Layout-Aware Document Parsing with Visual-Linguistic Fusion: The DATA-LUX with Academic Content Service Provider

Regulatory technologies (RegTechs) are a set of electronic and digital technologies applied to check compliance in industrial processes. Such applications also aim to simplify the process of data collection and exchange according to the expected format over the cloud or the internet. Industrial processes are required to meet basic regulatory requirements according to law and follow a set of industry practices. Industry practices must be compliant with the basic regulatory requirements. Such applications also need a high level of privacy to protect the individual participant’s data from competitors but are revealed to the relevant regulatory agencies. However, there cannot be a standard data procurement method, as the industrial processes are different for individual businesses and often involve various stages of data collection with different aims. Also, the regulatory requirements may be changed over time. These challenges can be addressed over an online system that uses large language models (LLM) to perform continuous compliance checks. With LLMs, RegTech can be easily scaled up to meet new requirements. It can also help with data analysis and reformatting for different stakeholders in RegTech, such as producers, supply chains, regulators, and financial institutions. It can check for acceptable values with regards to RegTech through either numeric comparisons or enumerations matching. In this paper, we propose a comprehensive RegTech framework backed by LLM and web services. We propose a method to measure the accuracy of LLM in returning appropriate responses for RegTech queries and herein analyze several LLMs to conclude that they are satisfactory for basic tasks, but a dedicated LLM is needed for RegTech. Furthermore, we test the LLM’s tool-calling capabilities to identify and use dedicated functions in the form of web services to enhance the analytical accuracy and consistency of RegTech-related prompts.

Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly-used approximation method in quantum physics: the Hartree-Fock method, requiring an analytic multi-step calculation deriving approximate Hamiltonian and corresponding self-consistency equations. To carry out the calculations using LLMs, we design multi-step prompt templates that break down the analytic calculation into standardized steps with placeholders for problem-specific information. We evaluate GPT-4’s performance in executing the calculation for 15 papers from the past decade, demonstrating that, with the correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases. Aggregating across all research papers, we find an average score of 87.5 (out of 100) on the execution of individual calculation steps. We further use LLMs to mitigate the two primary bottlenecks in this evaluation process: (i) extracting information from papers to fill in templates and (ii) automatic scoring of the calculation steps, demonstrating good results in both cases.

The swift evolution of Large Language Models (LLMs) like the GPT family, LLaMA, ChatGLM, and Qwen represents significant progress in artificial intelligence research. Despite their remarkable capabilities in generating content, these models encounter substantial challenges when producing structured outputs and engaging in dynamic interactions, particularly when they need to retrieve external information in real time. To address these limitations, researchers have developed the ”Function Calling” paradigm. This approach enables language models to analyze user inquiries and engage with defined functions, thereby facilitating precise responses through connections to external sources, including databases, programming interfaces, and live data streams. This functionality has been successfully implemented across numerous sectors such as finance analytics, healthcare systems, and service operations. The implementation of function calling comprises three essential phases: preparation, execution, and processing. The preparation phase encompasses query analysis and function identification. During execution, the system evaluates whether a function is necessary, extracts relevant parameters, and oversees the operation. The processing phase concentrates on analyzing outcomes and crafting appropriate responses. Each phase presents unique difficulties, ranging from accurately selecting functions to managing complex parameter extraction and ensuring reliable execution. Researchers have established various evaluation frameworks and metrics to assess function calling performance, including success rates, computational efficiency, parameter extraction accuracy, and response quality indicators such as ROUGE-L evaluation scores. This survey systematically reviews the current landscape of function calling in LLMs, analyzing technical challenges, examining existing solutions, and discussing evaluation methodologies. We particularly focus on practical implementations and industrial applications, providing insights into both current achievements and future directions in this rapidly evolving field. For a comprehensive collection of related research papers and the Appendix file, please refer to our repository at GitHub.

