The CCIS book constitutes selected papers of the 12th International Conference on Computational Science and Computational Intelligence, CSCI 2025, which took place in Las Vegas, NV, USA, during December 35, 2025.

The 40 full papers included in this book were carefully reviewed and selected from 294 submissions to the Research Track on Large Language Models, CSCI-LLM.

They were organized in topical sections as follows: Large Language Models, Knowledge Graphs, Prediction Methods, and Learning Methods; Large Language Models, Natural Language Processing, Security Methods, and Applications; Large Language Models, Data Science, and Related Issues; and Large Language Models, Artificial Intelligence, and Applications.

Inhalt

.- SECTION: Large Language Models, Knowledge Graphs, Prediction Methods, and Learning Methods.
.- LangChain-Integrated LLMs and Knowledge Graphs for Multi-Agent Reinforcement Learning in Complex Air Combat Environments.
.- From Prediction to Persuasion.
.- Dynamic Learning.
.- Enhancing Context-Aware Code Search with Knowledge Graphs and Large Language Models.
.- Multi-hop Natural Language Question Answering on Graphs by Using LLMs.
.- A Proactive Approach for Predicting Equipment Downtime in an Enterprise.
.- Hybrid NLP-LLM Pipelines for Automatic Learning Content Generation.
.- Evaluating Ensemble Learning for Robust Real Time Deepfake Detection.
.- SECTION: Large Language Models, Natural Language Processing, Security Methods, and Applications.
.- Metaphor Detection and Translation for Representing Natural Language in Formal Logic.
.- Kryptonite for Small LLMs.
.- Leveraging Large Language Models for Cybersecurity Compliance.
.- Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models.
.- Cross-Model Study of LLM Capabilities for Phishing Content Generation.
.- SECTION: Large Language Models, Data Science, and Related Issues.
.- Magnol.ai Plan & Execute.
.- CURATE-FT.
.- Utilizing Large Language Models to Structure and Analyze Large Unstructured Data Sources.
.- Fossilized Memories.
.- The Implications of Large Language Model Integration for Bias in Lending Practices.
.- SECTION: Large Language Models, Artificial Intelligence, and Applications.
.- E^3-Guarded Generation.
.- PCRLLM.
.- Comprehending LLM- vs. Human-generated Business Process Models.
.- LLM-Based Process Modeling.
.- QOC DAO.
.- An Intelligent Human-AI Collaboration Pipeline for Automated Extraction of PFAS Partition Coefficients.
.- Time and Energy Consumption of a Large Language Model.
.- LLM Driven Processes to Foster Explainable AI.
.- Generative AI for Robotic Autonomy and Planning.
.- Increasing AI Explainability by LLM Driven Standard Processes.
.- Comparative Evaluation of Prompting Techniques for Forensic Video Analysis Using GPT-4o.
.- Enhancing Large Language Model Accuracy with Retrieval-Augmented Generation.
.- Monosemantic Feature Neurons.
.- Toward Reliable AI-driven Autonomous Decision-Making.
.- Comparative Analysis of Large Language Models for Multimodal Agricultural Intelligence in Precision Nursery Management Systems
.- The Agentic Leash.
.- Ayna.
.- What's in a Name?.
.- KAN-FFN Replacements in Compact Transformers.
.- Convergence of Outputs When Two Large Language Models Interact in a Multi-Agentic Setup.
.- LegalSumAI.
.- Adaptive Melodic-Entropy-Guided Preprocessing for Lightweight MIDI Generation Models.