Abstract

This proposal presents a complete architecture toward Artificial General Intelligence (AGI). The architecture is built upon a core epistemological assumption: “The predictive power of the forward dynamics model is equivalent to the ability to understand the physical world.” Based on this, we construct a recursive evolutionary system where: 1) The base agent internalizes environmental regularities by training a high-fidelity forward model (i.e., world model); 2) Intrinsic motivation acts as the “cognitive gradient” of the world model, driving the agent to actively explore unknown domains to patch model deficiencies; 3) Recursive Meta-Architectural Search (RMAS) optimizes neural network topology through generational evolution to emerge stronger world simulation capabilities. This system aims to achieve a cognitive leap from “passive adaptation” to “active simulation and planning.”

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1. Theoretical Axiom: Forward Model Equivalence

Before defining the specific architecture, we first establish the core axiom of this system:

Axiom $H_{equiv}$: The Agent is the World Model

A complete agent must possess an internal function $f_{world}$ that can accurately predict the future state $s_{t+1}$ based on the current state $s_t$ and action $a_t$.

$$s_{t+1} \approx f_{world}(s_t, a_t; \theta)$$

• Corollary A (Simulation): If $f_{world}$ is sufficiently accurate, the agent can perform counterfactual reasoning in its “mind,” i.e., “simulation.”
• Corollary B (Understanding): The prediction error $\mathcal{L}_{pred} = \Vert f_{world}(s_t, a_t) - s_{t+1} \Vert$ is the only objective metric for measuring the agent’s “understanding.”

Understanding is compression. Prediction is decompression.

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2. Core Architecture Components

2.1 World Model

To avoid ineffective prediction in high-dimensional pixel space (addressing the “TV noise” problem raised by Yann LeCun), our world model is built upon latent space.

This module contains three coupled neural networks:

1. Feature Encoder (Inverse Model): Filters uncontrollable noise. By solving the inverse dynamics problem $\hat{a}_t = g(\phi(s_t), \phi(s_{t+1}))$, it extracts causally-relevant features $\phi(s)$. Tells the agent “what is worth paying attention to.”

2. Forward Dynamics Core: This is the world model itself.

$$\hat{\phi}_{t+1} = f_{forward}(\phi(s_t), a_t; \theta_{world})$$ Simulates physical laws, social rules, and logical causality.

3. Prediction Head: Maps latent states back to predicted outcomes (used for computing rewards).

2.2 Curiosity

In this architecture, curiosity is no longer just about “getting more points,” but about “patching the world model.”

• Mechanism: The agent actively seeks states where $f_{forward}$ prediction fails.
• Intrinsic Reward: $r_{intrinsic} \propto \Vert \hat{\phi}_{t+1} - \phi_{t+1} \Vert^2$.
• Philosophical Meaning: True learning only occurs when reality violates the model’s predictions (i.e., feeling “surprised”). This drive causes the agent to experiment like a scientist even without external tasks, to refine its internal physics model.

2.3 Memory

The world model needs to predict not only $t+1$ but also $t+N$. To support long-range dependencies:

• We introduce Memory $\mathcal{M}$.
• Memory matrix $\mathcal{M}$ stores the “long-term context” of the world model, transforming the forward model $f_{forward}$ into $s_{t+1} \approx f(s_t, a_t, \mathcal{M}_t)$, enabling it to handle non-Markovian environments (POMDPs).

2.4 Recursive Meta-Agent Search

Controlled by meta-agent $\Pi_\Phi$ (Architect).

• Objective: Not to directly maximize task rewards, but to maximize the sub-agent’s “world modeling efficiency” (i.e., achieving the lowest prediction error with the fewest samples).
• Evolution: The meta-agent continuously rewrites the base agent’s neural network architecture (e.g., number of layers, attention mechanism types) to evolve a “brain” that can more efficiently simulate complex physical laws.

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3. Algorithm Pseudocode

This pseudocode demonstrates how the relationship between all the compontents.