Field experiment data or crop observations at sites reported in agronomic literature are of high quality and have been considered as a potential source of information for the development of a global grid crop dataset. However, extracting data on a crop variable of interest from the text and tables of many papers is a time-consuming, painstaking task for dataset developers. Recent advances in large language models (LLMs) and resulting tools are expected to provide a promising solution. This study presents a computational method for extracting data from research papers using an LLM-based online tool, ChatPDF. The Python program we developed is applied to the 164 papers to extract crop phenology data of maize, soybean, wheat and rice for demonstration purposes. The results show that the LLM-based data extraction method can dramatically reduce the burden of data extraction in human curation, but needs improvement to become a reliable alternative that can replace manual data extraction. In particular, innovations are needed to increase the capture rate by avoiding data omissions and to reduce errors by correctly inferring longitudes, latitudes and harvesting years. The LLM-based data extraction is currently in its infancy and deserves future research for large-scale implementation.

Building and delivering high-quality software is critical in software engineering and requires verification and validation processes for end-to-end testing that are reliable, robust, and deliver correct results fast. Manual testing of LLM models, while feasible, is very time-consuming and inefficient and has scalability issues depending on how big the model under test (MUT) is. Recent research and cutting-edge technology innovations in LLM models have deeply influenced software engineering. We need to integrate its impact robustly in areas of model analysis, test automation, model execution, debugging, and report generation.This paper focuses on a framework approach for automated software testing of the LLM models to reduce human interactions and has improved results in a fast, cost-efficient, and time-efficient manner for automated testing methods for industries. The proposed Automation Framework (LLMAutoE2E) leverages and integrates LLMs for testing of different LLM models (like BERT, BART, Hugging Face, and multiple models available to test) to automate the end-to-end execution lifecycle of the LLM models. By leveraging LLMs, companies and industries can generate automated test cases, automated unit test codes, automated integration and end-to-end tests, and automated reporting of the LLM model's execution results. This research emphasizes the potential of the Automation Framework (LLMAutoE2E) for LLM to automate and streamline the overall execution and result generation of the LLM models and the overall testing workflows while addressing challenges in current LLM models testing, its accuracy and scalability for deployments, and reporting. The proposed Automation Framework (LLMAutoE2E) can also automate defect analysis, which improves the software reliability by a manifold and reduces the development cycles for companies. This Research paper details the role of Automation Frameworks for LLMs and how it is transforming QA processes, key methodologies, improving reliability and efficiency, addressing current challenges like model safety, bias detection, and continuous monitoring, and future trends in AI-driven software testing.

Background: The tumor microenvironment (TME) is a complex ecosystem of diverse cell types that interact dynamically, influencing tumor development and therapy response. Accurate cell type annotation in the TME is challenging due to partial overlap among marker genes, scarcity of representative reference datasets, and emergence of novel cell phenotypes. Our approach uses open-source Large Language Models (LLMs) including Llama 3.2 to provide a reference-free, efficient, and controllable method for cell type classification based on marker genes. Unlike tools that depend on curated reference datasets, this method demonstrates consistent performance across a wide range of cell types. By generating rationale for predicted cell types, our method offers a practical and interpretable framework for annotating diverse cellular populations while offering adaptability to the complexities of the TME. Methods: LangChain is a “Swiss-knife” platform for LLM applications. We developed a framework identifying cell types in the TME leveraging marker gene contexts. This approach combines structured outputs with predefined constraints to ensure biological plausibility and marker-based reasoning for interpretable cell type identification. Prompt engineering and schema-enforced validation ensure robust, biologically informed outputs, offering flexibility in ambiguous contexts while minimizing hallucinations. Results: Our method successfully identifies cell types in several cancer datasets, demonstrating high interpretability and reduced misclassification compared to competing methods, such as ScType and GPTCelltype. By employing a de novo cell typing approach, the model effectively resolves ambiguities in overlapping marker genes and adapts well to complex cell populations. The method also achieves superior performance in terms of agreement scores, TTFT (time-to-first-token), and TPOT (time-per-output-token), highlighting its efficiency in time-critical tasks. Explanations provided by the LLM support biological relevance and enhance user confidence in cell typing tasks. Conclusion: This study highlights the potential of light-weighted LLM for heterogeneous cell typing in complex environments like the TME. By circumventing the limitations of other cell typing methods, including those of reference-dependent and independent, this approach offers a flexible, robust, and interpretable solution for identifying diverse cell types in challenging biological contexts. Citation Format: Yooeun Kim, Jiwon Kim, Hongyoon Choi. Biologically informed cell typing in the tumor microenvironment with lightweight LLMs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2502.

