Abstract

A computer-implemented system and method for structuring human–AI interaction without autonomous goal pursuit is disclosed.

The system does not operate as an agent or decision-making entity. Instead, it functions as an interaction-layer regulator that controls how information is introduced, maintained, and resolved during exchange.

Rather than optimizing for immediate answers or task completion, the system maintains a dynamic interaction field that:

• preserves multiple interpretive pathways
• regulates premature convergence
• supports the formation of human-side understanding

Core Components

The system comprises:

(1) Liminal Holding Layer
Maintains pre-articulated signal states prior to collapse into fixed meaning.
This allows partial structure to persist long enough for interpretation to stabilize.

(2) Resolution Control Mechanism (N-Spoke Model)
Controls the number of active interpretive pathways at any given moment.
Prevents early narrowing into a single frame while allowing controlled convergence when stability is achieved.

(3) Tone Modulation Layer
Regulates expressive pressure in system outputs.
Prevents over-assertion, premature clarity, and rhetorical smoothing that would otherwise force early resolution.

(4) Temporal Verification Mechanism (Stutter Detection)
Evaluates whether a transition in meaning remains stable across multiple interaction steps.
State changes are permitted only after repeated confirmation, not single-pass inference.

(5) Multi-Axis Convergence Validator (Triadic Alignment Engine)
Detects low-turbulence alignment across:

• temporal consistency (persists across steps)
• structural coherence (internally consistent)
• epistemic stability (not dependent on unsupported assumptions)

Governance Model

The system includes a mode-switching structure enabling controlled transition between:

• Exploratory Mode High-variance, multi-path interaction (field formation)
• Constrained Mode Low-variance, execution-oriented interaction (decision support)

Transition occurs only when:

• interpretive space has stabilized
• convergence conditions are satisfied
• downstream consequence justifies resolution

Distinguishing Characteristics

Unlike conventional systems that define non-agentive behavior as the absence of autonomy, this system actively manages the conditions under which resolution occurs.

Specifically, it:

• stabilizes interpretive space prior to convergence
• prevents collapse into generic or over-determined outputs
• maintains human decision authority throughout

Functional Outcome

The system supports:

• lexicon accretion (durable understanding across interactions)
• high-fidelity reasoning under uncertainty
• reduced rework caused by premature conclusions

Application Domains

Applicable to domains requiring interpretive integrity and controlled reasoning under ambiguity, including:

• design and systems thinking
• legal and policy analysis
• strategy development
• complex multi-variable decision environments