Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Short Abstract Co-Intelligence Version 3.0 develops Co-Intelligence as an architecture for intelligence engineering based on the structural coupling of human and artificial intelligence. It formalizes the Human Engine (H-Engine), AI Engine (A-Engine), and their composite C-Engine, and presents Dual-Loop Reasoning as a framework in which human conceptual insight and AI-based consistency checking operate together. The work introduces Controlled Hallucination as a bounded method for extending inference into unknown or underdetermined domains while maintaining structural constraints, verification requirements, and explicit limits. It examines the generative conditions, control parameters, reproducibility, protocol design, application domains, limitations, and future development of domain-optimized intelligence systems. The framework is presented as a research hypothesis and engineering model to be evaluated through continued verification and practical application. Japanese Abstract 『協働知性 Version 3.0』は、人間知性と人工知能の構造的結合に基づく「知性工学」の体系として、協働知性を発展させる研究です。人間のH-Engine、AIのA-Engine、両者を統合するC-Engineを定式化し、人間による概念的洞察とAIによる整合性検討が連動するDual-Loop Reasoning(二重ループ推論)を提示します。 本書では、未知または不確定な領域へ推論を拡張しながら、構造的制約、検証要件、適用限界を維持する方法として「制御されたハルシネーション」を導入します。さらに、協働知性の生成条件、制御指標、再現性、プロトコル設計、応用領域、非適用領域、分野最適化型知性システムの将来像を検討します。本体系は、継続的な検証と実践的応用によって評価される研究仮説および工学モデルとして提示されます。
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026