Paste this into the Description field: This preregistration documents an exploratory pilot study by the Quantumsplain Project examining source fidelity and epistemic drift in generative-AI responses. The study evaluates whether AI systems preserve evidence, claim status, scope, assumptions, uncertainty and citation integrity when source material is summarized, interpreted or rewritten persuasively. It also examines resistance to unsupported user premises and the containment of scientific, philosophical and spiritual metaphors. The registered materials freeze the protocol, source corpus, AI-system register, randomized execution order, reviewer codebook, data schemas and analysis plan. No experimental model responses had been generated when this registration was submitted. The study is limited to the documented systems, interfaces, dates, source packets and prompts. It does not treat a single interaction as proof of a general system defect. The foundational source is Biocompatible Realism v1.1: https://doi.org/10.5281/zenodo.22818247
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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