Generative Seed Compression for Deterministic 3D Structures
Authors/Creators
Description
Causal Compression of Three-Dimensional Structural Data
The storage and transmission of three-dimensional structural data—from molecular configurations to digital twins—has become a critical bottleneck in computational biology, materials science, and large-scale simulation. As datasets grow exponentially, the energy and bandwidth costs of moving coordinate-heavy files threaten to outpace computational advances.
Traditional compression algorithms (ZIP, DEFLATE, LZMA) have reached fundamental limits when applied to floating-point coordinate arrays. These methods attempt to exploit statistical redundancy in byte patterns, but the quasi-random distribution of IEEE 754 mantissa bits presents near-maximal entropy, yielding compression ratios rarely exceeding 6:1.
We propose a paradigm shift: causal compression. Rather than compressing the coordinates themselves, we transmit the generative seed from which the structure can be deterministically reconstructed. This approach inverts the traditional data flow, storing the cause rather than the effect.
For structures generated via deterministic dynamical systems—demonstrated here using Collatz-sequence modulation on graph topologies—we achieve compression ratios exceeding 100:1 against raw representations and 15:1 against optimized general-purpose compressors, with exact reconstruction (RMSD = 0).
The compressed representation remains constant at 15 bytes, regardless of structure size, yielding ratios that scale linearly with atom count, demonstrated up to 1600:1 for 1000-atom structures.
This work opens a pathway toward transmitting “algorithmic matter”—complex three-dimensional structures encoded not as data, but as executable recipes. We present the mathematical formalism and invite the community to explore the boundaries and extensions of this approach.
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ACADEMIC USE AND COLLABORATION NOTICE
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This work is made available to support academic study, teaching, and collaborative research.
Students, educators, and independent researchers are encouraged to study, experiment with, improve upon, and discuss the ideas and implementations presented here for educational and research purposes.
The noncommercial license below is intended solely to prevent unauthorized commercial exploitation, and is not meant to restrict learning, collaboration, or good-faith scientific exchange.
Use in commercial or proprietary contexts requires a separate agreement.
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POLYFORM NONCOMMERCIAL LICENSE 1.0.0
COPYRIGHT NOTICE
Software / Work:
Generative Seed Compression for Deterministic 3D Structures
Copyright © 2026
Andrés Sebastián Pirolo
All rights reserved.
Contact:
apirolo@abc.gob.ar
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1. Rights Granted
The “Licensor” (Andrés Sebastián Pirolo) grants you the following rights:
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Install and execute the software / algorithms for
personal, educational, or academic research use. -
Modify the software / algorithms for
personal, educational, or academic research use. -
Distribute the software / algorithms, provided that:
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- This license file and the copyright notice
are included in all copies.
2. Limitations
You may NOT use the software / algorithms,
or any modified version, for any Commercial Purpose.
“Commercial Purpose” includes, but is not limited to:
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- Using the software / algorithms to develop a product for sale
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commercial research and development.
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THE SOFTWARE / WORK IS PROVIDED “AS IS”,
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please contact the Licensor to obtain a separate commercial license:
Andrés Sebastián Pirolo
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Pirolo2026Generative Seed Compression.pdf
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