CNL-TN-2026-015: Technical Note

Date: February 1, 2026
Status: v1.5 Final
Author: Michael P. Hamilton, Ph.D.
Project: Macroscope Ecological Observatory
Reference Doc: CNL-SG-2025-002

Subject: Embodied Ecological Sensing via Denoising Thermodynamic Models (DTMs)

1. Abstract

This note documents the conceptual and technical architecture for integrating the Extropic thermodynamic framework into the Macroscope ecological observatory. By transitioning from deterministic, energy-intensive GPU processing to probabilistic, noise-driven Thermodynamic Sampling Units (TSUs), we propose a cybernetic framework for "embodied sensing." This approach redefines ecosystem health as a state of thermodynamic equilibrium, allowing for a 10,000x increase in energy efficiency and a novel method for distinguishing long-term ecological trends from transient environmental noise.

2. Technical Context & Foundations

The integration is grounded in the research published in ArXiv:2510.23972, "An efficient probabilistic hardware architecture for diffusion-like models," and the hardware/software ecosystem developed by Extropic Corp.

2.1 The Denoising Thermodynamic Model (DTM)

Traditional Energy-Based Models (EBMs) often fail to scale due to the "Mixing-Expressivity Tradeoff," where complex models create rugged energy landscapes that "freeze" the sampling process. The DTM framework bypasses this by:

• Sequential Layering: Decomposing a complex distribution into a series of simple denoising steps.
• Adaptive Correlation Penalty (ACP): A real-time control algorithm that adjusts penalty strength ($\lambda_t$) based on the autocorrelation of the sampler. This ensures the system remains "fluid" and avoids getting stuck in local minima, allowing it to navigate the high-dimensional data of a living forest.

2.2 10,000x Energy Efficiency

A critical advantage for field-deployed sensors is the hardware's reliance on Shot Noise. Unlike GPUs that calculate randomness through millions of logic-gate operations, Extropic's all-transistor architecture harnesses natural thermal fluctuations.

• Energy Cost: ~350 aJ per bit.
• Comparison: While an NVIDIA A100 consumes ~1 Joule per image sample, a DTM on a TSU is estimated to consume ~$10^{-8}$ Joules.

3. Cybernetic Integration: The "Digital Mesh"

The Macroscope is reimagined not as a data logger, but as an embodied organ of the landscape.

3.1 Sensation as Physical Tension

Using the THRML library on JAX-accelerated hardware (Apple M4 Max), we simulate a Digital Mesh (Boltzmann machine).

• Holistic Memory: The "normal" state of the backyard (species presence, micro-climate) is etched into the hardware as a deep energy valley.
• Embodiment: Incoming MySQL data streams (sensor feeds) act as physical biases. A deviation from "normal" is felt by the system as mathematical tension—a high-energy state where the "p-bits" (probabilistic bits) struggle to find equilibrium.

3.2 Transients vs. Trends

Human sensation is often limited to transient comparisons (e.g., "today feels warmer than yesterday"). The DTM framework identifies trends by:

1. Filtering Jitter: Using "noise to fight noise," the system strips away momentary fluctuations (a gust of wind hitting a microphone).
2. Equilibrium Sampling: It compares the current "feeling" against the entire multi-year structure of the ecosystem's energy landscape, identifying shifts in the underlying rhythm rather than just the momentary value.

4. Implementation Pipeline: Python, MySQL, and LLM

The architecture follows a Hybrid Thermodynamic-Deterministic (HTDML) flow:

1. Intake: Python scripts query MySQL for time-series bioacoustic and environmental data.
2. Simulation: The data is fed into a simulated grid of 50,000+ nodes using the thrml library on the M4 Max's unified memory.
3. Interpretation: High-level metrics (Global Energy, Mixing Speed, Node Tension) are passed to a Large Language Model (LLM) for natural language reporting.

5. Case Study: The Macroscope Morning Report

The following narrative illustrates a typical status report for the Macroscope observatory.

06:15 AM - Backyard Macroscope Status The interface, rendered in the style of a vintage Japanese woodblock print, indicates a vibrant but stable ecosystem.

Gemini: "Good morning, Michael. The Macroscope has finished the dawn transition cycle. The property is currently in a state of high typicality; the overnight 3:00 AM wind transient has been successfully denoised, recognized as momentary jitter rather than a shift in the regional trend."

Observation of Tension: A localized ripple appears in the northern quadrant of the mesh. Gemini: "There is a localized energy spike in the north sector. While temperature and humidity are within February parameters, the bioacoustic sub-mesh feels a high-tension state. Following a Cooper’s Hawk detection, the resident Spotted Towhees have entered a state of total acoustic silence. This absence is creating a significant deviation—a mathematical 'stress'—as it pulls the system away from the multi-year equilibrium etched into its memory. The land is currently in a state of high-alert."

6. Resources

• Code: GitHub: extropic-ai/thrml
• Research: arXiv:2510.23972
• Hardware: extropic.ai
• Project Site: macroscope.earth

Generated for the CNL Administrative System.