Computational Complexity and Energetics of the Ant Stack

Catalog Row11

Citation KeyFriedman2025ComputationalComplexityEnergeticsAnt011

Paper FolderAvailable

Platform availability

✅ Zenodo
⬜ GitHub
⬜ arXiv
⬜ OSF
⬜ HuggingFace
⬜ Software Heritage
⬜ PyPI
✅ Full documentation

Overview

Extracted from the local paper documentation when available.

Extending the AntStack framework, this paper examines complexity science approaches to understanding ant colony organization. We connect concepts from information theory, complex adaptive systems, and non-equilibrium thermodynamics to the multilevel analysis of social insect colonies.

AntStackcomplexity scienceinformation theorycomplex adaptive systemsant coloniesnon-equilibrium thermodynamics

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts

We present a comprehensive computational complexity and energy analysis framework for the Ant Stack, an integrated biomimetic architecture for embodied artificial intelligence..
Our investigation employs analytical models for contact dynamics physics, sparse spiking neural networks, and active inference to characterize complexity and energy consumption in real-time embodied systems operating at 100 Hz control frequencies..
Energy efficiency has emerged as a critical constraint in embodied AI systems, yet traditional complexity analysis fails to capture the nuanced energy-performance trade-offs inherent in real-world implementations..

Methods / Techniques

Field observation and behavioral assays
Population genetics analysis

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2025. Computational Complexity and Energetics of the Ant Stack. Zenodo. DOI: 10.5281/zenodo.17238736. URL: https://doi.org/10.5281/zenodo.17238736.

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