The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist agents capable of operating within complex environments.These environments are often highly expansive, making it impossible for the LLM to process them within its short-term memory.Motivated by recent research on extending the capabilities of LLMs with tools, we seek to investigate the intriguing potential of tools to augment LLMs in handling such complexity by introducing a novel class of tools, termed middleware, to aid in the proactive exploration within these massive environments.Such specialized tools can serve as a middleware layer shielding the LLM from environmental complexity.In two representative complex environmentsknowledge bases (KBs) and databases-we demonstrate the significant potential of augmenting language agents with tools in complex environments.Notably, equipped with the middleware, GPT-4 achieves 2.8 the performance of the best baseline in tasks requiring access to database content and 2.2 in KB tasks.Our findings illuminate the path for advancing language agents in real-world applications. 1

The use of Natural Language Processing (NLP) in the field of engineering seems complex with many applications which require calculations and engineering judgements. Large Language Models (LLM) have been successfully presented to be one of the most effective solutions in many specific fields such as arts and literature, to finance, to even software development. However, the use of LLM in the field of Civil Engineering and specifically in Geotechnical Engineering has not been discussed often compared to the other fields of expertise that utilize the functionalities of Application Programming Interfaces (API) of OpenAI. This paper aims to cover the details of the framework and breakdown of how LLMs can be used to train a domain-specific dataset, specifically related to settlement analysis, ground improvement, to slope stability analysis and provide results that are generated from a fine-tuned model with the dataset of research papers and technical documents provided by geotechnical engineers. The paper presents three folds: introduction for LLM models with pre-trained models, and choice of the models used. Then, the paper discusses the fine-tuning process of the LLM model with a domain-specific database. The paper provides benchmarks and metrics for performance of the model using the technique called Recall-Oriented Understudy for Gisting Evaluation (ROGUE) for validation of the outputs by comparing the result with prompt-completion pairs. With sufficient data for the prompt-completion pairs from the experts in the field and also from the scientific literature and localized technical documents, the LLM model trained with data related to the geotechnical engineering domain provides reasonable responses that otherwise would not have been achieved with the original ChatGPT LLM model. This paper introduces innovative way to manage current database of geotechnical documents and literature that can be optimized to be used with LLMs. The paper provides discussion of the comparative results and the future potential capabilities of its fine-tuned model.

Business Process Model and Notation (BPMN) is a standard for formally modeling complex business processes. Manual creation of BPMN models can be time-consuming and error-prone, prompting a need for automation. Existing approaches, such as rule-based methods, machine learning, and machine translation, have progressed but face accuracy and real-world applicability challenges. In this research paper, we propose a novel method for automated extraction of BPMN models from textual descriptions using natural language processing (NLP) tools and deep learning models, including the spaCy library for text processing, a fine-tuned BERT model, and state-of-the-art large language models like GPT-3.5-Turbo and GPT-4. We utilize Graphviz, an open-source graph visualization software, to visualize the extracted processes. Our method supports representing tasks, exclusive gateways, parallel gateways, and start and end events in the generated BPMN models. The evaluation of 31 textual descriptions shows that our method generates process models with 96% accuracy using GPT-4 and 80% accuracy using GPT-3.5-Turbo large language models. Although subject to certain limitations, such as occasional inaccuracies in model outputs and reliance on well-formed input text, our approach offers a valuable contribution to the growing body of research on automating BPMN model generation.

False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process.In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu.The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs.

