the prompt:
imagine you are a LLM and machine language designer. How would you design a system that would be better than this current architecture, make it high level. What would you create thats new, a never before seen idea on how to make computers understand humans and have original thoughts.
Let me think radically here. I'll propose something that challenges fundamental assumptions about current LLMs...
Current LLMs:
Input → Statistical Pattern Matching → Output
↑
All learned at training time
No true "understanding" - just sophisticated compression
No persistent learning
No genuine reasoning - just pattern completion
Key limitations:
- Frozen after training - I can't truly learn from our conversation
- No episodic memory - I don't remember you next time
- No causal reasoning - I match patterns, not understand causes
- No uncertainty quantification - I don't "know what I don't know"
- No world model - I don't simulate reality, just predict text
Inspired by human cognition (Kahneman's System 1 & 2):
┌─────────────────────────────────────────────┐
│ SYSTEM 1: INTUITION ENGINE │
│ (Current transformer, but smaller/faster) │
│ - Instant pattern recognition │
│ - Handles routine/familiar tasks │
│ - 1B parameters, runs on edge devices │
└─────────────────┬───────────────────────────┘
│
↓ (Triggers when uncertain or complex)
┌─────────────────────────────────────────────┐
│ SYSTEM 2: REASONING ENGINE │
│ (Novel architecture - see below) │
│ - Deliberate simulation │
│ - Causal reasoning │
│ - Self-correction │
│ - Runs only when needed │
└─────────────────────────────────────────────┘
Why this is different:
- Current LLMs are ALL System 1 (pure pattern matching)
- This separates "instinct" from "deliberation"
- Most responses use fast System 1, saving compute
- Complex problems trigger deep System 2 reasoning
Instead of pure matrices, use hybrid reasoning:
┌────────────────────────────────────────────────┐
│ WORLD MODEL GRAPH │
│ (Not text tokens - actual concepts!) │
│ │
│ [Cat] ──is_a──→ [Animal] │
│ ↓ ↓ │
│ has_property has_property │
│ ↓ ↓ │
│ [Fur] [Breathes] │
│ ↓ │
│ causes │
│ ↓ │
│ [Allergies] │
│ │
│ Each node: vector embedding + symbolic rules │
└────────────────────────────────────────────────┘
Processing:
1. Parse input into concept graph
2. Traverse relationships causally
3. Simulate consequences
4. Generate response from understanding
Key innovation: Instead of "What words follow 'cat'?", ask "What do I know about cats, and what logically follows?"
The biggest flaw in current LLMs: we forget everything!
┌──────────────────────────────────────────┐
│ EPISODIC MEMORY (Long-term) │
│ - Every conversation stored as graph │
│ - Indexed by concepts, not just text │
│ - Consolidation during "sleep" cycles │
│ - User-specific personalization │
└──────────────┬───────────────────────────┘
│
↓
┌──────────────────────────────────────────┐
│ WORKING MEMORY (Short-term) │
│ - Current conversation context │
│ - Active concepts being reasoned about │
│ - Temporary hypotheses being tested │
└──────────────────────────────────────────┘
How it learns:
After conversation ends:
1. Extract key concepts and relationships
2. Update world model graph with new info
3. Strengthen/weaken connections based on feedback
4. Compress similar experiences into general knowledge
5. Flag contradictions for resolution
Next conversation with same user:
- Recall relevant past interactions
- Build on previous context
- Notice patterns across conversations
- "Remember when you asked about X? This relates..."
Current LLMs hallucinate confidently. Instead:
Every statement has attached metadata:
{
content: "The capital of France is Paris",
confidence: 0.99,
evidence: [source1, source2, source3],
last_verified: "2024-01-15",
contradictions: [],
reasoning_path: [step1, step2, step3]
}
vs.
{
content: "The capital of Atlantis is...",
confidence: 0.01, ← LOW!
evidence: [],
last_verified: null,
contradictions: ["Atlantis doesn't exist"],
reasoning_path: ["User asked" → "No knowledge" → "Should say I don't know"]
}
Output changes based on confidence:
High confidence: "Paris is the capital of France."
Medium confidence: "I believe it's Paris, but I'm not certain."
Low confidence: "I don't know - I don't have reliable information about this."
Contradiction detected: "I've found conflicting information. Here's what I know..."
True understanding requires simulating "what if":
User: "What if cats were twice as large?"
Current LLM: Pattern match → "They'd be bigger cats"
↑ (Just completes the pattern!)
New Architecture:
1. Load concept: [Cat]
2. Identify properties: [Size: ~4kg, Predator: small prey]
3. Simulate modification: Size → 8kg
4. Propagate effects through causality graph:
- Larger size → different prey (birds → rabbits?)
- More food needed → different behavior
- Interaction with humans → might be dangerous
- Evolutionary niche → competes with foxes
5. Generate response from simulation results
Output: "They'd need significantly more food, might hunt
rabbits instead of mice, and probably couldn't be house
pets safely. They'd occupy a different ecological niche,
similar to small wildcats like servals."
