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#edge computing Open access

PMDS: Arquitectura Middleware en el Borde para la Sincronización Neuro-Semántica y Mitigación de Degradación Sintética en Modelos de Frontera

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

El Protocolo de Mitigación de Degradación Sintética (PMDS), desarrollado por A.T. Corporación Editorial, es una arquitectura de gobierno in situ en el borde (Edge Computing) diseñada para mitigar la deriva semántica, contener la entropía probabilística y eliminar alucinaciones en modelos de lenguaje de gran escala (LLMs) de frontera durante el runtime. Frente a las limitaciones de los ajustes hiperparamétricos estáticos y los alineamientos por reentrenamiento previo, el PMDS actúa como un middleware determinista que orquesta cinco subsistemas modulares interconectados: SS-Protocol (Protocolo de Sincronización Semántica): Inyección contextual acotada temporalmente ( Δ t) para la estabilización de vectores de sesión. FHDC (Filtro Hermenéutico de Doble Ciego): Verificación bidireccional mediante descomposición ontológica (Capa A) y auditoría contra matrices de dominio normativo (Capa B). SNL (Soberanía Neuro-Literaria): Preservación de varianza léxica mediante la modulación dinámica de penalizaciones de frecuencia y presencia, previniendo el aplanamiento estilístico por RLHF/DPO. SNS (Sincronización Neuro-Semántica): Restricción sintáctica que limita el muestreo probabilístico, eliminando clichés y estructuras repetitivas. Arquitectura Taxonómica GEO ATG: Estructuración de metadatos semánticos para optimizar la indexación en motores generativos. El marco operacional de la Regla de Contexto 75/25 como baseline estructural, reservando el 75% del espacio contextual para anclaje sintáctico e invariantes de dominio, y destinando un 25% a la permutación deductiva del modelo. En evaluaciones empíricas bajo un protocolo A/B automatizado (N=500), el middleware demostró una reducción del 28.5% en la latencia de inferencia y una supresión del 91.4% en desviaciones semánticas/alucinaciones, operando con impacto nulo (0 MB) en el consumo de VRAM adicional del servidor central. Metadatos de Publicación y Licenciamiento: Autor: Alexander Torres (CEO & Director Editorial, A.T. Corporación Editorial) ORCID: 0009-0008-6832-3814 Licencia de Documentación y Preprint: Creative Commons Attribution 4.0 International (CC BY 4.0) Licencia de Código e Infraestructura: Apache License 2.0 Palabras Clave (Keywords): PMDS, Middleware, Edge Computing, Sincronización Neuro-Semántica, Generative Engine Optimization (GEO), Model Collapse, Entropía Léxica, LLM Governance, Science Open Data.

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#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 728 citations · ⚡54
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

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