Monolithic models cost $10K-$1M to add a new domain. CLMs do it for $0.50. A small base coordinates with routed NCN Module specialists through a capsule lattice — add capability by composition, not retraining. The wrapper works on any open transformer (validated on Mnema 360M and Llama 3.2 1B). Try it live below.
Most modern AI systems are monolithic — one giant model that tries to do everything. To add a new capability, you retrain or RLHF the whole model at a cost of tens of thousands of dollars per domain.
Mnema introduces a new architecture category: the Compositional
Language Model (CLM). A small shared base coordinates with many
domain specialists — proprietary NCN Modules
(.ncn files) trained on specific topics like appliance
diagnostics, code, or robotic navigation. At inference
time, a capsule lattice uses cosine retrieval over
learned capsule embeddings to pick the right specialist for each query —
no learned-gating router required, fully interpretable, training-free
to extend.
Each new specialist costs about $0.50 of compute and 30 minutes on a consumer GPU. Adding a domain doesn't retrain the base — it just adds another small file to the lattice.
Runs on Jetson Orin Nano, Raspberry Pi, and consumer laptops. 720 MB base + 5-10 MB per domain specialist. No cloud dependency.
Routes per query to the right specialist via coordinate-based capsule retrieval. Every routing decision is visible and inspectable.
Adding a new domain costs ~$0.50 and 30 minutes of consumer-GPU time. Compare to $10K+ for fine-tuning a commercial 7B+ model.
The live demo above is real. Below: how it works, recent benchmark runs, and the architecture under the hood.
Demo video
60-second screen capture coming this weekend
10 prompts. Router selects between two specialists. 9/10 correct routing decisions. The one miss is documented honestly.
Demonstrates a 1.67× out-of-distribution advantage on retrieved-locus queries through forced-anchor diffusion decoding. Published peer-citable result.
The 46-page mathematical framework specifying Mnema's 8 architectural modules: base transformer, block diffusion scheduler, anchor head, NCN router, fold memory lattice, compositional stack layers, retrieval bridge, calibration wrapper.
Internal technical specification — public excerpts available on request.
A novel methodology to distinguish "the model can't do this task" from "the model found a shortcut" by comparing causal and bidirectional attention paths on identical inputs. Useful diagnostic for any mask-trained language model.
Paper in preparation — workshop submission planned.
| Capability | Commercial LLM APIs (OpenAI, Anthropic, Mercury) |
Local open models (Llama, Mistral) |
Mnema |
|---|---|---|---|
| Add new domain capability | Re-RLHF: $10K+, days | Full fine-tune: hours of GPU | One NCN Module: $0.50, 30 min |
| Wrap a different base model | N/A — closed | N/A — single-base | Yes — validated on Llama 3.2 1B at 80% N=2 |
| Edge / on-device deployment | No — cloud only | Yes — but generic | Yes — with composition |
| Per-query specialist selection | No | No | Yes — routing visible |
| Inspectability / audit trail | Black box | Black box | Cosine scores per query |
| Add 50 specialists | Impossible | 50× model size | +250 MB total |
Independent AI research lab focused on vertical AI: specialized models for specific industries, deployed on the hardware closest to the user.
Mnema powers our own SaaS products including DiagBuddy, a diagnostic AI tool for appliance repair shops. The proprietary diagnostic data from those customers in turn trains the next generation of Mnema specialists — a virtuous cycle of vertical capability building.
We're raising seed capital to scale specialist training and complete the edge + robotics deployment.
invest@sophiaxt.com →University labs and independent researchers — we welcome collaboration on the NCN-Fold framework + composition operator paper.
research@sophiaxt.com →Want a vertical AI built on your data, deployed to your hardware? We do consulting engagements.
demo@sophiaxt.com →