large language models

A Transformation-Based AI Framework for Equitable RTI Tier Classification in Qatar

Consistent and equitable classification of students within Response to Intervention (RTI) frameworks remains a significant challenge in Qatar’s educational system, where tier-assignment decisions rely predominantly on qualitative, multidisciplinary evaluation reports rather than standardized quantitative measures. This...

Retraction notice to “Reshaping MOFs synthesis conditions mining with a dynamic multi-agents framework of large language model” [Trans. Mater. Res. 2 (2026) 100176]

Integrating LLM in Business Process Management: A Conceptual Framework for Augmenting the Process Lifecycle

A lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle, which positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential...

Effects of Shot Size on Social Impressions in Humans and Multimodal AI

The same person can be judged differently depending on how tightly they are framed. As image- and video-based evaluations become increasingly common in settings such as hiring, screening, and interviews, this raises a practical question: does shot size change social-impression ratings in human observers and a multimoda...

Narrating Authority Through Storytelling in Entrepreneurial Podcast Interviews

How do entrepreneurs claim the authority to lead an enterprise that was never theirs alone? Although the discursive construction of leadership has attracted growing interest, most existing work is qualitative and case-limited, and leadership authority has rarely been examined as a discursive phenomenon at scale. Drawin...

Explainable Hallucination Detection in Large Language Models Using Evidence-Grounded Verification

This paper presents Evidence-Grounded Verification (EGV), a modular framework for explainable hallucination detection in large language models. EGV decomposes model outputs into atomic claims, retrieves independent evidence, performs fine-grained four-way verification (Supported, Partially Supported, Contradicted, Unve...

Narrating Authority Through Storytelling in Entrepreneurial Podcast Interviews

How do entrepreneurs claim the authority to lead an enterprise that was never theirs alone? Although the discursive construction of leadership has attracted growing interest, most existing work is qualitative and case-limited, and leadership authority has rarely been examined as a discursive phenomenon at scale. Drawin...

Explainable Hallucination Detection in Large Language Models Using Evidence-Grounded Verification

This paper presents Evidence-Grounded Verification (EGV), a modular framework for explainable hallucination detection in large language models. EGV decomposes model outputs into atomic claims, retrieves independent evidence, performs fine-grained four-way verification (Supported, Partially Supported, Contradicted, Unve...

Decoding protein mutational fitness through evolutionary sequence intelligence: a large-scale ESM-2 benchmark across 217 DMS assays

Development and benchmark validation of PubChat for PubMed-grounded multilingual biomedical literature retrieval

The rapid growth of biomedical literature has rendered traditional systematic reviews unsustainable. Although large language models (LLMs) offer automation potential, citation fabrication and unreliable evidence discrimination remain critical barriers. Here, using PubChat as a PubMed E-utilities-grounded retrieval fram...

Iterated Agent for Symbolic Regression

This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search that achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines.

Fixing, Breaking, or Faking It? An Execution-Calibrated Evaluation of LLM Vulnerability Patching in JavaScript, Python, Go, and Java, and the Limits of LLM-as-Judge

Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness, leaving no single judge trustworthy.