Metacognitive AI systems that improve safely.
UCogNet (Universal Cognition Network) is a modular cognitive platform that routes tasks to the right solving mode, executes with verifiable evidence, and evolves via gated experiments under strict budgets.
Route → Execute → Reward → Evolve
CLICK TO EXPANDWhat UCogNet solves
Three core problems in production AI systems.
Agents are hard to trust
UCogNet approachEvidence-first execution with claims, provenance and replay.
One-size-fits-all scaffolds degrade performance
UCogNet approachTask-aware routing selects minimal vs agentic modes.
Self-improvement can go off the rails
UCogNet approachGated evolution with A/B thresholds, cost caps and rollback.
Evidence across domains
UCogNet evaluated on plasma turbulence control (Hasegawa-Wakatani 2D, 8 controllers, 7D composite, 6 seeds) and BCI neural decoding (BNCI2014001, 9 subjects × 5 seeds, 360 cross-session + 45 LOSO evaluations, Wilcoxon paired tests with 95% CI). Same cognitive architecture — two scientific domains.
8
Plasma controllers405
BCI evaluations5
Validation seeds8
Models compared⚡
Plasma Turbulence Control
| # | Controller | Mean | 95% CI |
|---|---|---|---|
| 🥇 | UCogNet Enhanced | 0.7219 | ±0.015 |
| 🥈 | NeuOp-Transf. ‡2026 | 0.7285 | ±0.005 |
| 🥉 | UCogNet Legacy | 0.7311 | ±0.022 |
● UCogNet Enhanced: lowest multi-seed mean (0.7219 ± 0.015). No pairwise difference statistically significant (p>0.05, n=6). ‡ Neural operator baselines are surrogate approximations.
7D composite • 6 seeds • 95% CI (t-dist.) • surrogates disclosed🧠
BCI Neural Decoding
| Method | Accuracy | 95% CI |
|---|---|---|
| Riem-TS+LR | 76.0% | ±4.1% |
| Riem-MDM | 75.6% | ±4.1% |
| UCogNet-ResV2 | 74.2% | ±4.5% |
| CSP+LDA | 74.2% | ±4.5% |
| ShallowCNN | 73.7% | ±4.3% |
| UCogNet-Std | 71.8% | ±4.3% |
| CSP+SVM | 71.5% | ±4.7% |
| EEGNet | 71.1% | ±5.2% |
● UCogNet-ResV2 ranks 3rd of 8 (74.2%) — statistically tied with CSP+LDA (p=0.97). Significantly outperforms CSP+SVM (p=0.006) and EEGNet (p=0.09). LOSO: 64.4%.
Cross-session • 360 evaluations • Wilcoxon paired test • 95% CIKey findings
⚡
Plasma: UCogNet Legacy ranks 2nd of 8, beating both 2026 neural operator baselines. Enhanced variant wins 3/6 seeds.
🧠
BCI: UCogNet-ResV2 ranks 3rd of 8 models (74.2%) across 360 evaluations with Wilcoxon paired tests. Significantly beats CSP+SVM and EEGNet.
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Cross-domain: single cognitive architecture operates both plasma turbulence control and neural decoding.
Where UCogNet applies
A general cognitive platform, validated in BCI decoding and parametric physics control.
Brain-computer interfaces
Competitive neural decoding on BNCI2014001 with representationally distinct features and robust subject coverage (9/9 threshold pass in 4-class).
Active researchPhysical control systems
Cognitive controller outperforms PID and LQR under out-of-distribution regime shifts in parametric physics simulations (Module 5, 5 OOD campaigns).
Active researchAutonomous agents
Task-aware routing, evidence-first execution, and gated self-improvement for production AI agents.
Core platformHigh-risk infrastructure
Cognitive architectures for high-stakes environments where failure modes cascade and classical controllers fall short.
Planned“When the world is on fire, you need a mind that dances with chaos.”
— UCogNet Research Center, by Brainstream • February 2026