-cs-ro

PLURAL: A Global Dataset for Value Alignment

arXiv:2607.08034v1 Announce Type: cross Abstract: Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value syst

Search-based Testing of Vision Language Models for In-Car Scene Understanding

arXiv:2607.02300v2 Announce Type: replace Abstract: In the automotive domain, in-car scene understanding (ISU) enables the detection of safety-critical events, such as driver distraction, and supports

safetyarxiv-cs-cv

Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

arXiv:2607.08511v1 Announce Type: cross Abstract: Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While class

TNODEV: Toolbox for Neural ODE Verification

arXiv:2606.16567v2 Announce Type: replace Abstract: Neural ordinary differential equations (neural ODE) gained attention in safety critical settings such as continuous-time controllers for cyber-physi

XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery

arXiv:2607.08332v1 Announce Type: new Abstract: Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discover

safetyarxiv-cs-cl

A Multi-Analyst LLM Pipeline for Auditable Rule Discovery Across 68 Public Physiological Corpora

arXiv:2607.06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints. They can

CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair

arXiv:2606.15667v2 Announce Type: replace Abstract: Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft s

Hardware

DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction

arXiv:2606.15346v2 Announce Type: replace Abstract: Spatio-temporal prediction supports radar/satellite nowcasting and city-scale traffic monitoring, but modern models are often too expensive for real

hardwarearxiv-cs-cv

Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

arXiv:2607.06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS)

From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective

arXiv:2607.06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Lar

From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings

model-releasesarxiv-cs-cl

arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating a cold start problem for item calibration

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

model-releasesarxiv-cs-cv

arXiv:2607.07673v1 Announce Type: new Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundat

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

model-releasesarxiv-cs-lg

arXiv:2603.17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while re

Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

arXiv:2607.06585v1 Announce Type: new Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in econ

Rethinking Multimodal Time-Series Forecasting Evaluation

model-releasesarxiv-cs-lg

arXiv:2607.06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world t

TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation

arXiv:2607.07169v1 Announce Type: new Abstract: The precise pixel-level localization of 2D material flakes is crucial for high-throughput screening. However, traditional fully supervised methods rely

safetyarxiv-cs-cv

Video-Based Detection of squint and cataract for accessibility-aware adaptive web interface rendering

arXiv:2607.07099v1 Announce Type: new Abstract: Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-b

A Three-Layer Framework for AI in Scientific Discovery

arXiv:2606.13566v2 Announce Type: replace Abstract: Current discussions of AI in scientific discovery are often dominated by two visible capabilities: search over existing knowledge and execution thro

Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique

arXiv:2602.13213v2 Announce Type: replace Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine p

Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation

arXiv:2607.05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure M

Applications

Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines

arXiv:2607.05680v1 Announce Type: cross Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when t

applicationsarxiv-cs-ai

Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning

arXiv:2607.05773v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making. We introduce A

Complementary Roles of Image Classification and Vessel Segmentation in AI-Based Screening for Retinopathy of Prematurity Plus Disease in a Kenyan Preterm Cohort

arXiv:2607.05825v1 Announce Type: cross Abstract: Background. Retinopathy of prematurity (ROP) is a preventable cause of childhood blindness, with rising burden in low- and middle-income countries whe

Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring

arXiv:2607.05625v1 Announce Type: new Abstract: Wound monitoring is a critical yet underserved clinical challenge, where timely identification of severe adverse events (SAEs) such as infection, tissue

safetyarxiv-cs-cv

Foundation Models for Automatic CAD Generation

arXiv:2607.05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-

From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

arXiv:2607.05563v1 Announce Type: new Abstract: Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects, enabling a deeper understanding of

Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning

arXiv:2607.06501v1 Announce Type: new Abstract: We consider an open-world planning setting in which service robots must operate in unknown environments with incomplete knowledge of objects and actions

agentsarxiv-cs-ro