Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Narrative Theory and AnalysisArtificial Intelligence in Games
Abstract
This work introduces Local Narrative Physics, a formal mathematical framework for modeling narrative coherence within bounded regions of a story world. The central contribution is the formulation of five field equations—narrative momentum conservation, narrative Gauss law, narrative thermodynamic inequality, affective wave propagation, and a narrative uncertainty principle—each defined on a coherent world branch W and a local region Ω⊂W. These equations capture how information density, causal alignment, affective dynamics, and reader–author uncertainty interact to produce locally coherent narrative segments even when global consistency is absent, as in dreams, multi‑world structures, or meta‑fictional transitions. The framework integrates concepts from information theory, statistical estimation, cognitive psychology, and dynamical systems. It incorporates Fisher information I(θ;Ω,W) and chunk capacity Cchunk(W) to model semantic precision and cognitive load, providing quantitative constraints on narrative entropy and uncertainty. The appendices develop functional models for chunk‑based cognition, operator‑level rigor for narrative phase spaces, boundary conditions for meta‑structural transitions, and algorithms for dynamically estimating Fisher information and chunk capacity within simulation environments. This theoretical structure is intended not only for narrative analysis but also as a consistency‑governing module for future world models, cognitive architectures, and generative systems. As AI systems expand beyond physical world modeling into subjective cognition, imagination, and dream‑like simulation spaces, Local Narrative Physics offers a principled mechanism for maintaining semantic coherence across diverse experiential domains.
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.
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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.
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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.
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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.
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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.
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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