Research
Architecting cognition
Our research focuses on the systems-level question: how do you build AI that learns continuously, reasons reliably, and augments human capability in complex domains?
Research principles
Architecture over scale
Intelligence emerges from structure. We invest in the right abstractions, not just more compute.
Deployment as research
Real-world feedback drives our research agenda. Every deployment teaches us something models alone cannot.
Measure what matters
We optimize for R&D velocity—time to answer, problems tractable, consistency under complexity—not benchmark scores.
Focus areas
Our work spans memory, reasoning, and learning—the core components we believe are necessary for systems that genuinely augment human intelligence.
Hierarchical memory architectures that mirror human cognition—working memory for immediate context, episodic memory for experiences, semantic memory for consolidated knowledge.
Publications
LEAP: Learning through Episodic Accumulation and Processing
Internal
Request accessSurprise-Gated Memory Consolidation in Neural Networks
Internal
Request accessMulti-step reasoning through hypothesis generation, refinement, and verification. DAG-based execution with full trace logging for interpretability.
Publications
Monad: A Traceable Runtime for Agent Orchestration
In Production (Beta)
Request accessIterative Refinement in Multi-Document Reasoning
Internal
Request accessSystems that improve from deployment feedback without catastrophic forgetting. Meta-gating mechanisms that balance stability and plasticity.
Publications
Engram-VQ: Fast/Slow Memory with Meta-Gating
In Production (Beta)
Request accessDiffusion-Based Reasoning at Small Scale
Research Preview
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Collaborate with us
We're always looking for collaborators—whether you're a researcher, practitioner, or organization working on related problems.