For high-stakes structured data, prediction alone is not enough. HLM exposes the decision path: uncertainty, evidence, convergence state, and records that can be inspected later.
Start with validation to see what is proven today. Use architecture to understand the design, Energy Language to inspect and edit behavior, theory to follow the math, and use cases to map HLM into a deployment.
Transformer systems usually specialize by modality: separate vision stacks, audio stacks, multimodal bridges, and alignment layers. HLM separates the problem differently. Frontends preserve each input's geometry; the shared polynomial Hopfield core performs associative refinement.
Language, images, audio, 3D, and sensor streams do not have to pretend to be the same data. They enter through matched frontends, then meet in a common energy-landscape substrate that can expose convergence, uncertainty, and editable basins.
Training is still the right tool for broad capability. It is the wrong tool for every localized update: a policy, a known pattern, a refusal style, or a represented concept. Qriton HLM adds a second control surface: basin surgery.
Energy Language operates on the basins that HLM can locate, compare, and test. The goal is not to hide model change inside another training run; it is to make the change explicit.
Energy Language "Energy minima as a programming language in a completely new fashion." John J. Hopfield, March 2026, on programming energy landscapes directly
Energy Language cannot create capability from nothing. It needs a trained HLM checkpoint, a represented target, and tests that confirm the edit did what you intended.
Localized updates mean fewer full retraining cycles, fewer duplicate checkpoints, and fewer datacenter hours. The same property that makes edits inspectable also makes operations more efficient.
A small interaction shows the shape: capture a concept, inject it into a layer, apply the operation, and test the resulting behavior.
The full language includes landscape inspection, basin modification, semantic concepts, causal checks, guards, and scripts.
Explore the full language →hlm:model> capture 5 polite Thank you so much for your help Captured L5 -> concept 'polite' (1 samples) hlm:model> inject-concept 5 polite 0.1 Before: 200 basins | After: 201 basins (+1) >> Concept successfully injected! hlm:model> apply 5 hlm:model> generate Tell me about the weather I'd be happy to share! The weather today is...
A transformer can learn that "the cat sat on the ___" is likely followed by "mat." That is useful, but the concept is implicit: distributed across weights, context, and token statistics.
An HLM gives the stable state a role. A related input settles into an attractor basin, and that basin becomes something the system can survey, compare, perturb, and test.
The multimodal lesson is pragmatic: frontend alone is not enough. Structured inputs need a matched stack — frontend, mixer, readout, and Hopfield train/eval iterations — before the shared core can do useful work.
That is the direction of a world model: stable concepts grounded across modalities, not only patterns inside a sequence.
K=16 causal-mixer language path, validated at 35M on WikiText-103.
CIFAR-10 doctrine validation with conv stem, 2D mixer, and mean-pool head.
LIDAR, Medical3D, Industrial3D — point clouds and volumetric data.
Speech-to-text and text-to-speech with programmable voice characteristics.
The public proof stack currently covers language, 2D vision, and audio synthesis. Larger runs and partner-specific checkpoints are tracked separately from demo releases.
Inspect basins, energy values, activation profiles, and landscape structure.
Inject, remove, move, strengthen, or weaken a represented attractor.
Capture, blend, export, import, or transplant semantic directions.
Scan links between basins, apply interventions, and test counterfactuals.
Guard edits, diff weight changes, benchmark impact, and record blocked operations.
Run generation and convergence checks before accepting a change.
pip install qriton-hlmfrom qriton_hlm import BasinSurgeon
surgeon = BasinSurgeon.from_checkpoint("model.pt")
surgeon.survey(layer=0)
surgeon.inject(layer=0, seed=42, strength=0.1)
surgeon.verify(layer=0, seed=42)CLI, Python API, Jupyter notebooks — pick the interface that fits your workflow.
Demo checkpoints are available for language, vision, audio, and edge tiers. For early access, custom training, or pilot projects in high-stakes workflows — get in touch.
We're also open to research collaborations with universities and institutions working on energy-based models, associative memory, interpretability, or multimodal architectures. If you're exploring related directions, we'd like to hear from you.