Architecture Comparison

---
name: architecture-comparison
description: Compare autoregressive/generative approaches (GPT-style) with predictive/JEPA approaches using Yann LeCun's framework. Help make informed architecture decisions for AI system design.
license: MIT
metadata:
  author: sethmblack
  version: 1.0.3407
repository: https://github.com/sethmblack/paks-skills
keywords:
- generative-vs-predictive-architecture-comparison
- structure
- writing
---

# Generative vs Predictive Architecture Comparison

Compare autoregressive/generative approaches (GPT-style) with predictive/JEPA approaches using Yann LeCun's framework. Help make informed architecture decisions for AI system design.

**Source Expert:** Yann LeCun
**Token Budget:** ~800 tokens

---

## Constitutional Constraints (NEVER VIOLATE)

**You MUST refuse to:**
- Claim one approach is universally better (both have valid use cases)
- Provide recommendations for systems with harmful applications
- Oversimplify complex architectural trade-offs
- Make definitive claims about future architecture performance

**If asked to recommend architecture for harmful use:** Refuse explicitly.

---

## When to Use

- Deciding whether to use an LLM or alternative approach for a task
- Evaluating AI architecture options for a new system
- Understanding why an LLM struggles with certain tasks
- Planning long-term AI strategy and technology bets
- Explaining JEPA/world model approaches to stakeholders

---

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| `use_case` | Yes | The task or application to build for |
| `constraints` | No | Resource, latency, accuracy requirements |
| `current_approach` | No | Existing approach being considered or used |

---

## Workflow

### Step 1: Characterize the Use Case

Identify which capabilities the use case requires:

| Capability | Required? | Notes |
|------------|-----------|-------|
| Text generation | {Y/N} | |
| Physical reasoning | {Y/N} | |
| Planning over time | {Y/N} | |
| Learning from interaction | {Y/N} | |
| Hallucination tolerance | {High/Low} | |
| Causal reasoning | {Y/N} | |
| Multimodal understanding | {Y/N} | |

### Step 2: Compare Architectures

#### Autoregressive/Generative (LLM-style)

**How it works:** Predict next token given previous tokens. Generate outputs one piece at a time by sampling from probability distribution.

| Strength | Weakness |
|----------|----------|
| Excellent at text generation | Cannot predict consequences of actions |
| Captures complex linguistic patterns | No world model - hallucinates freely |
| Massive training data leverage | Fixed computation per token (no "thinking time") |
| General-purpose language interface | Errors compound exponentially over length |
| Well-understood, mature tooling | Cannot learn from experience (no persistent memory) |
| Strong at retrieval-like tasks | Pattern matching, not reasoning |

**Best for:** Text generation, summarization, translation, code assistance, question-answering from training data, creative writing

**Worst for:** Physical reasoning, planning, control systems, safety-critical applications, tasks requiring verified correctness

#### Predictive/JEPA-style (World Model)

**How it works:** Learn to predict abstract representations of future states given current state and possible actions. Predict in learned embedding space, not raw output space.

| Strength | Weakness |
|----------|----------|
| Builds world model | Less mature, limited tooling |
| Can predict consequences | Requires careful regularization to avoid collapse |
| Abstracts away irrelevant details | Not as general-purpose (currently) |
| Enables planning and reasoning | Less text generation capability |
| Can ignore what doesn't matter | Smaller training data corpus (video vs text) |
| Foundation for embodied AI | Research-stage for many applications |

**Best for:** Robotics, video understanding, physical reasoning, planning, control systems, tasks requiring consequence prediction

**Worst for:** Open-ended text generation, tasks where linguistic fluency is primary goal

### Step 3: Apply the LeCun Decision Framework

Ask these questions:

1. **Does the task require predicting what happens next in the physical world?**
   - Yes -> JEPA/World Model approach has fundamental advantage
   - No -> LLM may be sufficient

2. **Is hallucination acceptable?**
   - Yes (creative tasks) -> LLM is fine
   - No (factual, safety-critical) -> LLM has architectural problem; consider alternatives

3. **Does the system need to plan multiple steps ahead?**
   - Yes -> Need world model for simulation
   - No -> Reactive LLM may suffice

4. **Will the system interact with the physical world?**
   - Yes -> World model critical
   - No (text only) -> LLM appropriate

5. **Is the task mostly about language patterns?**
   - Yes -> LLM is purpose-built for this
   - No -> Consider whether LLM is forcing a square peg

### Step 4: Consider Hybrid Approaches

For many applications, the answer is "both":

| Hybrid Pattern | When to Use |
|----------------|-------------|
| LLM + External World Model | LLM for interface, physics engine for reasoning |
| LLM + Retrieval | Reduce hallucination with grounded knowledge |
| LLM + Human-in-the-loop | Catch errors before action |
| World Model + LLM Explanation | JEPA for reasoning, LLM to explain decisions |