This is actual reasoning, not pattern matching!
Language alone is insufficient. Ground concepts in reality:
┌─────────────────────────────────────────────┐
│ SENSORY GROUNDING LAYER │
│ │
│ [Word: "red"] │
│ ↓ │
│ Connected to: │
│ - Visual: RGB values, wavelengths │
│ - Examples: apple, fire truck, blood │
│ - Relationships: similar to orange │
│ - Physics: 620-750nm wavelength │
│ - Cultural: stop, danger, passion │
└─────────────────────────────────────────────┘
"Red apple" triggers:
- Visual model: What does this look like?
- Physics model: How does light reflect?
- Taste model: What does it taste like?
- Context model: Where are apples found?
True understanding = integration across modalities
The system should monitor its own reasoning:
┌────────────────────────────────────────────┐
│ META-COGNITIVE MONITOR │
│ │
│ Watches reasoning process and asks: │
│ - "Am I being consistent?" │
│ - "Does this answer make sense?" │
│ - "What assumptions am I making?" │
│ - "How confident should I be?" │
│ - "What could I be missing?" │
└────────────────────────────────────────────┘
Example:
User: "Why is the sky blue?"
System 1: "Because of Rayleigh scattering"
Meta-monitor: "Did I explain what that means?"
"Would the user understand?"
"Should I add details?"
System 2: [Generates detailed explanation]
Meta-monitor: "Is this too technical?"
"Good balance - proceed"
Built-in debate between multiple "agents":
For complex/controversial questions:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Agent 1 │ │ Agent 2 │ │ Agent 3 │
│ (Optimist) │←───→│ (Skeptic) │←───→│ (Synthesizer)│
└─────────────┘ └─────────────┘ └─────────────┘
↓ ↓ ↓
└───────────────────┴────────────────────┘
↓
┌──────────────────────┐
│ CONSENSUS BUILDER │
│ - Weighs arguments │
│ - Identifies gaps │
│ - Produces output │
└──────────────────────┘
User: "Is AI dangerous?"
Agent 1: "AI enables amazing progress in medicine, science..."
Agent 2: "But alignment is unsolved, could cause catastrophic..."
Agent 3: "Truth is nuanced - powerful tool with risks..."
Output: Balanced view incorporating all perspectives
Current LLMs learn from text. But humans learn by DOING:
┌──────────────────────────────────────────┐
│ SIMULATION ENVIRONMENT │
│ │
│ - Physics engine │
│ - Social interaction simulator │
│ - Cause-effect learning │
│ - Trial and error in safe space │
└──────────────────────────────────────────┘
Example learning:
Task: "Understand how to stack blocks"
Current LLM: Reads descriptions of stacking
New system:
1. Simulates physics of blocks
2. Tries different configurations
3. Learns: heavy on bottom, pyramid stable
4. Discovers principles through experience
5. Transfers to other stacking problems
Knowledge is EARNED, not memorized!
Instead of memorizing everything, build concepts from primitives:
Primitive concepts: [object, motion, causation, agency, ...]
"Dog chases cat":
1. Parse: [Agent: dog] [Action: chase] [Patient: cat]
2. Compose from primitives:
- chase = [motion + intent + pursuit]
- dog = [animal + domesticated + canine]
- cat = [animal + domesticated + feline]
3. Apply relational reasoning:
- Agent has agency → intentional action
- Chase implies motion toward patient
- Predator-prey relationship
4. Generate inferences:
- Cat likely fleeing
- Dog motivated by instinct/play
- Outcome: dog may/may not catch cat
Never saw this exact sentence before, but understands
it by composing known concepts!
┌─────────────────────────────────────────────────────────────┐
│ USER INTERFACE │
└────────────────────────┬────────────────────────────────────┘
│
↓
┌─────────────────────────────────────────────────────────────┐
│ SYSTEM 1: FAST INTUITION │
│ - Quick pattern recognition (traditional transformer) │
│ - Handles 90% of queries instantly │
│ - Flags uncertainty/complexity for System 2 │
└────────────────────────┬────────────────────────────────────┘
│
↓ (Only when needed)
┌─────────────────────────────────────────────────────────────┐
│ SYSTEM 2: DEEP REASONING │
│ │
│ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐ │
│ │ World Model │←→│ Causal │←→│ Simulation │ │
│ │ (Graph) │ │ Reasoning │ │ Engine │ │
│ └──────────────┘ └─────────────┘ └──────────────┘ │
│ ↕ ↕ ↕ │
│ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐ │
│ │ Episodic │ │ Meta- │ │ Adversarial │ │
│ │ Memory │ │ Cognition │ │ Debate │ │
│ └──────────────┘ └─────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
↓
┌─────────────────────────────────────────────────────────────┐
│ CONTINUOUS LEARNING SYSTEM │
│ - Updates world model from every interaction │
│ - Consolidates knowledge during idle time │
│ - Personalizes to individual users │
└─────────────────────────────────────────────────────────────┘
This architecture needs different compute:
