Embodied Ecological Sensing via Denoising Thermodynamic Models (DTMs)

Document ID: CNL-TN-2026-014
Version: 2.0
Date: February 1, 2026
Status: Draft
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)

From Representation to Thermodynamic Embodiment

AI Assistance Disclosure: This technical note was developed with assistance from Claude (Anthropic, Opus 4.5). The AI contributed to literature synthesis, conceptual framework development, and manuscript drafting through extended dialogue. The author takes full responsibility for the content, accuracy, and conclusions.

1. 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.

2. Introduction

2.1 The Epistemological Shift

Every ecological monitoring system built to date operates on a paradigm of representation. Sensors measure environmental variables. Data accumulates in databases. Algorithms summarize patterns and detect anomalies. The system knows about the landscape the way a filing cabinet knows about its contents: through stored records that can be retrieved, compared, and analyzed.

This note proposes something different: systems that embody landscape state rather than represent it. The distinction is not merely semantic. A system that embodies its domain does not store a baseline and compare current measurements against it. Instead, the normal state of the ecosystem exists as the shape of an energy landscape, a topological structure that the system settles into naturally. When sensor streams flow in, they act as physical biases. The system either relaxes into equilibrium or it does not. Deviation manifests as tension, not as a calculated metric.

Consider what an experienced field ecologist actually does when stepping outside at dawn. They do not consult a mental database of expected species, cross-reference against seasonal norms, and calculate deviation scores. They feel whether the morning is right. Decades of accumulated experience have shaped their perceptual apparatus so that wrongness registers as unease before it registers as thought. This is embodied knowledge: the landscape exists within the observer as much as the observer exists within the landscape.

2.2 Enabling Technology

Recent advances in thermodynamic computing provide the technical substrate for embodied sensing systems. The Denoising Thermodynamic Model (DTM) framework, published in arXiv:2510.23972 by Extropic Corp., demonstrates that probabilistic computers built from specialized stochastic circuits can achieve performance parity with GPUs on generative modeling tasks while consuming approximately 10,000 times less energy per sample [1]. This efficiency advantage derives from a fundamental insight: rather than calculating randomness through millions of logic-gate operations, thermodynamic hardware harvests noise from natural thermal fluctuations in subthreshold transistors.

The DTM framework solves a long-standing problem in Energy-Based Models (EBMs): the mixing-expressivity tradeoff. Monolithic EBMs fail at scale because success creates failure. As the model learns to fit complex data distributions, its energy landscape becomes increasingly rugged, with deep valleys separated by tall barriers. Sampling algorithms get trapped; the better the model, the harder to use it. DTMs sidestep this by chaining multiple simple EBMs, each performing a denoising step. Individual layers remain smooth enough to sample efficiently while the chain as a whole builds arbitrary complexity.

3. Conceptual Framework

3.1 From Representation to Embodiment

Traditional monitoring systems operate through a cycle of measurement, storage, retrieval, and comparison. A temperature sensor records 54°F. The value enters a database. An algorithm retrieves historical February mornings, computes statistics, and determines whether today falls within normal bounds. Knowledge exists in the explicit comparison between current measurement and stored baseline.

An embodied system operates differently. The normal February morning does not exist as stored data points but as the shape of an energy valley within a Boltzmann machine mesh. The mesh has been trained on years of co-occurring sensor streams until typical patterns correspond to low-energy configurations that the system settles into naturally. When current sensor values flow in as biasing currents, they do not get compared to anything. They push the mesh toward or away from equilibrium. If the morning is normal, the system relaxes. If something is wrong, the system experiences tension, a high-energy state where probabilistic bits struggle to find stable configurations.

This is not a metaphor. The mathematics of Boltzmann machines directly implements energy minimization. A trained mesh literally feels deviations as mathematical tension. The question shifts from "how does current state compare to baseline?" to "how does the landscape feel today?"

3.2 Temporal Topology

Previous work on the Macroscope has explored temporal compression through "time crystal" architectures that encode multi-year data into tractable representations. The embodied framework suggests a different approach: embedding temporal structure in the architecture of the mesh itself rather than in data summaries.

The DTM framework chains multiple EBMs to build complexity through sequential denoising. This suggests a mesh architecture where different layers encode different temporal scales. The deepest layer might encode 30-year climate normals: the slow background against which everything else unfolds. A middle layer encodes decadal trends, capturing phenomena like the current La Niña pattern or long-term shifts in species composition. A shallow layer encodes seasonal rhythms, the expected progression from winter dormancy through spring emergence. The surface layer encodes daily cycles and momentary state.

In this architecture, the present moment exists as a trajectory through all temporal contexts simultaneously. The mesh does not report "current temperature is 54°F" but rather expresses "this February morning, in this La Niña year, in this warming decade, feels like this." Anomaly detection becomes temporal context-sensitivity. A temperature that falls within daily bounds might create tension against decadal expectations or vice versa.

3.3 Absence as Signal

Traditional sensors detect presence. Microphones record sounds that occur. Cameras capture subjects that appear. Temperature probes measure values that exist. But ecological meaning often lives in holes: the species missing from an expected community, the dawn chorus that did not happen, the silence following a predator.

