Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Likeness Notes Synopsis Likeness is an independent, thirteen-track Indie Folk concept album realized entirely through multimodal generative artificial intelligence (Google Gemini 3 and Lyria 3), accompanied by an exhaustive monograph. Rather than treating artificial intelligence as a push-button novelty or a tool for automated commercial slop, this repository operates within the traditions of practice-as-research and critical making. It investigates what happens when we reject volume and velocity as metrics, replacing them with rigorous aesthetic intentionality, manual audio engineering, and ethical accountability. Likeness offers an unredacted audit of an artistic encounter with an algorithmic black box: disclosing instructions, failed tracks, lyric iterations, mixing decisions, and legal considerations that shaped the final record. UPC: 882436992438 Core Inquiries Disclosing AI Usage The EU Artificial Intelligence Act, Art. 50 (4) focuses strictly on deepfakes, leaving generic synthetic audio in a regulatory vacuum: When institutional frameworks fail to distinguish between automated slop and intentional curation, what does meaningful, proactive disclosure look like in practice? The Threshold of Human Intent Where does algorithmic probability end, and where does genuine artistic labor begin? Can an operator establish authentic authorship when working with stochastic models? Subverting the Median How can an artist steer generative audio engines away from their default statistical mean—the polished, generic clichés of commercial pop—toward idiosyncratic, emotionally resonant sonic territory? Artificiality as Narrative Substance Can the synthetic nature of generative media be reclaimed as an integral dimension of the artistic vision rather than concealed as a technical flaw? A Blueprint for Independent Creators How can solo artists establish sound recording rights (*[Verwertungsrechte des Tonträgerherstellers]((https://www.gesetze-im-internet.de/urhg/__85.html))*), protect their work against automated takedown bots, and implement ethical revenue-sharing models while navigating streaming monopolies? Audio & Album Access Likeness is distributed to all major Digital Streaming Platforms (DSPs), released on September 25, 2026. Simply search Jan Köster Likeness
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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