# Energentic Intelligence: From Self-Sustaining Systems to Enduring Artificial Life

arXiv: https://arxiv.org/abs/2506.04916 · HTML: https://mlsanity.com/paper/2506.04916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Computational Inertia as a Conserved Quantity in Frictionless and Damped Learning Dynamics](https://mlsanity.com/paper/2505.19171/) (2025)

## Built on by (most cited first, 3 of 3)

- [Self-Organizing Survival Manifolds: A Theory for Unsupervised Discovery of Prognostic Structures in Biological Systems](https://mlsanity.com/paper/2508.06539/) (2025)
- [CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI](https://mlsanity.com/paper/2509.13356/) (2025)
- [A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI](https://mlsanity.com/paper/2605.31021/) (2026)

## Abstract (arXiv)

This paper introduces Energentic Intelligence, a class of autonomous systems defined not by task performance, but by their capacity to sustain themselves through internal energy regulation. Departing from conventional reward-driven paradigms, these agents treat survival-maintaining functional operation under fluctuating energetic and thermal conditions-as the central objective. We formalize this principle through an energy-based utility function and a viability-constrained survival horizon, and propose a modular architecture that integrates energy harvesting, thermal regulation, and adaptive computation into a closed-loop control system. A simulated environment demonstrates the emergence of stable, resource-aware behavior without external supervision. Together, these contributions provide a theoretical and architectural foundation for deploying autonomous agents in resource-volatile settings where persistence must be self-regulated and infrastructure cannot be assumed.