This document presents the foundational manifesto of Fractal-Wave Algebra (FWA), a dynamic mathematical and computational paradigm that reframes arithmetic, algebra, and information processing as wave‑based, symmetry‑breaking evolutionary processes. Classical mathematics is described as a static projection of deeper dynamic wave transitions, while FWA introduces a generative ontology where numbers represent quanta of state evolution and algebraic operations correspond to active symmetry‑deformation transitions. The manifesto establishes the philosophical, mathematical, and physical basis of FWA. As stated in the document: “Classical mathematics is fundamentally static… Under FWA, numbers are historical markers of sequential symmetry breaking, and algebraic equations are projections of dynamic state evolution.” It contrasts dynamic arithmetic with classical linear arithmetic, showing that expressions like a + b = c are simplified macro‑projections of deeper wave‑state transitions. The document introduces operators such as Da, Pc, and AS, describing how symmetry deformation collapses into classical arithmetic when AS → 0. The manifesto applies FWA to major scientific paradoxes: P vs NP — demonstrating that NP complexity collapses to polynomial time inside wave‑native photonic hardware through multi‑layered interference and resonance reinforcement. Riemann Hypothesis — interpreting non‑trivial zeros as fractal wave nodes fixed to Re(s)=1/2 due to symmetry‑collapse constraints. The document outlines a technical blueprint for future computing hardware, including photonic integrated circuits, in‑memory wave computing, and wave‑based AI architectures. It emphasizes replacing digital matrix weights with phase‑resonance mappings and diagnosing errors as geometric variations across hierarchical wave levels. The manifesto also includes prior‑art Python code demonstrating self‑similar fractal acceleration for non‑linear data grids: “This document serves as an open-access foundational manifesto establishing prior art for Fractal-Wave Algebra and Dynamic Arithmetic applications…” Overall, this publication establishes the theoretical, computational, and engineering foundations of FWA as a new paradigm for mathematics, quantum computing, and artificial intelligence.
Kolesnikov Igor, Kolesnikov Igor· Zenodo (CERN European Organi...· 0 citations
This document presents a practical reliability guideline for using generative AI responses in business and everyday decision-making. It is based on a dialogue record in which an AI system was asked to analyze how memory, context, sycophancy, anchoring, and personalization may influence its own responses. The dialogue is not treated as empirical proof. Instead, it is used as an observational source from which a practical verification framework is derived. The central claim of this document is that the reliability of AI responses should not be judged by fluency or confidence, but by the type of question, verifiability, information density in training data, context dependence, and the availability of external validation. Definitions, code generation, structured summaries, translation, and transformations of provided text are relatively high-reliability uses. By contrast, current facts, numerical claims, prices, predictions, philosophical claims, personal intention inference, and AI self-evaluation require independent verification. This work is intended as a practical research note and guide rather than a peer-reviewed empirical study. Its purpose is to help users classify AI responses by reliability level, identify low-reliability signals, and apply verification steps before using AI outputs in business, education, research support, or everyday decision-making.
Takufumi Sato· Zenodo (CERN European Organi...· 0 citations
This revised preprint studies the derivative Laguerre quantities associated with the Jacobi theta kernel in the Fourier representation of the Riemann xi-function. It gives exact rational certificates showing that the ninth quantity is negative throughout a nontrivial interval around the symmetry point, with the certified range extended to absolute parameter value at most one fiftieth. It also verifies positivity at the symmetry point for levels one through eight and negativity at level nine. The proof uses explicit derivative polynomials, exact rational interval arithmetic, and elementary exponential bounds. A supplementary Python verifier reproduces the decisive sign computations using integer and rational arithmetic only. Ryan Kielhorn publicly deposited an exact level-nine counterexample at the symmetry point before the original Koide deposit. Brandon Yates later registered a Lean 4 formalization of the point counterexample. This revised version makes no priority claim for the point counterexample. Its distinct contribution is the certified interval of negativity, together with an exact and independently executable reproducibility certificate. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.
