POLYFORM NONCOMMERCIAL LICENSE 1.0.0 COPYRIGHT NOTICE Software / Work:Generative Seed Compression for Deterministic 3D Structures Copyright © 2026Andrés Sebastián PiroloAll rights reserved. Contact:apirolo@abc.gob.ar TERMS AND CONDITIONS 1. Rights Granted The “Licensor” (Andrés Sebastián Pirolo) grants you the following rights: Install and execute the software / algorithms forpersonal, educational, or academic research use. Modify the software / algorithms forpersonal, educational, or academic research use. Distribute the software / algorithms, provided that: No fee is charged for the software / algorithms. This license file and the copyright noticeare 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: Using the software / algorithms to provide a service for a fee. Using the software / algorithms to develop a product for sale(e.g., drug discovery pipelines, compression software, proprietary databases). Using the software / algorithms in business operations orcommercial research and development. 3. No Warranty THE SOFTWARE / WORK IS PROVIDED “AS IS”,WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,INCLUDING BUT NOT LIMITED TO THE WARRANTIES OFMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE,AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 4. Commercial Licensing For commercial use, including: integration into corporate R&D pipelines, or inclusion in commercial software products, please contact the Licensor to obtain a separate commercial license: Andrés Sebastián Pirolo lctrnc1@gmail.com

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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