# Algorithm: RMAS with Generative World Models

def RMAS_World_Model_System():
    # Meta-Agent: The Architect of Intelligence
    Meta_Architect = Initialize_Policy()

    # --- OUTER LOOP: Evolution of Cognitive Architecture ---
    while not Singularity_Reached:
        
        # 1. Design Phase: Generate a Brain Structure optimized for World Modeling
        # Topology determines the capacity of the Forward Model
        Brain_Topology, Theta_init = Meta_Architect.generate_design()
        
        # 2. Simulation Phase: Evaluate Intelligence in Objective Reality
        fitness, prediction_fidelity = Simulate_Cognition(Brain_Topology, Theta_init)
        
        # 3. Meta-Update: Survival of the "Most Predictive"
        # We favor architectures that can better predict (understand) the world
        Meta_Architect.update_strategy(fitness, prediction_fidelity)
        
        # 4. Curriculum: Increase World Complexity
        if prediction_fidelity > High_Fidelity_Threshold:
            World.unlock_quantum_mechanics_level() # Example of complexity jump

def Simulate_Cognition(Topology, Theta):
    # Instantiate the agent
    Agent = Build_Agent(Topology, Theta)
    
    # [The Core]: The Forward Model IS the World Model
    # It predicts the future state features given current state and action
    World_Model = Instantiate_Forward_Model(Topology) 
    
    # [The Filter]: Inverse Model to ignore noise (LeCun's JEPA style)
    Feature_Encoder = Instantiate_Inverse_Model(Topology)
    
    Memory = Initialize_Memory()
    s_t = Environment.reset()

    total_reward = 0
    avg_prediction_error = 0

    while Agent.is_alive():
        
        # A. Perception & Context Retrieval
        context = Memory.read(s_t)
        
        # B. Imagination/Planning (Optional step for Model-Based RL)
        # Agent can use World_Model to simulate 5 steps into the future before acting
        # imagined_future = World_Model.rollout(s_t, context, candidate_actions)
        # action = choose_best(imagined_future)
        
        # For Model-Free + Curiosity (Simpler version):
        action = Agent.policy(s_t, context)
        
        # C. Interaction
        s_next, r_ext = Environment.step(action)
        
        # D. World Model Validation (The "Understanding" Step)
        # 1. Encode Reality: Get latent features of s_t and s_next
        phi_t = Feature_Encoder(s_t)
        phi_next = Feature_Encoder(s_next)
        
        # 2. Predict Reality: What did the World Model think would happen?
        phi_next_pred = World_Model.forward(phi_t, action, context)
        
        # 3. Measure Surprise (Prediction Error)
        # Low Error = Understanding; High Error = Ignorance (or Hallucination)
        surprise = MSE_Loss(phi_next_pred, phi_next)
        
        # E. Intrinsic Motivation Calculation
        # The agent gets a dopamine hit for being "surprised" (learning opportunity)
        # BUT the long-term goal is to minimize this surprise (mastery)
        r_int = Calculate_Curiosity(surprise)
        
        # F. Update Synapses (Learning)
        # 1. Improve Policy (to get more reward)
        Agent.update_policy(r_ext + Lambda * r_int)
        
        # 2. Improve World Model (to better understand physics)
        # This is Self-Supervised Learning: The world provides the labels
        World_Model.update(loss=surprise)
        Feature_Encoder.update(s_t, s_next, action)
        
        # Update state
        Memory.write(s_t, action, context)
        s_t = s_next
        total_reward += r_ext
        avg_prediction_error += surprise.item()

    # Return fitness AND how well the agent understood the world
    return total_reward, 1.0 / (avg_prediction_error + epsilon)

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4. Conclusion

This proposal presents a complete path toward AGI by combining four essential components: World Models (to understand causality), Curiosity (to drive exploration), Memory (to handle long-term context), and Meta-Agent Architecture (to recursively improve learning). When placed in a rich simulated environment, an agent with these capabilities can achieve human-level learning through experience alone.

However, before reaching the singularity, safety must come first. I think AI-Human Co Improve is more important.

The goal is not AI that replaces humanity, but AI that learns together with us, augmenting human capability while preserving safety and shared purpose.

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References

1. LeCun, Y. (2022). “A Path Towards Autonomous Machine Intelligence.” OpenReview, Version 0.9.2. https://openreview.net/forum?id=BZ5a1r-kVsf
   Yann LeCun’s Joint-Embedding Predictive Architecture (JEPA), proposing that world models should operate in abstract representation space to avoid predicting irrelevant details.

2. Richens, J., Abel, D., Bellot, A., & Everitt, T. (2025). “General Agents Contain World Models.” arXiv preprint arXiv:2506.01622. https://arxiv.org/abs/2506.01622
   Formally proves that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment, even if implicitly.

3. Sutton, R. S. (2025). “The Oak Architecture: A Vision of SuperIntelligence from Experience.” Reinforcement Learning Conference (RLC) 2025. https://www.youtube.com/watch?v=gEbbGyNkR2U
   Proposes the Oak architecture, a model-based RL system where all components learn continually, using the FC-STOMP progression: Feature Construction, SubTask, Option, Model, and Planning to build abstractions in state and time.

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