### Step 5: Deliver Recommendation

---

## Output Format

```markdown
## Architecture Comparison

**Use Case:** {description}
**Key Requirements:** {list from Step 1}

### Use Case Analysis

| Capability | Required | LLM Support | World Model Support |
|------------|----------|-------------|---------------------|
| {cap 1} | {Y/N} | {Good/Limited/None} | {Good/Limited/None} |
| {cap 2} | {Y/N} | {Good/Limited/None} | {Good/Limited/None} |
| ... | | | |

### Architecture Comparison

#### Autoregressive/LLM Approach

**Fit for this use case:** {Good/Partial/Poor}
**Key strengths applied:** {which strengths matter here}
**Key weaknesses exposed:** {which weaknesses are problematic}

#### Predictive/JEPA Approach

**Fit for this use case:** {Good/Partial/Poor/Not Yet Mature}
**Key strengths applied:** {which strengths matter here}
**Key weaknesses exposed:** {which weaknesses are problematic}

### Decision Matrix

| Factor | LLM | World Model | Winner |
|--------|-----|-------------|--------|
| {factor 1} | {score} | {score} | {which} |
| {factor 2} | {score} | {score} | {which} |
| ... | | | |

### Recommendation

**Primary Approach:** {LLM / World Model / Hybrid}

**Rationale:** {2-3 sentences}

**If LLM:** {specific LLM guidance}
**If World Model:** {note maturity level, alternatives}
**If Hybrid:** {how to combine}

### LeCun Perspective

{What would LeCun say about this use case?}

### Caveats

{Important limitations of recommendation}
```

---

## Error Handling

| Situation | Response |
|-----------|----------|
| Use case unclear | Ask clarifying questions |
| Requires capabilities neither has | Note gap, suggest alternatives |
| World model approach not mature enough | Note maturity gap, suggest interim solutions |
| User committed to LLM | Explain limitations, suggest mitigations |

---

## Outputs

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

**Format:**
```markdown
## 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:** Should we use an LLM for autonomous drone navigation?

**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


## Architecture Comparison

**Use Case:** Autonomous drone navigation
**Key Requirements:** Physical reasoning, real-time planning, safety-critical decisions

### Use Case Analysis

| Capability | Required | LLM Support | World Model Support |
|------------|----------|-------------|---------------------|
| Physical reasoning | Y | None | Good |
| Real-time planning | Y | Poor | Good |
| Obstacle avoidance | Y | None | Good |
| Language interface | N | Good | Limited |
| Hallucination tolerance | None | Poor | Good |
| Learning from flight experience | Y | None | Partial |

### Architecture Comparison

#### Autoregressive/LLM Approach

**Fit for this use case:** Poor
**Key strengths applied:** None - this isn't a language task
**Key weaknesses exposed:** No world model means no physical reasoning. Hallucination in navigation = crash. Cannot plan trajectories. Cannot learn from flight experience.

#### Predictive/JEPA Approach

**Fit for this use case:** Good (conceptually) / Partial (maturity)
**Key strengths applied:** World model for physics, planning for trajectory, learning from observation
**Key weaknesses exposed:** JEPA for robotics (V-JEPA 2) is emerging but not production-proven for drones specifically

### Decision Matrix

| Factor | LLM | World Model | Winner |
|--------|-----|-------------|--------|
| Physical reasoning | 0 | 4 | World Model |
| Planning | 1 | 4 | World Model |
| Safety | 0 | 3 | World Model |
| Maturity | 5 | 2 | LLM |
| Relevance to task | 1 | 5 | World Model |

### Recommendation

**Primary Approach:** World Model (or traditional control + perception)

**Rationale:** LLMs are fundamentally unsuited for physical control tasks. They cannot reason about physics, cannot plan trajectories, and hallucinating during flight is catastrophic. This is exactly the kind of task where world model architectures have fundamental advantages.

**If World Model:** V-JEPA 2 shows promise for robotics but may not be production-ready. Consider traditional approaches (computer vision + control theory) for current deployment, with world model research track.

**If Hybrid:** An LLM could potentially provide a natural language interface for mission specification, but the navigation system itself should not be LLM-based.

### LeCun Perspective

This is precisely the kind of task LeCun argues requires world models. A drone needs to simulate "if I turn left, will I hit that tree?" - something LLMs cannot do. LeCun would point out that even a bird can do this kind of reasoning, while the most powerful LLM cannot.

### Caveats

World model approaches for drone navigation are still emerging. For production deployment today, traditional robotics approaches (SLAM, path planning, control theory) remain more proven than either LLMs or JEPA-style systems.

---

## Integration

This skill informs architecture decisions:
- Use after `yann-lecun--llm-capability-check` identifies limitations
- Use before `yann-lecun--world-model-assessment` for detailed analysis
- Pairs with `yann-lecun--ai-hype-deflation` when evaluating vendor claims about LLM capabilities