Embodied Ecological Sensing via Denoising Thermodynamic Models (DTMs)
Published: February 1, 2026Version: 2
Abstract
This note proposes a fundamental reconceptualization of ecological monitoring: the transition from systems that represent landscape state to systems that embody it. Drawing on recent advances in thermodynamic computing, specifically the Denoising Thermodynamic Model (DTM) framework developed by Extropic Corp., we outline an architecture for integrating probabilistic hardware into the Macroscope ecological observatory. The approach encodes multi-year ecosystem patterns not as stored baselines but as topological structures within energy landscapes. Incoming sensor data acts as physical bias on a Boltzmann machine mesh, where deviations from normal state manifest as mathematical tension rather than calculated anomalies. This framework offers three novel capabilities: temporal topology that embeds multiple timescales in mesh architecture rather than data summaries; absence detection through relational structure that makes missing elements create positive signal; and cross-domain resonance where couplings across EARTH, LIFE, HOME, and SELF domains emerge from learned topology rather than specified algorithms. The 10,000-fold energy efficiency advantage of thermodynamic hardware over GPUs enables field deployment at unprecedented scale. We propose the Canemah Nature Laboratory, with its 12-year observational archive and continuous sensor streams, as an ideal testbed for this embodied sensing paradigm.
@techreport{cnl2026embodied,
author = {},
title = {Embodied Ecological Sensing via Denoising Thermodynamic Models (DTMs)},
institution = {Canemah Nature Laboratory},
year = {2026},
number = {CNL-TN-2026-014},
month = {february},
url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-014},
abstract = {This note proposes a fundamental reconceptualization of ecological monitoring: the transition from systems that represent landscape state to systems that embody it. Drawing on recent advances in thermodynamic computing, specifically the Denoising Thermodynamic Model (DTM) framework developed by Extropic Corp., we outline an architecture for integrating probabilistic hardware into the Macroscope ecological observatory. The approach encodes multi-year ecosystem patterns not as stored baselines but as topological structures within energy landscapes. Incoming sensor data acts as physical bias on a Boltzmann machine mesh, where deviations from normal state manifest as mathematical tension rather than calculated anomalies. This framework offers three novel capabilities: temporal topology that embeds multiple timescales in mesh architecture rather than data summaries; absence detection through relational structure that makes missing elements create positive signal; and cross-domain resonance where couplings across EARTH, LIFE, HOME, and SELF domains emerge from learned topology rather than specified algorithms. The 10,000-fold energy efficiency advantage of thermodynamic hardware over GPUs enables field deployment at unprecedented scale. We propose the Canemah Nature Laboratory, with its 12-year observational archive and continuous sensor streams, as an ideal testbed for this embodied sensing paradigm.}
}