01
What is true?
CPS maintains authoritative state independently from generated descriptions of that state. A model saying that something happened is not enough to make it canonical.
Infrastructure for Persistent Context and Consequence.
01 / CONTINUITY
Context should not disappear when an interaction ends.
Most generative AI interactions are temporary.
A model receives context, interprets it, produces an output, and the interaction moves on. Long-term continuity is often reconstructed later from messages, summaries or another prompt.
CPS treats continuity as structured system state.
Information can retain its source. Knowledge can remain local. Decisions can survive the interaction that created them. Intended actions can remain distinct from completed ones. Events can change what becomes possible later.
The result is context that has history, ownership and consequence.
02 / DISTINCTIONS
Four things CPS keeps distinct.
02
Who knows it?
Information does not automatically become globally available. Different participants can hold different knowledge, beliefs or memories, with provenance describing where that information came from.
03
What is intended?
Interpretation, decisions, plans and obligations can persist as structured state without being confused with completed events.
04
What actually happened?
Execution creates evidence. Actions can complete, fail, remain pending or never occur. Later behavior can therefore depend on outcomes rather than generated claims.
03 / CAUSAL LOOP
Context moves. State changes. Consequences accumulate.
- 01Event / State
- 02Accessible Context
- 03Interpretation / Relevance
- 04Decision / Response
- 05Execution
- 06Canonical Outcome
- 07Changed Context
NO EFFECTNO ACTIONDEFERFAIL
CPS preserves the boundaries that determine whether one state legitimately becomes another.
04 / PROPAGATION
Information can move without becoming global truth.
Context in CPS can propagate between participants while retaining structure.
Someone receiving information does not necessarily acquire the original event as personal experience or objective truth. Source, provenance and epistemic position can remain distinguishable.
Whether that information travels further depends on later circumstances.
It may become relevant, remain private, be contradicted, influence a decision or never matter again.
Propagation is possible. It is not automatic.
05 / AUTHORITY
Language models interpret. CPS preserves authority.
Language models can interpret context, propose decisions and generate language.
CPS maintains the structured authorities around those outputs.
A generated statement can be distinguished from a decision. A decision from an obligation. An obligation from an attempt. An attempt from a completed event.
This prevents generated language from silently becoming system reality.
What a model says happened and what the system knows happened are not the same thing.
06 / CONSEQUENCE
Stored context is not enough.
Remembering something only matters when it can legitimately affect the future.
CPS connects retained state to later situations while preserving the evidence required to explain that connection.
A previous event may change what information is available, what someone believes, which relationships matter, which obligations remain, what actions are possible or how a later situation is interpreted.
Or it may have no effect at all.
Persistence matters when it changes what can happen next.
07 / OBSERVABILITY
CPS Studio is the proof surface.
CPS Studio exposes the state and lineage behind system behavior.
Instead of seeing only the final generated output, an operator can inspect what context existed, where it came from, what became relevant, what was decided, what persisted, what actually executed and what changed afterward.
This makes continuity testable rather than merely plausible.
What was known?
Inspect knowledge, source and provenance.
Why did this matter?
Trace the context that became relevant.
What was decided?
Separate interpretation and intention from execution.
What actually happened?
Inspect canonical outcomes and the evidence behind them.
08 / CAPABILITIES
One continuity layer. Different kinds of state.
World state
What exists and what has actually happened.
Knowledge
Who knows, believes, suspects or remembers what.
Decisions
What has been chosen, rejected or deferred.
Obligations
What remains to be done beyond the current interaction.
Execution
What was attempted, completed or failed.
Relationships
Persistent state between participants that can matter later.
Memory
Retained experience and information with provenance.
Lineage
Evidence connecting later consequences to earlier causes.
09 / EXPLORATION
Where this becomes useful is still being explored.
CPS is being developed as general continuity infrastructure rather than for one fixed application.
Current exploration includes persistent artificial worlds, multi-participant simulations, long-running agent systems and environments where provenance, decisions and consequences must survive individual model interactions.
The architecture is intentionally separated from any single interface, model provider or rendering engine.
10 / WHY CPS
Generation is easy to fake. Continuity is harder.
A language model can produce a convincing explanation of a past, a relationship, an intention or an action.
The difficult part is maintaining the structured history that makes those claims legitimate.
CPS focuses on that layer: preserving enough authority, provenance and causal state that the future does not need to be invented from scratch every time a model is called.
11 / FAQ + PROJECT STATUS