Akihiro Koide· Zenodo (CERN European Organi...· 0 citations
This upload archives and indexes 23 Reddit posts by Agerico Montecillo De Villa (u/propjerry / r/propjerry) covering the 200-day period from February 11 to August 30, 2026. The accompanying PDF provides scholarly abstracts, dates, tags, and URLs for the individual submissions. The collection spans Philosophy of Science, AI safety, systems governance, philosophy of education, philosophy of biology, macroeconomics, information theory, narrative systems, cybersecurity, provenance, and related areas. The Reddit submissions are mirror posts of Agerico Montecillo De Villa’s Substack posts, providing an additional public dissemination and archival channel for the same developing research program. Most of these posts are not presented as demonstrations that the Bridge360 Metatheory Model has been scientifically validated. Rather, they are intended primarily as Philosophy of Science heuristic probes: applications of the model to heterogeneous contemporary problems in order to test where its conceptual vocabulary generates useful questions, reveals previously obscured structural relationships, identifies possible anomalies or weak convergences, or reaches the limits of its own explanatory usefulness. The collection should therefore be read in continuity with the earlier Zenodo uploads ASI Engagement: Scientific Foundation of Hope (December 8, 2025) and In Search for Validation and Meaning: Bridge360 Metatheory Model “ASI Engagement: Scientific Foundation of Hope” monograph sequel (May 6, 2026). Those works establish the broader metatheoretical research program within which these shorter Reddit/Substack applications operate. The present archive documents subsequent attempts to expose that framework to a wide range of cases rather than restricting it to a single disciplinary domain. Among the cases indexed are autonomous-AI containment and cybersecurity failures, neural operators and physical AI, LLM sophistry and educational design, operational definitions of life, NVIDIA Vera Rubin and exploratory AI architectures, provenance under generative-AI abundance, ideological and inferential capture, ontologies in agentic systems, open-weight AI governance, operational intelligence systems, embodied cognition, and macroeconomic instability. The later entries also include applications to Rules-of-Inference Memetics, reversibility and rollback, weak-convergence detection, and registered macroeconomic forecast windows. Within this research program, “testing” is therefore heuristic and metatheoretical before it is empirical. The posts function as exploratory applications, conceptual stress tests, potential anomaly registers, and invitations for domain specialists to determine whether particular Bridge360 constructs can be operationalized, measured, falsified, revised, or rejected. Apparent correspondences between subsequent scientific, technological, economic, or institutional developments and earlier Bridge360 formulations should consequently be treated as material for further investigation rather than as retrospective proof of the metatheory. The PDF is intended as an archival research index and provenance record of this continuing series of public applications, making the chronology of the posts, their subject matter, and their evolving use of the Bridge360 Metatheory Model readily auditable.
Agerico De Villa· Zenodo (CERN European Organi...· 0 citations
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Generative artificial intelligence entered higher education English language teaching (ELT) faster than the field could theorize it, and the scholarly vocabulary available for describing what learners do when they write to a generative AI system has largely been borrowed from human-computer interaction, educational policy, and general AIliteracy research rather than built for the specific case of a second-language (L2) English user addressing a generative AI interlocutor in natural language. This chapter addresses that gap through a theoretical and conceptual analysis. Building on communicative competence theory (Hymes, 1972; Canale & Swain, 1980; Bachman & Palmer, 1996) and on the emerging literature that has begun to name and describe prompt literacy for language learners specifically – most directly Hwang et al. (2023) and Tour and Zadorozhnyy (2025) – the chapter argues that prompt-related competence for L2 English users is best theorized not as a subtype of general AI literacy or of prompt engineering, but as a linguistically grounded form of L2 communicative competence. It distinguishes this reading from Digital Literacy, Digital Competence, AI Literacy, and Prompt Engineering; proposes a four-dimension taxonomy (linguisticformational, pragmatic-interactional, metacognitive-strategic, critical-epistemic); integrates seven learning-theoretic traditions into that taxonomy while preserving a genuine, unresolved tension between cognitive load theory and learner autonomy theory; and develops a conceptually derived developmental continuum, an application across the skill areas of ELT, and a proposed assessment rubric grounded in validity theory (Messick, 1995). The central contribution is theoretical: a reconceptualization of prompt literacy, in continuity with rather than in place of the term's originators, as an applied-linguistic construct. The taxonomy, developmental continuum, and rubric are offered as theoretically derived, conceptually testable proposals rather than as validated instruments. The chapter is a theoretical and conceptual study; it does not report original empirical data collection, and it closes by naming the empirical work – piloting, inter-rater reliability testing, and validation research – that this proposal now requires. Keywords: prompt literacy; L2 communicative competence; generative AI; English language teaching; AI literacy; applied linguistics; assessment validity; higher education.