Boltzmann machines naturally encode relational structure. Training on years of co-occurring observations teaches the mesh what belongs together. When an expected element fails to appear, its absence creates real tension. The mesh expects Spotted Towhee vocalizations on February mornings. When a Cooper's Hawk triggers defensive silence, the missing towhee calls are not simply "not detected." They pull the mesh away from its learned equilibrium. The silence has positive weight.

This capability extends to phenological monitoring. A camera trained on years of springtime imagery learns when flowers should appear. Before explicit bloom detection triggers, the mesh might feel tension from absence: the visual patterns that should have appeared but have not yet. Absence detection through relational structure provides early warning that algorithmic threshold-crossing cannot match.

3.4 Cross-Domain Resonance

The Macroscope paradigm spans four domains: EARTH (geography, climate, environment), LIFE (biodiversity, taxonomy, ecology), HOME (human built habitat), and SELF (personal health, activity, cognition). The persistent dream has been integration: understanding how atmospheric rivers affect bird behavior affect sleep quality affect cognitive performance. But traditional integration requires specifying relationships algorithmically. Someone must hypothesize connections and code detection rules.

A mesh trained on years of co-occurring cross-domain data discovers couplings rather than implementing them. If barometric pressure drops consistently precede headaches by two hours, that relationship gets etched into the topology. If ecosystem stress and personal stress echo each other in ways not consciously noticed, the mesh learns the resonance. Cross-domain connections emerge from training rather than being specified in advance.

This suggests a monitoring system that operates more like a living organism than a measurement apparatus. The human body does not run separate subsystems for digestion, circulation, and respiration with explicit inter-system protocols. Everything couples through shared medium. An embodied sensing mesh achieves similar integration: EARTH and LIFE and HOME and SELF as facets of a single energy landscape rather than federated databases requiring explicit joins.

4. Technical Architecture

4.1 The Denoising Thermodynamic Model

The DTM framework [1] chains Energy-Based Models to implement denoising diffusion at the hardware level. Each layer in the chain models a conditional distribution of the form:

P(x_{t-1}|xt) ∝ exp(-(E^f{t-1}(x_{t-1}, xt) + E^θ{t-1}(x{t-1}, z{t-1}, θ)))

The forward energy E^f constrains the denoised output to stay close to the noisy input. The learned energy E^θ shapes local structure through latent variables z. Critically, increasing the number of denoising steps T simultaneously increases the expressive power of the chain and makes each step easier to sample, entirely bypassing the mixing-expressivity tradeoff.

4.2 Hardware Efficiency

The energy advantage of thermodynamic hardware derives from using shot noise in subthreshold transistors as the source of randomness. Where a GPU must calculate pseudo-random numbers through millions of logic operations, thermodynamic circuits harvest natural thermal fluctuations at approximately 350 attojoules per bit. System-level analysis indicates energy consumption of approximately 10⁻⁸ Joules per sample versus approximately 1 Joule for an NVIDIA A100 generating equivalent-quality images [1].

For ecological monitoring, this efficiency advantage enables deployment scenarios impossible with conventional hardware. Solar-powered field sensors could run thermodynamic mesh inference continuously. Networks of hundreds of low-power nodes could maintain embodied models of landscape state across watershed scales. The energy budget shifts from "how much computation can we afford?" to "how much sensing resolution do we want?"

4.3 The THRML Library

Extropic provides THRML, an open-source JAX library for simulating thermodynamic hardware [2]. The library enables development of DTM algorithms on conventional hardware (including Apple Silicon unified memory) that will transfer to dedicated Thermodynamic Sampling Units (TSUs) when available. Key capabilities include blocked Gibbs sampling for probabilistic graphical models, arbitrary PyTree node states, and heterogeneous graph support.

For Macroscope development, THRML running on an M4 Max MacBook Pro can simulate meshes of 50,000+ nodes with reasonable iteration times. This enables prototyping of embodied sensing algorithms before dedicated hardware becomes available, ensuring that conceptual development proceeds in parallel with hardware maturation.

4.4 Implementation Pipeline

The proposed architecture follows a Hybrid Thermodynamic-Deterministic Machine Learning (HTDML) flow combining probabilistic and conventional components:

1. Intake: Python scripts query MySQL databases containing time-series bioacoustic, environmental, and phenological data from the Macroscope sensor network.

2. Encoding: Continuous sensor values are embedded into binary representations suitable for Boltzmann machine processing, potentially using learned embeddings that preserve relevant structure.

3. Thermodynamic Core: The encoded data biases a trained mesh implemented via THRML. The mesh settles (or fails to settle) toward equilibrium, with global energy, mixing speed, and node tension providing summary metrics.

4. Interpretation: High-level metrics pass to a Large Language Model for natural language synthesis, translating mesh state into human-readable landscape assessments.

5. The Canemah Testbed

5.1 Unique Assets

The Canemah Nature Laboratory offers an unusually rich testbed for embodied sensing development. Unlike purpose-built sensor networks that begin with hardware deployment, Canemah brings 12 years of accumulated observational data:

• iNaturalist archive: 1,338 geotagged observations spanning 731 species across 13 taxonomic groups, providing phenological ground truth across multiple annual cycles.

