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RAPA (Regime-Adaptive Pair-Gated Agent)

The Regime-Adaptive Pair-Gated Agent (RAPA) represents the engineering implementation of the Dimensional Emergence Framework. Unlike traditional monolithic neural networks, RAPA is designed to be biomimetic, mirroring the functional organization and regime-switching logic observed in biological intelligence.

The RAPA architecture is governed by three primary structural constraints:

  • Regime-Adaptivity: The agent activates only the necessary dimensional capacity for a given task, conserving resources and maintaining focus.
  • Pairwise Bias: Information is organized into four streams that reflect the complementary pairings of DEF, preventing the unintended entanglement of distinct operational modes.
  • Deconstruction & Consolidation: High-dimensional semantic data is periodically projected back into more stable, lower-dimensional belief and intent states to prevent context drift.

In its fully manifest R4 configuration, RAPA operates through four distinct pair-streams

StreamDEF PairingFunctional Role
Stream AStructure ↔ SpaceWorld State: Reconstructs object identities and spatial topography.
Stream BDynamics ↔ ExchangeInteraction: Models transitions, causality, and the “physics” of interaction.
Stream CValence ↔ ReferenceAgent State: Manages preferences, goals, and intentional directedness.
Stream DMeaning ↔ NarrativeCognitive State: Handles semantic networks, instruction encoding, and long-term coherence.

A central router dynamically adjusts the agent’s complexity by gating these streams based on the current task requirements:

  • R2 Regime (Physicality): Activates only Streams A and B. The agent operates as a purely reactive system, focusing on “world physics” and basic movement.
  • R3 Regime (Sentience): Activates Streams A, B, and C. The agent gains intentionality and evaluates outcomes based on internal preferences and goals.
  • R4 Regime (Consciousness): Activates all four streams. This enables complex language processing, autobiographical continuity, and reflexive meta-cognition.

To ensure long-term stability, RAPA employs a deconstruction process that “folds” high-dimensional information back into the core modules:

  • Narrative Compression (D → C): Narrative episodes are compressed into stable updates for the agent’s long-term intent and valence structure.
  • Preference Integration (C → B): Long-term preferences are embedded as behavioral priors or biases within the dynamics model.
  • Reset Mechanism: Transitive parts of the Narrative (D) and Agent (C) streams are reset after consolidation to clear the “working memory” and prevent cumulative errors.

RAPA serves as a blueprint for AI that is inherently grounded. By enforcing pairwise coupling—where, for example, language (Stream D) cannot bypass the agent’s intent (Stream C) to directly trigger action (Stream B)—we create a system that is fundamentally more interpretable and aligned with structural reality.