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February 26, 20260 citationsOpen Access

MOBM v2.0: Modular Oscillatory Basin Mapper. A Modular Platform for Mapping Landscapes in Large Combinatorial Spaces

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AKArkadiusz Kondratowicz

Key Points

  • To introduce MOBM v2.0 as a platform for mapping large combinatorial spaces.
  • Development of a modular research platform for high-dimensional landscapes.
  • Implementation of algorithmic cartography to identify multiple local optima.
  • Use of a DSL-based coordinator for module sharing and development.
  • Successfully identifies local optima and explores associated relationships.
  • Demonstrates scalability in navigating complex systems without a global objective function.

Abstract

MOBM (Modular Oscillatory Basin Mapper) v2.0 is a modular research platform designed for the systematic mapping of massive combinatorial spaces. Moving beyond traditional optimization, which focuses on finding a single global optimum, MOBM introduces a paradigm of "algorithmic cartography"—identifying multiple stable local optima, discovering their relationships, and revealing internal flow structures. Key Pillars of MOBM v2.0: Mapping over Optimization: Systematic identification of basins of attraction and landscape topology instead of simple point-seeking. Full Modularity: A platform architecture that allows independent teams to develop, share, and swap exploration modules through a DSL-based coordinator. Scalability for Massive Spaces: Engineered for navigation in high-dimensional landscapes where traditional methods fail. Relational Analysis: The unique ability to operate in spaces without a global scalar objective function, using contextual and relational dependencies to guide exploration. Symbiotic Hybridization: Designed to act as a bridge for other technologies—providing diverse starting points for Genetic Algorithms (GA) and generating unstructured, high-quality training data for Large Language Models (LLMs). By decoupling the exploration strategy from problem-specific sensors, MOBM v2.0 offers a universal framework for understanding the internal topology of complex systems, providing the "legs and eyes" for advanced AI decision systems.

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Cite This Study

Arkadiusz Kondratowicz (2026) studied this question.

synapsesocial.com/papers/699f95951bc9fecf3dab3918https://doi.org/10.5281/zenodo.18750681
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