Qriton HLMBuild controllable AI systems on Hopfield memory
For early adopters, data scientists, academic labs, startups, and enterprise teams developing the next generation of inspectable, editable, and auditable AI.
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Built for technical early adopters
The portal is built for teams evaluating HLM as a real platform: read the docs, run demos, inspect checkpoints, test Energy Language workflows, and decide where controllable AI fits your roadmap.
Data science & researchModel references, validation notes, tutorials, and theory for labs and technical teams studying energy-based AI.
Startup evaluationRunnable demos, edge model artifacts, and workflow examples for teams prototyping new AI products.
Enterprise pilotsAuditability, controllability, deployment notes, and release status for high-stakes AI evaluation.
Start from your evaluation path
The portal is organized around practical adoption paths. Pick the track closest to your work, then move from concept to demo to model artifact without changing context.
For buildersSet up the toolkit, load a checkpoint, and run the first inspection/surgery path.For data teamsCompare HLM3, vision, audio, spatial, micro, and nano families by workload and constraint.For controllabilityUse Energy Language for inspection, basin edits, guards, causal checks, and scripted changes.For pilotsTry diagnostics, document audit, model lab, enterprise memory, and edge deployment flows.For decision makersReview validation results, checkpoints, known limits, and what is still experimental.For academiaStudy why attractor basins can be inspected, edited, replayed, and tested.
Developer docs and demos require approved early access. The public home page and request form stay open.
One Memory Core. Multiple Frontends.
HLM keeps modality-specific frontends at the edge of the system, then routes representations into a shared polynomial Hopfield core. The practical goal is controllability: model states you can inspect, uncertainty you can measure, basins you can edit, and decisions you can replay.
For data scientists and engineering teams, this means the same inspection surface can show up across language, image, audio, 3D, and sensor workloads. For researchers, it exposes a concrete substrate for studying memory, convergence, and interventions. For startups and enterprise pilots, it creates a path toward AI behavior that can be tested and governed.
The docs focus on those operational surfaces: model loading, inspection, basin surgery, validation, deployment patterns, and audit records.
Change Behavior with Commands
Training is still the tool for broad capability. Energy Language is the control surface for localized changes: capture a concept, inject or move a basin, apply the edit, and test the behavior.
The important property is that the change is explicit. A lab can study it, a data team can measure it, a startup can prototype with it, and an enterprise team can review, revert, and attach it to an audit trail.
Energy Language "Energy minima as a programming language in a completely new fashion." John J. Hopfield, March 2026, on programming energy landscapes directly
From experiment to control surface
Start interactively, then promote stable operations into scripts. The same primitives cover landscape inspection, basin modification, semantic concepts, causal checks, and guards.
The portal includes command references and walkthroughs for testing the result after each operation, from research notebooks to pilot workflows.
Request access to the docs ->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 Concept added as a new basin (+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...
Access flow
1. RequestUse the early-access form and include your role, domain, workload, integration target, or model tier you want to evaluate.
2. ReviewRequests are reviewed manually. Researchers, early adopters, startup builders, and enterprise pilot teams are prioritized.
3. InvitationIf approved, you receive a personal access link. No password flow; the link signs the browser into the portal.
4. ExploreOpen docs, demos, tutorials, model cards, and release artifacts from the same member portal.
Most requests are reviewed within a few business days. Declined requests stay on file for later phases.
Model Artifacts and Release Notes
The Model Zoo is the place to check what is available, what changed, and where the limits are. Early-access members get the first validated checkpoints and the notes needed for research, product evaluation, and pilot planning.
HLM-Micro & HLM-Nano
Edge / MCU - shipping now
Compact tiers for edge sensing and on-device inference. Current releases include efficiency-tuned checkpoints with lower iteration counts and reduced weight footprint.
HLM3
Language
Language models focused on inspectable decision paths, uncertainty gating, and replayable audit records.
HLM-Vision & HLM-Spatial
Vision / 3D
Image and spatial frontends feeding the shared Hopfield core, with the same inspection and audit surfaces.
HLM-Audio
Audio
Audio-stream frontends for monitoring and diagnostics where each decision needs supporting trace data.
Early-access members are notified by email as each checkpoint becomes available. See the Model Zoo for what is released today.
Build the Next Generation of Controllable AI
If you are an early adopter, data scientist, academic researcher, startup founder, or enterprise team evaluating controllable AI, request access and describe the technical path you want to explore.
Approved members get the documentation, demos, model cards, release notes, and Energy Language references needed for serious evaluation.