large language models

Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study

This study traces the full decision-making pipelines of the 5 APR agents across 500 real-world repair tasks, from issue description to patch validation, and reveals that, while agents excel at simple fixes, they struggle with logic-intensive bugs.

From Blind Edits to Verified Repair: Building Trustworthy User-Side LLM Agents for Web Accessibility

Three building blocks toward a complete, privacy-preserving browser agent, a verified repair instrument pairing a trilingual seeded-violation benchmark with an audit-inject-verify loop, and a dual-condition protocol that measures harm as carefully as benefit are contributed.

MonitrLLM: A Community-Centered Evaluation Infrastructure for Large Language Models

MonitrLLM is introduced, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata.

Automated Vulnerability Patching in Legacy Code Using LLMs and Multi-AI Agents

While multi-agent LLM pipelines show real promise for legacy code specifically, since their capacity for autonomous context reconstruction and iterative, feedback-driven refinement directly addresses the sparse documentation and thin test coverage that define legacy environments, the literature to date has been validat...

Physics filtering favors the generalization of robot learning

It is shown that robots can generalize effectively under dynamics uncertainties even with limited training data by leveraging a feedback mechanism, namely PhyFilter, that corrects learning outputs with physics-filtered learning residuals, and shows that physics-filtered feedback can serve as a powerful alternative to m...

Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training

While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables. However, existing multimodal pre-training methods underutilize this potential due to semantic-agnostic designs that treat tabular inputs as...

AI-Powered Anomaly Detection in Cloud-Based Applications

The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.

Act2Intention: A Benchmark For Developing Active Mobile Agents Through Inferring User Intention from GUI Actions

The Act2Intention framework is proposed, which builds an active mobile agent by integrating understanding, predicting user intentions, and executing decisions, and establishes a standardized platform for developing and evaluating proactive agents and consequently paves the way for research on intention-driven human-com...

Can large language models accurately compute descriptive statistics from structured datasets? A comparative evaluation of ChatGPT and Claude

Background Large language models (LLMs) are increasingly used to support statistical analyses in biomedical research. However, their ability to accurately and reproducibly compute descriptive statistics directly from datasets has received limited evaluation. Objective To compare the accuracy, within-modality repeatabil...

Large language model-based swarm intelligence for parameter identification of photovoltaic cells

A Knowledge Graph–Augmented Large Language Model for Automated Quality Compliance Checking in Construction

This research proposes a large language model–based approach combined with structured algorithms that process domain-specific competency questions and unstructured construction documents to build ontologies and extract structured knowledge graphs to support the automated generation of quality inspection checklists and...

A Decision Support Tool for Determining the Suitable Multifunctional Digital Twin Maturity Levels in Construction Projects

The research concludes that the DST provides stakeholders with high-level insights regarding the suitable DT maturity levels—essential for stakeholder buy-in—assists them in making data-driven decisions especially during the front-end planning stage, and helps determine realistic technology objectives that contribute t...