AETHEL-SWARM: A Logic-Agnostic Binary Self-Healing Engine via Dynamic Phase-Inverted Swarm Optimization
Year: 2026
Type: preprint
Abstract: AETHEL-SWARM (Ultra-Swarm Engine v3.0) introduces a logic-agnostic binary self-healing framework capable of autonomously repairing corrupted binary data sequences without prior mathematical models, structural assumptions, or algebraic error-correcting matrices (such as Hamming or Reed-Solomon codes). The software utilizes a population-based heuristic driven by: - Dynamic bit-length scaling to prevent premature convergence. - Stochastic mutation operators (bit-flipping, cyclic shifts). - Global phase inversions (0 <-> 1 bit swapping) to escape local traps. - Anti-Stagnation Injection ("fresh blood") to purge sub-optimal lineages. To overcome the combinatorial complexity of high-dimensional binary spaces, AETHEL-SWARM employs a segmented architecture that decomposes large payloads into independent sub-segments. Experimental validation demonstrates 100% precision reconstruction of a 256-bit payload subjected to severe 21.09% bit corruption (54 inverted bits) within 165 operational cycles. This repository contains the full technical report, architectural specifications, and standalone executable Python source code (aethel_swarm.py) for independent testing and deployment.
Author: NEDELCHEV, HRISTO
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225011997