Learn From Use.Adapt Through Physics.
We build language models as thermodynamic fields. One equation governs learning, forgetting, and inference. No fine-tuning. No retrieval.
The Problem
Every PatchMakes It Worse
Today’s models freeze at deployment. To make them personal, you bolt things on.
Retrieval
Store everything the user’s ever done. Search it every call. The database grows. Latency grows. Cost grows.
Adapters
Train a small model per user. Now you’re running thousands of training jobs. Per day.
Fine-tuning
Retrain the base model on new data. Expensive. Slow. Stale by the time it ships.
Each layer adds cost that scales with users. At a million users, you’re running a million parallel systems pretending to be one.
Their system remembers the evidence. Ours becomes different because of it.
The Thesis
One Equation.Three Phenomena.
A ThermoField model is a thermodynamic energy landscape. Every interaction shapes the field. No optimizer. No gradient descent. Just physics.
Hysteresis = Learning
Interactions push the field into new states—the same mechanism that gives magnets memory. Use the model, and it learns.
Relaxation = Forgetting
Without reinforcement, states decay. Stale knowledge fades on its own. No deletion logic. Physics handles forgetting.
Spatial Locality = Personalization
Each user carves their own basin on the energy landscape. Millions of personalized states with zero interference.
Why Now
It Works.Today.
ThermoField runs on GPUs right now. Classical simulation of thermodynamic dynamics. It matches static baselines and personalizes without fine-tuning.
The Math Is Proven
Euler-Maruyama integration, Hopfield energy landscapes, underdamped Langevin dynamics. Not speculative. Physics.
The Hardware Is Coming
Analog substrates that perform stochastic relaxation natively are in active development. When they arrive, continual learning costs heat dissipation.
The Economics Flip
On classical hardware, continual learning is expensive. On thermodynamic hardware, it’s the default. Adaptation becomes cheaper than freezing.
Architecture
One Field.Many Basins.
A shared field holds common knowledge. Each user shapes a personal basin. Inference reads both. No per-user model. No per-user database. Just physics.
Foundation
Shared Thermodynamic Field
Foundation model
Adapt
Δ
User A Basin
Δ
User B Basin
Δ
User C Basin
Merge
Inference = Field + User Basin
Interaction ⇢ Hysteresis on Basin
Contact
Shape WhatComes Next
For researchers, engineers, and investors working on the next platform shift.