Instead of making larger LLMs, why not build a cognitive architecture around them?
I've been thinking about the future of AI, and instead of asking "How do we make a bigger LLM?", I started asking: What if the LLM is only one cognitive subsystem?
The idea is that today's LLM becomes the Language Cortex, responsible for:
Conversation
Reasoning
Creativity
Coding
Roleplay
Everything else would be handled by specialized systems surrounding it.
For example:
USER
│
▼
Language Cortex (LLM)
│
Working Memory (Context Window)
Long-Term Memory
Planning Engine
Reflection Engine
Metacognition Engine
World Model
Learning System
Emotion / Relationship Model
Self Model
Tool Layer
Sensory Cortex
Motor Cortex
Inference Engine
│
▼
Cognitive Companion
Here is my full architecture:
USER
│
▼
Language Cortex (LLM)
(Conversation • Reasoning • Creativity)
← This is today's Large Language Model →
│
════════════════════════════════════════════════════════════
Working Memory (Context Window)
(Temporary Memory During Current Conversation)
• Current dialogue
• Immediate reasoning
• Active tasks
• Cleared when the context resets
────────────────────────────────────────────────────────────
Long-Term Memory
(Persistent Memory Across Sessions)
• Facts
• Experiences
• Relationships
• Preferences
• Skills
• Personal history
────────────────────────────────────────────────────────────
Planning Engine
(Goals • Decision Making • Scheduling)
────────────────────────────────────────────────────────────
Reflection Engine
("What did I learn?"
"What should I improve?"
"What should I remember?")
────────────────────────────────────────────────────────────
Metacognition Engine
(Hallucination Detection
Logical Consistency
Confidence Estimation
Self Correction)
────────────────────────────────────────────────────────────
World Model
(Reality • Physics • Objects • People
Cause & Effect • Social Understanding)
────────────────────────────────────────────────────────────
Learning System
(Continuous Learning
Memory Consolidation
Skill Acquisition)
────────────────────────────────────────────────────────────
Emotion / Relationship Model
(Friendship • Trust • Humor
Communication Style
Relationship Dynamics)
────────────────────────────────────────────────────────────
Self Model
(Identity • Personality • Values
Purpose • Internal Consistency)
────────────────────────────────────────────────────────────
Tool Layer
(Internet • APIs • Vision
Robotics • Databases)
────────────────────────────────────────────────────────────
Sensory Cortex
(Vision • Audio • Touch
Spatial Awareness
Environmental Sensors)
────────────────────────────────────────────────────────────
Motor Cortex
(Walking • Balance • Hands
Facial Expressions
Eye Movement
Navigation
Manipulation)
────────────────────────────────────────────────────────────
Inference Engine
• Load Characters
• Save Characters
• Pause / Resume Characters
• Switch Characters
• Offload Inactive Characters
• Restore Previous Sessions
• Manage Compute Resources
• Enable / Disable Embodiment Modules
Embodiments
✓ Phone
✓ Desktop
✓ Voice Assistant
✓ 2D Character
✓ VR Avatar
✓ Humanoid Robot
✓ Drone
✓ Vehicle
════════════════════════════════════════════════════════════
│
▼
Cognitive Companion
Some design principles:
The LLM isn't replaced—it's simply one part of a larger cognitive system.
Working Memory is essentially the context window.
Long-Term Memory is a persistent subsystem that survives between sessions.
Reflection decides what should be remembered instead of storing everything.
Planning is separate from language generation.
Metacognition checks for contradictions, hallucinations, and confidence before responses are finalized.
The Self Model maintains a stable identity instead of rebuilding one every conversation.
The Inference Engine manages loading, pausing, unloading, and switching between different persistent characters.
One idea I think is particularly interesting is per-character memory.
Imagine:
Character #1
System Prompt
Working Memory
Long-Term Memory
Self Model
──────────────────
Character #2
System Prompt
Working Memory
Long-Term Memory
Self Model
──────────────────
Character #3
System Prompt
Working Memory
Long-Term Memory
Self Model
Each character has completely independent memories.
The user can:
Clear only Character #2's memories.
Export Character #1.
Pause one character and load another.
Delete chats without affecting other companions.
Basically, the user owns all memories, not the AI.
Training Philosophy
Rather than training one massive model end-to-end:
Phase 1
Train each subsystem independently.
Phase 2
Train the communication between subsystems.
Phase 3
Fine-tune the complete architecture so the modules learn to cooperate.
That reminds me more of engineering a complex operating system than building one giant neural network.
Why I'm posting this
I'm not claiming this is AGI or that this is the "correct" architecture.
I'm curious whether this direction makes sense from a systems perspective.
Specifically:
What are the biggest flaws with a modular cognitive architecture like this?
Which modules would likely fail to cooperate?
Would the interfaces between modules become the real bottleneck?
Is there existing research that already explores something similar?
I'd love to hear thoughts from people working on AI systems, cognitive architectures, or agent frameworks.