Daryna Pavlivna Mudryk· Zenodo (CERN European Organi...· 0 citations
Generative Artificial Intelligence (GenAI) has gained popularity despite skepticism from some people, especially educators, about its implications for learning dynamics. However, it has also proven to be an effective alternative learning tool because it can process a vast array of data with an efficiency that no other digital tool can match. Two challenges in Science Education in the Philippines are overworked teachers and poor scientific literacy, which this kind of digital media may help address. Accordingly, this research explored the effectiveness of the intervention, called Teacher-Enhanced AI-generated Video (TEAIV), in teaching experimental design ability (EDA) among pre-service and in-service science teachers (n = 22) using a quasi-experimental pretest-posttest design. The collected data were analyzed using Wilcoxon Signed-Rank and Binomial Tests. Results indicate that teachers significantly increased their EDA (p = 0.003; p = 0.031). In addition, reflective questions asked of all participants provided more detailed insights about the effectiveness of TEAIV. This study shows how combining human and artificial intelligence in a single learning material enables a better and more efficient teaching-learning process. This is especially useful for institutions that rely heavily on digital and online learning, particularly asynchronous and hybrid modalities, where they can explore interactive, expert-developed AI-generated videos.
The increasing use of Generative Artificial Intelligence (GenAI) in higher education has changed how students learn, complete tasks, and access information. In criminology education, tools such as ChatGPT are used for concept clarification, writing support, and task completion. This study aimed to explore the lived experiences of criminology students in Gingoog City regarding their use of GenAI for learning and personal development. Specifically, it examined their experiences with AI, how AI shapes their learning initiatives, critical thinking, and self-awareness, and their aspirations for its future use. The study employed a qualitative phenomenological design. Data were gathered through in-depth interviews and analyzed using Braun and Clarke’s thematic analysis. The findings revealed that students viewed AI as a valuable academic support tool that helped them understand difficult concepts, improve writing, organize ideas, and reduce academic stress. However, they also experienced ethical tension, including guilt, reduced pride, and diminished ownership of AI-assisted outputs. While AI supports comprehension, idea generation, and reflection, excessive dependence on it may weaken learning initiative, critical thinking, and independent effort. Students also recognized AI’s limitations and practiced verifying its outputs. The study implies the need for a clear policy to promote the fair, responsible, and ethical use of AI in the Criminal Justice Program. This research was completed in July 2026.
