Skip to content
#generative ai Open access

Generic Identifiability and Directed Containment for Strongly Tree-Child Level-2 Networks under the Kimura Two-Parameter Model: The Principal Positive Domain and Strict Continuous Time

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research) · 1 references

Abstract

We classify regular full-dimensional stochastic containment among binary standard semi-directed strongly tree-child level-2 phylogenetic networks under the Kimura two-parameter (K2P) model. On the principal positive Fourier domain D₊ = {(s,g): 02s−1}, a directed containment germ exists if and only if the two labelled networks are isomorphic after independently redirecting ordinary three-cycle factors. The same condition is equivalent to a common full-dimensional regular germ; in particular, no proper one-sided containment occurs. It follows that the semi-directed topology is generically identifiable modulo ordinary triangle redirection, and that its structural triangle class is exactly reconstructible away from a proper algebraic exceptional set. The proof combines displayed-quartet inequalities and exact whole-map identities, an exact two-sector bridge-fibre theorem, physical marginal submersions, localization, and a bounded graph-to-algebra classification of cycle and theta factors. The bounded classification is computer-assisted: every directed primitive relation, rank exclusion, restoration parent, transport, and one-/two-port probe is represented by an exact certificate with independent replay and mutation evidence. The classification transfers to the strict continuous-time domain 0<s<1, s²<g<1. For every n≥3, two weakly but not strongly tree-child level-2 networks have continuous-time K2P images sharing a regular germ of dimension 4n−3, proving sharpness of strong tree-childness. This record is the complete v1.0.5-r1 priority and reproducibility package: the 26-page article, 24-page reader supplement, compile-complete five-file source archive, deterministic 495-member referee/verifier archive, external archive-qualification report, checksum sidecars, and dual-license notice. The manuscript source is v1.0.5; revision r1 repairs only an auxiliary probe-current semantic binding and changes neither the theorem, manuscript, PDFs, nor frozen classification. The clean verifier replay passed 41/41 layers, and the focused semantic mutation suite rejected 20/20 attacks. Exact source bindings: package tag k2p-same-referee-package-v1.0.5-r1; annotated tag object 6c9c89d38f4f4cdc9c328d8bb1237458c617136d; commit e2f6e32e6fe885e90c8e83a8c5b00785e663a4ae; referee archive SHA-256 4564cd1f8cd95f670a2e0d9619babaf3c343762cfd8ceeb190cd17df72802889. Article, supplement, and certificate data are licensed under CC BY 4.0; verifier and build code are licensed under MIT. No specific funding supported this work. The author declares no competing interests. Generative-AI assistance and its verification workflow are disclosed in the article. No mixed-sign K2P classification is claimed.r

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method 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 · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our 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. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation .

Malik Abdul Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 43 citations · ⚡2
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

AI Ethics in Industry: A Research Framework

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 27 citations · ⚡3

Related blog posts