• BirdWeather acoustic monitoring: Continuous bioacoustic surveillance capturing species presence/absence, vocalization patterns, and community dynamics.

• Tempest weather station: High-resolution environmental time series including temperature, humidity, pressure, wind, solar radiation, UV index, and precipitation.

• Phenology cameras: Visual documentation of seasonal progression across multiple fixed viewpoints.

• Place knowledge: 36 years of professional ecological observation by the principal investigator, providing interpretive context that no sensor network captures.

5.2 Proof-of-Concept Scenarios

Initial development should focus on scenarios where embodied sensing offers clear advantages over representational approaches:

The Predator Silence Scenario: When a Cooper's Hawk enters the property, resident passerines enter acoustic suppression. Traditional monitoring detects hawk presence through direct vocalization or visual identification. Embodied monitoring feels the silence as tension: the expected Spotted Towhee and Song Sparrow activity fails to materialize, creating positive signal from absence against the learned equilibrium of normal February morning soundscape.

The Phenological Anticipation Scenario: Trained on years of springtime imagery, the mesh develops expectations for bloom timing. Before explicit flower detection triggers, the system might feel mounting tension from absence: the visual patterns that should have appeared but have not yet. This provides early warning of phenological delay or advancement relative to learned multi-year norms.

The Cross-Domain Echo Scenario: Training on co-occurring streams across EARTH, LIFE, and SELF domains enables discovery of unlabeled correlations. Barometric pressure, bird activity, and human cognitive performance may couple in ways not specified algorithmically. The mesh learns these resonances and reports them as unified landscape-observer state rather than separate domain metrics.

6. Research Directions

6.1 Encoding Challenges

The DTM paper demonstrates binary Boltzmann machines operating on binarized Fashion-MNIST images. Ecological sensor streams present richer encoding challenges: continuous temperature values, spectral audio representations, high-dimensional image features. The integer-to-binary embedding described in the paper's Appendix I provides one approach, representing k-state categorical variables as sums of k binary variables. Learned embeddings that preserve ecological structure may prove more powerful.

6.2 Mesh Architecture

The temporal topology proposal requires investigation of multi-layer mesh architectures where different strata encode different timescales. Questions include: How should layers couple? Should temporal scale correlate with mesh depth? How does information flow between fast surface dynamics and slow deep structure? The DTM framework's chained-EBM approach provides a starting point, but ecological applications may require novel architectures.

6.3 Training Methodology

The Adaptive Correlation Penalty (ACP) described in the DTM paper maintains trainability by penalizing models that mix poorly. Ecological training data presents additional challenges: irregular sampling, missing observations, multi-year timescales, and distributional shift as climate changes. Training methodologies must handle these realities while producing meshes that encode meaningful ecological equilibria.

6.4 Interpretation Interface

Mesh state requires translation into human-interpretable form. The proof-of-concept narrative in the original version of this note imagined an LLM reporting "the bioacoustic sub-mesh feels a high-tension state" following hawk detection. Developing this interpretation layer requires understanding which mesh metrics carry ecological meaning, how to communicate uncertainty and confidence, and how to present cross-domain couplings without overwhelming users.

7. Conclusion

The transition from representational to embodied monitoring constitutes a genuine paradigm shift in ecological observation. Traditional systems know about landscapes through stored measurements and computed comparisons. Embodied systems know landscapes the way experienced naturalists know them: as felt texture, as tension and ease, as rightness or wrongness that registers before explicit analysis.

Thermodynamic computing provides the enabling substrate. The 10,000-fold energy efficiency advantage makes field deployment feasible. The DTM framework solves the mixing-expressivity tradeoff that has limited probabilistic hardware. The THRML library enables prototyping on conventional hardware today. What remains is the hard work of developing ecologically meaningful mesh architectures, encoding schemes, training methodologies, and interpretation interfaces.

The Canemah Nature Laboratory offers an ideal testbed: 12 years of observational data, continuous sensor streams, and the interpretive context of deep place knowledge. Extropic has the hardware vision; Macroscope has the data and ecological intuition to make it mean something. The question is no longer whether embodied ecological sensing is possible, but what it will feel like when the landscape speaks for itself.

8. References

[1] Jelinčič, A., Lockwood, O., Garlapati, A., Schillinger, P., Chuang, I. L., Verdon, G., & McCourt, T. (2025). "An efficient probabilistic hardware architecture for diffusion-like models." arXiv:2510.23972.

[2] Extropic Corp. (2025). "THRML: Thermodynamic Hypergraphical Model Library." https://github.com/extropic-ai/thrml

[3] Extropic Corp. (2025). "Thermodynamic Computing: From Zero to One." https://extropic.ai/writing/thermodynamic-computing-from-zero-to-one

[4] Hamilton, M. P. (2025). "Canemah Nature Laboratory Technical Note Style Guide." CNL-SG-2025-002. https://canemah.org/archive/document.php?id=CNL-SG-2025-002

9. Document History