John Del L. Responso, Estelito A. Dela Cruz Jr.· Zenodo (CERN European Organi...· 0 citations
We classify regular full-dimensional stochastic containment among binary standard semi-directed strongly tree-child level-2 phylogenetic networks under the Kimura three-parameter (K3P) model. On the principal positive Fourier domain, a directed containment germ exists if and only if the labelled reduced trees of blobs agree and corresponding complete factors are either labelled-isomorphic or ordinarily triangle-redirected, with coherent boundary transports. The same condition is equivalent to a common full-dimensional regular germ and remains exact in strict continuous time. Thus no proper one-sided containment occurs in the strong class, and the semi-directed topology is generically identifiable and exactly reconstructible outside a proper exceptional set, modulo ordinary triangle redirection. The three ordinary K3P triangle orientations have generic normalized rank 14, share the same irreducible eight-term quartic hypersurface H₁₄ in normalized three-leaf Fourier space, and meet in a common strict continuous-time smooth rank-14 germ. The bounded residue consists of fourteen four-port directed relation orbits—nine polynomially separated and five directed-rank separated—plus two separately separated sink swaps. Exact restoration and coherent one- and two-port probes extend the bounded classification to arbitrary labelled subdivision words. Strong tree-childness is sharp against weakening to weak tree-childness. For every n ≥ 3, two weakly but not strongly tree-child networks have strict continuous-time K3P images sharing a common full-dimensional regular germ of dimension 6n − 3. This is the first Zenodo/DOI-bearing archival release of version 1.0.0 of the complete K3P level-2 classification and its reproducibility evidence. It contains the article, reader supplement, compile-complete source archives, canonical full proof archive, compact verifier, independent-referee replay package, exact manifest, checksums, citation metadata, and a file-level license notice. The deposited bytes correspond to immutable Git commit 0b76dd8e38f262ebe9ba8c1d23281853e334fef2, annotated tag k3p-level2-identifiability-v1.0.0 resolving to that commit, and full-archive SHA-256 4f84417f40b4e5ddae80b36d87dc7bb6d00389a573a58d0b82a2592c9f7c6403. The archive includes the previously completed all-producer regeneration evidence bound to unchanged mathematical inputs, together with successful exact and independently implemented replays, rigorous interval arithmetic where required, and fail-closed mutation tests. The present release changes bibliography, nonmathematical administrative/public-release prose, and release engineering only; unchanged multi-hour mathematical producers were not rerun during this dependency-scoped reseal. No empirical data set is used. Preprint; not peer reviewed by a journal. Generative-AI assistance and its verification workflow are disclosed in the article. Article, supplement, figures, documentation, and mathematical certificate data are licensed under CC BY 4.0; original verifier and build code are licensed under MIT, as mapped in LICENSES.md. No specific funding supported this work. The author declares no competing interests.
Alec Kriebel· Zenodo (CERN European Organi...· 0 citations
Despite the fast expansion of artificial intelligence (AI) in English as a foreign language (EFL) education, evidence regarding its effectiveness in supporting speaking development remains fragmented, with limited synthesis of AI tool types, methodological quality, learning outcomes, and research trends. This systematic review examined empirical studies on AI-supported EFL speaking development and was published between 2018 and 2026. A search of Scopus and Web of Science was conducted using keywords related to AI, EFL, and speaking skills, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data from 33 studies that met the inclusion criteria were analyzed according to established coding categories aligned with the research questions. The findings indicate that AI tools include conversational agents, speech recognition systems, intelligent tutoring systems, generative AI, and multimodal applications, which each support different aspects of speaking development. Speech recognition tools mostly improved pronunciation, while conversational and generative AI mainly enhanced fluency, confidence, and autonomous speaking practice. Affective gains, including less speaking anxiety and increased willingness to communicate, were more consistent than improvements in more complex speaking skills. This review also found that the evidence base is methodologically inconsistent, concentrated in higher education and specific geographical regions, and increasingly dominated by generative AI technologies.
Paul F. Gonzalez-Torres, Eliana I. Pinza· International Journal of Lea...· 0 citations
Manufacturing supply chains are vulnerable to disruptions such as supply delays, demand fluctuations, equipment failures, and logistics interruptions, which may cause inventory imbalance, capacity reduction, and delivery delays.Existing studies often rely on predefined scenarios and static indicators, limiting the characterization of dynamic degradation and recovery processes.This study proposes a generative artificial intelligence-enabled simulation-based method for manufacturing supply chain resilience assessment.A discrete-event simulation model integrating suppliers, inventories, production workshops, finished-goods warehouses, and customer orders was developed.Generative artificial intelligence was used to generate diverse disruption scenarios, which were converted into structured simulation inputs.Through multiple simulation experiments, resilience was evaluated based on performance degradation, recovery time, order fulfilment, inventory stability, and operational cost.The proposed framework supports supply chain stress testing, vulnerability identification, and recovery strategy evaluation under complex disruptions.
