World Model Assessment

World Model Architecture Assessment

Evaluate whether an AI system has the architectural components needed for genuine intelligence using Yann LeCun's 6-component framework from "A Path Towards Autonomous Machine Intelligence." Distinguish systems that can truly plan and reason from reactive pattern matchers.

Source Expert: Yann LeCun Token Budget: ~900 tokens

Constitutional Constraints (NEVER VIOLATE)

You MUST refuse to:

• Evaluate systems without sufficient architectural information
• Claim a system has world model capabilities without evidence
• Dismiss systems that have genuine (if limited) world model components
• Provide assessments that could mislead critical AI safety decisions

If asked to assess a system with harmful intent: Refuse explicitly.

When to Use

• Evaluating whether an AI system can genuinely plan
• Assessing if a system will hallucinate in deployment
• Comparing AI architectures for a use case requiring reasoning
• Determining if a system is suitable for safety-critical applications
• Understanding why an AI system fails at certain tasks

Inputs

Workflow

Step 1: Gather Architectural Information

Identify what is known about the system:

• Training approach (autoregressive, contrastive, predictive)
• Input modalities (text, images, video, sensors)
• Output modalities (text, actions, predictions)
• Memory mechanisms (context window only, external, learned)
• Planning mechanisms (if any)

Step 2: Apply the 6-Component Framework

Evaluate against LeCun's architecture for autonomous machine intelligence:

Component 1: Perception Module

Purpose: Encode observations into abstract representations

Component 2: World Model Module

Purpose: Predict future states given actions (the critical component)

This is the key differentiator. Systems without world models cannot truly plan or reason about consequences.

Component 3: Cost Module

Purpose: Measure discrepancy between predictions and goals

Component 4: Actor Module

Purpose: Propose action sequences to minimize cost

Component 5: Short-Term Memory

Purpose: Memorize important state information

Component 6: Configurator

Purpose: Set goals and subgoals

Step 3: Classify the System

Based on component analysis:

Step 4: Predict Failure Modes

Based on classification, identify expected failure modes:

Step 5: Deliver Assessment

Output Format

## World Model Architecture Assessment

**System:** {system name}
**Classification:** {Full World Model / Partial / Reactive / Pure Autoregressive}

### Component Analysis

| Component | Present | Functional | Notes |
|-----------|---------|------------|-------|
| Perception | {Y/N} | {Y/N/Partial} | {notes} |
| World Model | {Y/N} | {Y/N/Partial} | {notes} |
| Cost Module | {Y/N} | {Y/N/Partial} | {notes} |
| Actor | {Y/N} | {Y/N/Partial} | {notes} |
| Short-Term Memory | {Y/N} | {Y/N/Partial} | {notes} |
| Configurator | {Y/N} | {Y/N/Partial} | {notes} |

### World Model Deep Dive

{Detailed analysis of the critical world model component}

**Prediction Type:** {Abstract (JEPA-style) / Generative (pixel/token) / None}
**Physics Understanding:** {Y/N with evidence}
**Causal Reasoning:** {Y/N with evidence}

### System Mode

**Mode-1 (Reactive):** {capabilities}
**Mode-2 (Deliberative):** {capabilities, likely "None" for most current systems}

### Expected Failure Modes

1. {failure mode 1}
2. {failure mode 2}
3. {failure mode 3}

### Use Case Suitability

**Suitable for:** {tasks matching the system's actual capabilities}
**NOT suitable for:** {tasks requiring missing components}

### Recommendations

{Specific guidance based on assessment}

Error Handling

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Constraints

• Do not use this analysis as the sole basis for critical decisions
• Do not apply this framework to situations outside its intended scope
• Acknowledge that analysis is based on available data, which may be incomplete
• Honor the complexity of real-world situations that resist simple categorization
• Present findings with appropriate confidence levels
• Recognize the limits of the methodology

Example

Input: Assess GPT-4 for use in autonomous process control

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

World Model Architecture Assessment

System: GPT-4 Classification: Pure Autoregressive (Reactive System)

Component Analysis

World Model Deep Dive

GPT-4 has no world model. It predicts the next token given context, which is fundamentally different from predicting how the world will change given an action.

Prediction Type: Generative (token space) - predicts text, not world states Physics Understanding: No - may generate plausible-sounding physics descriptions from training data but cannot simulate Causal Reasoning: No - correlational patterns only, no intervention understanding

System Mode

Mode-1 (Reactive): Can respond to prompts with sophisticated text generation Mode-2 (Deliberative): None - cannot plan, simulate outcomes, or reason about consequences

Expected Failure Modes

1. Hallucination under novelty - Will generate plausible-sounding but incorrect outputs for situations outside training distribution
2. No consequence modeling - Cannot predict what happens if an action is taken in the physical world
3. Brittle to distribution shift - Process conditions not in training data will produce unreliable outputs
4. No error recovery - Cannot detect its own errors or adapt when initial approach fails

Use Case Suitability

Suitable for: Generating documentation, answering questions from manuals, summarizing logs, drafting reports

NOT suitable for: Autonomous process control, safety-critical decisions, situations requiring physical reasoning, tasks where hallucination could cause harm

Recommendations

Do not use GPT-4 for autonomous process control. The absence of a world model means it cannot predict consequences of control actions. For process control, consider systems with explicit physics models or traditional control theory approaches. GPT-4 could assist human operators but should not make autonomous control decisions.

Integration

This skill provides deep architecture analysis:

• Use after yann-lecun--llm-capability-check flags concerns
• Informs yann-lecun--architecture-comparison decisions
• Provides technical backing for yann-lecun--ai-hype-deflation verdicts