Colony-Based Adaptive Image Generation: A Framework for Emergent Visual Synthesis Through Local Growth, Proliferation, Competition, and Reinforcement Learning
Abstract
Contemporary image generation methods commonly construct images through learned latent representations, iterative denoising, autoregressive prediction, or transformations of existing visual content. This paper proposes Colony-Based Adaptive Image Generation (CBAIG), a framework that investigates an alternative organizational principle: images emerge through the spatial development and interaction of multiple localized generative colonies. Inspired by colony formation in biological systems, the proposed framework represents an image as a dynamically evolving field in which generative units can expand, proliferate, differentiate, compete for spatial resources, merge with compatible neighboring colonies, and terminate their growth. Unlike a fixed partitioning strategy, colony-based generation allows the number, spatial extent, and functional roles of generative units to change during synthesis. Unlike purely local growth rules, the framework incorporates global image objectives and optional reinforcement learning to coordinate local actions with overall visual quality. We formalize the image state, colony state, candidate growth process, competitive spatial allocation, colony differentiation, merging, and termination. We further introduce a reward formulation that accounts for improvements in perceptual quality, semantic alignment, structural coherence, computational cost, and unwanted interference. A hierarchical controller separates local growth decisions from global coordination. Because the proposed approach is a research framework rather than an empirically validated system, this paper specifies a reproducible experimental protocol instead of reporting fabricated performance results. Experiments are designed to evaluate whether dynamic colony interactions provide advantages over fixed growth rules, noncompetitive growth, cellular-automaton-based generation, and suitable pretrained image-generation baselines. The framework establishes a testable formulation for studying image synthesis as adaptive colony formation and identifies the conditions under which this organizational principle may be useful.
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BibTeX
@misc{zhang2026colonybased,
title = {{Colony-Based Adaptive Image Generation: A Framework for Emergent Visual Synthesis Through Local Growth, Proliferation, Competition, and Reinforcement Learning}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23555922},
url = {https://zenodo.org/records/23555922},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Colony-Based Adaptive Image Generation: A Framework for Emergent Visual Synthesis Through Local Growth, Proliferation, Competition, and Reinforcement Learning
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23555922
UR - https://zenodo.org/records/23555922
AB - Contemporary image generation methods commonly construct images through learned latent representations, iterative denoising, autoregressive prediction, or transformations of existing visual content. This paper proposes Colony-Based Adaptive Image Generation (CBAIG), a framework that investigates an alternative organizational principle: images emerge through the spatial development and interaction of multiple localized generative colonies. Inspired by colony formation in biological systems, the proposed framework represents an image as a dynamically evolving field in which generative units can expand, proliferate, differentiate, compete for spatial resources, merge with compatible neighboring colonies, and terminate their growth. Unlike a fixed partitioning strategy, colony-based generation allows the number, spatial extent, and functional roles of generative units to change during synthesis. Unlike purely local growth rules, the framework incorporates global image objectives and optional reinforcement learning to coordinate local actions with overall visual quality. We formalize the image state, colony state, candidate growth process, competitive spatial allocation, colony differentiation, merging, and termination. We further introduce a reward formulation that accounts for improvements in perceptual quality, semantic alignment, structural coherence, computational cost, and unwanted interference. A hierarchical controller separates local growth decisions from global coordination. Because the proposed approach is a research framework rather than an empirically validated system, this paper specifies a reproducible experimental protocol instead of reporting fabricated performance results. Experiments are designed to evaluate whether dynamic colony interactions provide advantages over fixed growth rules, noncompetitive growth, cellular-automaton-based generation, and suitable pretrained image-generation baselines. The framework establishes a testable formulation for studying image synthesis as adaptive colony formation and identifies the conditions under which this organizational principle may be useful.
ER -