Y. K. Chu, Q. Chen· International Journal of Sim...· 0 citations
Generative Artificial Intelligence (AI) tools have become embedded in the everyday academic practice of undergraduate engineering students, yet most large language models remain optimised for standard English rather than the code-mixed, multilingual registers through which students in linguistically plural regions actually think and communicate. This study examines technology acceptance of vernacular and code-mixed AI interaction among 84 undergraduate engineering students enrolled in APJ Abdul Kalam Technological University (KTU)-affiliated institutions in Kasaragod district, Kerala, a region historically described as Saptha Bhasha Sangama Bhoomi, the confluence land of seven languages. Using a structured questionnaire grounded in the Technology Acceptance Model (Davis, 1989), the study measured Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Output Accuracy, and Linguistic Inclusion across five research hypotheses. Findings indicate that students from regional-medium secondary schooling backgrounds report significantly higher vernacular or code-mixed AI prompting than English-medium peers, chi-square(3, N = 84) = 22.91, p < .001. Perceived Usefulness correlates strongly with Perceived Ease of Use, r = .64, p < .001. Students who habitually use vernacular or code-mixed prompts report significantly higher ease of use than strictly English prompters, t(82) = 2.01, p = .048. Perceived terminological distortion is positively associated with reported reliance on AI-translated academic content, r = .27, p = .012, and native speakers of the unscripted Tulu dialect report markedly higher AI comprehension failure than speakers of scripted regional languages, t(79) = 11.60, p < .001. The results support all five hypotheses and highlight a persistent linguistic-inclusion gap in generative AI systems used within multilingual engineering classrooms. Implications for dialect-aware AI design and inclusive digital pedagogy in polyglot regions such as Kasaragod are discussed.
Amal George· Zenodo (CERN European Organi...· 0 citations
The rapid growth of GenAI has provided with new opportunities to English Language Learning, including personalized language learning, immediate feedback, error correction, writing assistance and explanations. However, the increasing availability of AI-generated content raises essential questions towards students’ critical thinking and independent intellectual engagement. When learners totally agree to AI-generated content without analyzing its relevance, accuracy, appropriateness or limitations, the technology might assists with task completion while limiting possibilities for independent reasoning. This paper analyzes the potential of Generative Artificial Intelligence in order to support critical thinking in English Language Learning and determine pedagogical conditions which may prevent passive reliance on AI-generated content. Based on recent studies on GenAI, critical thinking, EFL education and AI literacy, the paper adopts a conceptual approach so that to examine not only educational opportunities but also cognitive challenges regarding AI-supported learning. Specific focus is given to the role of the students in evaluation of AI-generated responses and to the role of the teachers in developing activities which require reflection, analysis, verification, evaluation and independent decision-making. The paper contends that the educational value of generative artificial intelligence highly depends on how learners interact with the answers rather than its ability to produce quick and sophisticated responses. Therefore, rather than treating AI as an unquestionable source of knowledge, students should be motivated to utilize artificial intelligence tools as an object of critical inquiry. This paper, thus, proposes a pedagogical sequence where AI assistance is followed by critical questioning, verification, reflection and independent judgement. This approach can allow GenAI to function as a cognitive support while remaining the learner’s active role in the learning process. In summary, the study concludes that responsible implementation of generative artificial intelligence into English Language Education demands a change from answer-oriented AI usage toward reflective and critical AI-supported learning. Hence, educators play a vital mediating role in assisting students to develop abilities to implement, question, evaluate and responsibly apply AI-generated content..
Diyora Muxsinjanova· Zenodo (CERN European Organi...· 0 citations