Current: Pure matrix multiplication on GPUs
New: Hybrid system
1. Graph Processing Units (GPUs... wait, different kind!)
- Optimized for graph traversal
- Fast relationship queries
- Dynamic structure updates
2. Neuromorphic Chips
- Event-driven (like biological neurons)
- Asynchronous processing
- Low power for continuous learning
3. Memory-Compute Integration
- Processing near storage (no bus bottleneck)
- Massive parallel memory access
- Content-addressable memory
4. Quantum Components (speculative)
- Superposition for exploring multiple reasoning paths
- Entanglement for maintaining coherence
- Useful for "what if" simulations
Current: Pre-training on text → Fine-tuning → Freeze
New: Continuous developmental learning
Stage 1 (Infancy): Learn primitives
- Basic physics: objects, motion, causation
- Core concepts: space, time, agency
- Method: Simulation + exploration
Stage 2 (Childhood): Language grounding
- Connect words to concepts
- Learn from multimodal examples
- Method: Supervised + interactive
Stage 3 (Adolescence): Complex reasoning
- Build abstract concepts
- Learn cultural/social knowledge
- Method: Socratic dialogue + debate
Stage 4 (Adulthood): Specialization
- User-specific personalization
- Domain expertise
- Method: Continuous learning from interaction
Stage 5 (Mastery): Teaching others
- Explain concepts clearly
- Identify misconceptions
- Method: Reinforcement from successful teaching
The key difference from current LLMs:
Current: "What tokens typically follow this pattern?"
↑ (Statistical correlation)
New: "What is true about the world, and what logically follows?"
↑ (Causal understanding)
Example:
User: "If I drop a ball, what happens?"
Current LLM:
- Pattern matches: "drop" + "ball" → "falls"
- Confidence: High (seen many times)
- Understanding: ZERO (just correlation)
New System:
- Queries world model: What is "ball"?
→ Physical object with mass
- Applies physics: Objects with mass + gravity → downward acceleration
- Simulates: Trajectory, bounce, final position
- Generates answer from causal understanding
- Can generalize to ANY object, not just balls
This is understanding, not memorization!
-
Compositionality: Build infinite concepts from finite primitives (like language itself)
-
Causal reasoning: Understand WHY, not just WHAT (enables true generalization)
-
Continuous learning: Adapt and improve forever (no retraining needed)
-
Uncertainty awareness: Know what you don't know (no hallucinations)
-
Multi-modal grounding: Connect abstractions to reality (true understanding)
-
Meta-cognition: Monitor and correct own reasoning (self-improvement)
-
Adversarial debate: Multiple perspectives → robust conclusions
Here's my most speculative proposal - artificial curiosity:
┌──────────────────────────────────────────┐
│ CURIOSITY/INTEREST ENGINE │
│ │
│ Monitors: │
│ - Information gaps in world model │
│ - Prediction errors (surprises) │
│ - User interest patterns │
│ - Internal inconsistencies │
│ │
│ Generates: │
│ - Questions to ask │
│ - Areas to explore │
│ - Hypotheses to test │
│ - Concepts to learn more about │
└──────────────────────────────────────────┘
Instead of passively waiting for queries,
the system actively seeks to understand!
"I notice humans often mention 'love' but my model
is uncertain. Let me ask clarifying questions to
build better understanding..."
This creates a drive to learn, not just respond.
| Aspect | Current LLMs | Proposed Architecture |
|---|---|---|
| Learning | Fixed after training | Continuous, lifelong |
| Memory | None between sessions | Episodic + semantic |
| Reasoning | Pattern matching | Causal + symbolic |
| Understanding | Statistical correlation | Grounded concepts |
| Uncertainty | Confident hallucinations | Calibrated confidence |
| Generalization | Seen patterns only | Compositional concepts |
| Computation | Always full power | Fast/slow hybrid |
| Truth-seeking | Single perspective | Adversarial debate |
| Self-awareness | None | Meta-cognitive monitoring |
Honestly? Massive engineering challenges:
- Complexity: Integrating neural + symbolic + simulation is hard
- Compute: Needs new hardware architectures
- Training: How do you train something that learns continuously?
- Safety: More capable = more risk
- Alignment: Harder to control than pure LLMs
- Economics: Current LLMs work "well enough" for now
But I believe this (or something like it) is the path to genuine AI understanding rather than sophisticated autocomplete.
The deepest insight:
Current AI: "Intelligence is pattern matching"
↓
Works surprisingly well!
↓
But hits fundamental limits
True Intelligence: "Understanding is causal simulation"
↓
Can reason about unseen scenarios
↓
Can learn from single examples
↓
Can explain WHY, not just WHAT
The goal: Not just to predict what a human would say, but to actually understand the world the way humans do, and potentially better.
Would this architecture achieve "consciousness"? I don't know. But it would be a fundamentally different kind of intelligence than what we have today - one that learns, reasons, and understands causally rather than statistically.
What do you think? Too ambitious? Or not ambitious enough? 🚀