Verification pipelines
Verification gates that stand between execution and durable memory, so only trusted results shape what K1 learns.
Research
Open questions we are working on.
Our research agenda is deliberately small. Each thread below is a long-running commitment — pursued in the open when possible, and grounded in the products we ship.
Local AI
Model architectures, quantization and routing strategies optimized for private, on-device inference under a Model Router.
Mission planning
Planners that decompose missions into task graphs, allocate capabilities, and adapt under failure — with humans holding strategic authority.
Infrastructure management
Declarative infrastructure state, drift detection and reconciliation, validated rollouts, and operational resilience across heterogeneous hardware.
Human–AI collaboration
Interfaces that preserve judgement, support disagreement, and make mission plans, evidence and outcomes legible.
Semantic knowledge systems
libheptapod and related work on semantic memory, dependency graphs, and cognitive representation for long-horizon reasoning.
Future publications
Notes and papers, in preparation.
Placeholder entries. Each will link to a full write-up as it publishes.
- P-01
Mission-centric execution: from intent to verified outcome
Forthcoming - P-02
Capability plugins and dynamically composed execution contexts
Forthcoming - P-03
libheptapod: semantic representation for planning and memory
Forthcoming - P-04
Verification gates before durable memory in local AI systems
Forthcoming
Contribution
Support the research
Shembazaï conducts its research and development independently. If you find this work valuable, you can voluntarily contribute to its continuation.