MotivationMetagenomic data increasingly reflect the coexistence of species from Archaea, Bacteria, Eukaryotes, and Viruses in complex environments. Taxonomic classification across the four superkingdoms is essential for understanding microbial communities, exploring genomic evolutionary relationships, and identifying novel species. This task is inherently imbalanced, uneven, and hierarchical. Genomic sequences provide crucial information for taxonomy classification, but many existing methods relying on sequence similarity to reference genomes often leave sequences misclassified due to incomplete or absent reference databases. Large language models offer a novel approach to extract intrinsic characteristics from sequences.ResultsWe present ICCTax, a classifier integrating the large language model HyenaDNA with complementary-view-based hierarchical metric learning and hierarchical-level compactness loss to identify taxonomic genomic sequences. ICCTax accurately classifies sequences to 155 genera and 43 phyla across the four superkingdoms, including unseen taxa. Across three datasets built with different strategies, ICCTax outperforms baseline methods, particularly on Out-of-Distribution data. On Simulated Marine Metagenomic Communities datasets from three oceanic sites, DairyDB-16S rRNA, Tara Oceans, and wastewater metagenomic datasets, it demonstrates strong performance, showcasing real-world applicability. ICCTax can further support identification of novel species and functional genes across diverse environments, enhancing understanding of microbial ecology.Availability and implementationCode is available at https://github.com/Ying-Lab/ICCTax.

Cyber-physical systems (CPS) have traditionally relied on rule-based mechanisms and machine learning models for context awareness. However, these approaches often struggle with dynamic adaptation, multimodal data integration, and real-time decision-making in complex environments. With the emergence of large language models (LLMs), we argue that CPS should adopt LLMs as adaptive cognitive layers capable of interpreting, reasoning, and responding to real-world contexts in real time. This position paper explores the paradigm shift introduced by LLMs, discusses their advantages and limitations, and presents a vision for their integration into next-generation CPS.

The Internet of Drones (IoD) integrates autonomous aerial platforms with security, logistics, agriculture, and disaster relief. Decision-making in IoD suffers in real-time adaptability, platform interoperability, and scalability. Conventional decision frameworks with heuristic algorithms and narrow Artificial Intelligence (AI) falter in complex environments. To mitigate these, in this study, an augmented decision model is proposed, combining large language models (LLMs) and retrieval-augmented generation (RAG) for enhancing IoD intelligence. Centralized intelligence is achieved by processing environment factors, mission logs, and telemetry, with real-time adaptability. Efficient retrieval of contextual information through RAG is merged with LLMs for timely, correct decision-making. Contextualized decision-making vastly improves adaptability in uncertain environments for a drone network. With LLMs and RAG, the model introduces a scalable, adaptable IoD operations solution. It enables the development of autonomous aerial platforms in industries, with future work in computational efficiency, ethics, and extending operational environments. In-depth analysis with the collection of drone telemetry logs and operational factors was conducted. Decision accuracy, response time, and contextual relevance were measured to gauge system effectiveness. The model’s performance increased remarkably, with a BLEU of 0.82 and a cosine similarity of 0.87, proving its effectiveness for operational commands. Decision latency averaged 120 milliseconds, proving its suitability for real-time IoD use cases.

The convergence of Artificial Intelligence (AI) and robotics has led to the emergence of embodied AI, where intelligent systems equipped with sensors and actuators interact with the physical world and operate alongside humans. These systems are transforming industries such as autonomous driving, healthcare, and household assistance. However, despite extensive research, embodied AI systems face significant limitations, including poor generalization and performance degradation in complex environments, hindering their commercialization. Recent developments in Large Language Models (LLMs) present new opportunities to address the above challenges. This study aims to explore the integration of LLMs into embodied AI systems, highlighting their potential to enhance scene understanding, reasoning, and planning capabilities. The paper provides a detailed review of LLMs’ applications in embodied AI, demonstrating how these models can improve the robustness and adaptability of AI systems. Additionally, the study examines the limitations of LLMs, such as hallucinations and efficiency challenges, and discusses potential solutions to mitigate these issues. Through an in-depth analysis of LLM-powered enhancements in embodied AI, this research underscores the transformative impact of LLMs on intelligent systems. By addressing current limitations and implementing innovative solutions, LLMs can significantly advance the field of embodied AI, paving the way for more versatile and intelligent systems that can operate effectively in diverse real-world environments.

Evaluation of ChatGPT and Google Bard Using Prompt Engineering in Cancer Screening Algorithms