Structured development cycles
Completed prototype and test cycles, including falsification and refinement. Not independent external benchmarks.
Every consequential decision changes the conditions for the next. CAPS addresses the problem of maintaining operational orientation as intelligent systems act, encounter feedback, and transform their own circumstances.
As an AI system acts, earlier decisions change what it knows, what it can do, what it is authorized to do, and what matters next. A decision that was justified yesterday may no longer be justified after the system changes its environment.
More context, better retrieval, and stronger individual reasoning can help. They do not, by themselves, settle how changing circumstances alter the significance and authority of earlier events.
The work behind CAPS began long before AI became a household word. After decades of exploring how complex systems change and develop, a focused research effort began in early 2025. In 2026, that work led to a discovery about how living systems maintain continuity as circumstances change. Working with AI, the idea was turned into an architecture for AI systems and tested through a structured engineering program. Rather than treating each new step as a fresh answer to the current prompt, CAPS addresses whether a proposed continuation remains coherent and admissible in light of the evolving history.
“Memory tells an agent what happened. CAPS governs what may validly continue because it happened.”
This is a public functional description, not a disclosure of the protected implementation or a claim that a biological mechanism has been independently established.
Three different forms of evidence. Kept deliberately distinct.
Completed prototype and test cycles, including falsification and refinement. Not independent external benchmarks.
Automated tests passed in the frozen Continuity Evaluator v0.4 record. Engineering conformance, not proof of superiority.
CAPS scored higher than BASE across six declared Passage Governance criteria in a preserved controlled same-model comparison.
Illustrative areas for technical integration—not claims of completed field deployments.
Maintain coherent objectives and decision authority across extended tasks, interruption, and recovery.
Keep shared operational context and accountability meaningful as multiple agents change the task environment.
Reconsider plans as actions alter physical conditions, constraints, and available choices at different system levels.
Carry forward the significance of observations, failed hypotheses, and revised interpretations across research cycles.
Govern long-running workflows when exceptions, approvals, and previous interventions change what is permissible.
Explore accountable reconsideration when new evidence changes the relevance of earlier decisions.
Public updates from the CAPS project. Publisher-controlled; no public posting access.
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We welcome serious technical and strategic discussions with organizations equipped to evaluate, integrate, and develop continuity governance.
Tell us about your organization, the AI systems you are developing, and the continuity challenges that matter to you. Please do not submit trade secrets or confidential implementation details through this initial form.
An inquiry is not a bid and does not activate an auction. Further technical disclosure is subject to appropriate arrangements.
Occasional CAPS research, engineering and licensing updates. Separate from company inquiries.