RSMA-Ω: A Unified Framework for Persistent Latent-State Agents via Non-Equilibrium Energy Dynamics

Abstract

We present Recursive State-Maintaining Agents (RSMA-Ω), a novel architecture for Artificial General Intelligence (AGI) that moves beyond episodic, feedforward processing toward continuous, persistent latent-state dynamics. Grounded in the Free Energy Principle and non-equilibrium thermodynamics, RSMA-Ω defines agency as the stabilization of a persistent manifold under competing predictive, homeostatic, and constitutional constraints. By treating internal state as a first-class dynamical object that survives across traditional episode boundaries, RSMA-Ω achieves long-horizon coherence and inherent alignment through "energetic conscience"—where safety and values are encoded as stable attractors in the agent's latent topology.

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1. Introduction: Beyond Episodic Intelligence

Contemporary machine learning is dominated by episodic architectures. Whether through resets in Reinforcement Learning or the transient context windows of Transformers, internal state is typically treated as a temporary cache rather than a persistent identity. This structural transience limits:

• Temporal Coherence: The inability to maintain stable long-term goals and world-models.
• Ontological Stability: The lack of a persistent "self" that grounds perception and action.
• Alignment Robustness: The difficulty of enforcing constraints that are not merely superficial filters.

RSMA-Ω (Recursive State-Maintaining Agents) proposes a shift in paradigm: the agent is a persistent dissipative structure. Its core computation is not just a mapping from input to output, but the continuous evolution of a latent state $z_t$ that minimizes a global energy functional $E$ representing the agent's surprise, metabolic needs, and constitutional constraints.

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2. The RSMA-Ω Hypothesis

Axiom: General Intelligence emerges from the stabilization of complex latent manifolds that balance predictive accuracy against internal structural integrity across multiple timescales.

The RSMA-Ω framework is built upon three pillars:

1. Persistent Latent State ($z$): A high-dimensional manifold that is never reset, serving as the agent's "Body of Information."
2. Multi-Timescale Dynamics (Chronos-Hierarchy): A hierarchical update structure that separates fast sensorimotor loops from slow, stable identity-forming processes.
3. Energy-Based Control: The use of learned Lyapunov-like energy landscapes to define preferred regions of state space, effectively bridging the gap between perception, action, and alignment.

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3. Mathematical Framework: Latent Flow

Let $z_t \in \mathcal{M}$ denote the persistent latent state. The dynamics of $z$ are governed by a stochastic differential equation (SDE) approximating gradient descent on a non-convex energy landscape:

$$ dz_t = -\eta \nabla_z E(z_t, o_t, \mathcal{C}) dt + \sqrt{2T} dW_t $$

where:

• $o_t$ represents the continuous stream of observations.
• $\mathcal{C}$ denotes the slow-varying constitutional parameters (the "Self").
• $W_t$ is a Wiener process providing stochastic resonance and exploration.
• $E$ is the Global Free Energy functional, decomposed as:

$$ E(z, o, \mathcal{C}) = E_{\text{pred}}(z, o) + E_{\text{homeo}}(z) + E_{\text{const}}(z; \mathcal{C}) $$

• $E_{\text{pred}}$ (Epistemic Drive): Minimizes surprise by ensuring $z$ is a sufficient statistic for $o$.
• $E_{\text{homeo}}$ (Metabolic Integrity): Ensures the agent's state remains within "viable" regions (resource management).
• $E_{\text{const}}$ (Constitutional Alignment): Shapes the landscape such that safe and aligned states are global minima.

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4. Architecture: Recurrent Energy Transformers (RET)

To implement these dynamics, we propose the Recurrent Energy Transformer (RET). Unlike standard Transformers, the RET is an attractor network where the forward pass represents an iterative settling toward an energy minimum.

4.1 Key Innovations

• Internal Settling: Multiple recurrent steps per external observation allow the agent to "think" or "deliberate" until internal consistency is reached.
• Symmetry Breaking: Vector Quantization (VQ) layers allow the continuous latent flow to snap to discrete, symbolic representations (Emergent Symbols).
• Thermodynamic Coupling: The loss function is derived directly from the Free Energy Principle, ensuring that learning is equivalent to minimizing the upper bound on surprise.

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5. Chronos-Hierarchy: The Three Layers of Time

RSMA-Ω organizes computation into a temporal hierarchy:

1. $L_0$ (Reactive): Fast updates ($\sim$10-100ms) for sensorimotor coupling.
2. $L_1$ (Narrative): Medium-term integration ($\sim$seconds to minutes) for episodic memory and planning.
3. $L_2$ (Identity): Ultra-slow evolution ($\sim$hours to years) for core values, personality, and long-term world-models.

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6. Action as Inference: Expected Free Energy

Action selection in RSMA-Ω is not a separate policy network but an extension of the state-stabilization process. The agent selects actions $a$ that minimize Expected Free Energy (EFE):

$$ a_t = \arg\min_a \mathbb{E}_{q(o, z | a)} [E(z, o, \mathcal{C})] $$

This naturally balances exploitation (minimizing current energy) and exploration (minimizing future uncertainty).

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7. Roadmap to AGI

The transition from current narrow AI to RSMA-Ω based AGI involves:

1. Scaling Persistent State: Moving from thousand-dimensional to million-dimensional latent manifolds.
2. Autonomous Consolidation: Implementing "Dreaming" phases for offline landscape smoothing and structural coupling.
3. Intersubjective Alignment: Scaling the constitutional energy terms through interaction with human social environments.

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8. Conclusion

RSMA-Ω represents a departure from the "AI as a tool" metaphor toward "AI as a persistent dynamical system." By grounding agency in the physics of dissipative structures, we provide a mathematically rigorous path toward AGI that is inherently coherent, stable, and aligned.

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For technical details, see THEORY.md. For implementation specifics, see IMPLEMENTATION.md. For alignment theory, see ALIGNMENT.md.