Field-Theoretic Memory Systems for AI Agents
Memory systems modeled as continuous fields enabling smooth interpolation, natural decay, and principled attention over long contexts.
Research from Rota, Inc.
Trust Intelligence Research
We develop the science and tools for trust decisions—whether verifying an AI agent's output, approving a financial transaction, or validating a network configuration.
4+
Research Papers
10
Open Source Projects
96%
Sandbagging Detection
Featured Research
Verity: Verified Synthesis via Confidence-Error-Effort-Progress
Framework for verified code generation using four measurable signals that predict correctness without ground truth.
Research Tracks
| Track | Focus | Status |
|---|---|---|
| Field-Theoretic Memory | Continuous field memory for AI agents | Complete |
| Verified Synthesis | CE2P hypothesis for code verification | Complete |
| Red Queen | Evolutionary adversarial testing | Complete |
| Trust Cascade Theory | Formal ROI-based decision routing | Complete |
| Sparse Trust Circuits | Mechanistic interpretability for trust | Active |
| Graph Trust Propagation | GNNs for entity trust networks | Active |
Open Source
rotalabs-cascade
Domain-agnostic trust cascade routing. Routes decisions to the cheapest sufficient layer.
pip install rotalabs-cascaderotalabs-probe
Sandbagging detection via metacognitive probes. 90-96% accuracy.
pip install rotalabs-proberotalabs-steer
Runtime behavior control with steering vectors.
pip install rotalabs-steerAbout
Rotalabs is the research division of Rota, Inc., a trust intelligence company. We publish open research and open-source tools for trust decisions in AI systems and regulated industries.