r/newAIParadigms

We often hear about many different kinds of memory (the following definitions are just rough intuitions):

• working memory (fades in a couple of seconds, with a capacity of around 6 items)
• short-term memory
• long-term memory (lasts years if not indefinitely)
• associative memory (ability to retrieve information from partial cues, e.g., in Hopfield networks)
• semantic or declarative memory (very roughly: the ability to remember textual info like "Paris is the capital of France")
• episodic memory (based on meaningful events)

Do you think these could all emerge from one fundamental mechanism, or are you in favor of explicitly hardwiring a few of these to build AGI?

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.

I think Hybrid Intelligence is already here but not how we expected it.
When we think about it, we immedeatly go to a biological-neural approach but there are many ways we can potentiate human skills without replacing them.
Have this in mind:

• What AI does best: Data processing, pattern recognition at scale, hyperpersonalization.
• What Humans do best: Empathy, social learning and accountability, creativity, communication (among many, many other things)

The challenge? figuring out the exact balance point between those two. With clear boundaries you can totally make this work. This is how I've applied it in my field:

My partners and me figured out that innovations usually hits education last, especially in the language edtech sector. Then we asked ourselves What are main challenges for teachers? personalization. That's where AI comes in incredible handy.

We've created ORA, a system where the human professor leads the group class, fosters community and communication, reads the classroom's energy and holds students accountable. And AI? It's handles the background work, helps studients with their specific needs tailoring the experience to improve the learning curve.

This is just a minor example of what I think AI can do if used right but we need to actively search for these balance points. What do you think does this actually counts as hybrid intelligence or am I just being naive?

Hey everyone,

I am not a scientist but I got really intrigued by "The Thousand Brains Theory" (TBT) and some other books I came across. What started as going back and forth with Claude led to a whole bunch of experiments, a scientific approach to collaborating with Claude and, what I consider, an uncommon approach to intelligent systems with online learning capabilities.

Having a frozen llm model is one thing, but in my opinion, it will not lead to anything substantial. After all, it remains a game of playing with prompt/context/<fill-in-today's-term> engineering. If we want to get somewhere meaningful, we need a system that never stops learning. That's where the whole PRA idea came from and honestly, the journey link above explains it better than I can directly.

Instead of focussing on a reward-based system, PRA uses a drive and curiosity as I discovered in https://impire.io/poseres-book/part-3-the-mechanism/09-wanting-things.html.

I am not claiming anything here, just want to share what I think is interesting as many of the things sure did surprise me. All of this is done in the open and I explicitly keep a journal. The website is updated based on that journal so the "book" you see, is actually more of some sort of diary.

I can't jot down the whole journey here as it is a multi-week storyline. So take a look at the journey and the project

I hope it is useful to some of you! If not, either way is fine by me. I will just continue with this because it is fun and keeps my brain going ;)

Cheers!
D.

No question this is terrible news for us. According to Goertzel, recent moves at Google (such as Demis stepping down as CEO and DeepMind being more integrated into Google) suggest that they may be abandoning research on alternative paths to AGI and going all in on Transformer LLMs.

I made a thread a few months ago where I ranked Google as the biggest contributor to the research landscape for alternative architectures. They are almost the modern version of the legendary AT&T lab, where they provide tons of resources to researchers to explore interesting paths that may not pan out immediately (and for those who don't know, AT&T directly led to most of the biggest technological breakthroughs in the 20th century).

This would be a massive loss for the scientific community. IMO, science progresses when a deep-pocketed company can afford to pour billions into ideas that have no guarantee to work.

This is all speculation from Goertzel for now, but for me it's a truly terrifying prospect. Especially as someone who believes we are relatively close to AGI but that LLMs in their current form have already offered everything they have to offer. I am curious where y'all stand on this.

Link to the full story: https://x.com/bengoertzel/status/2085440120413421685

For the last few years, we have evaluated AI systems primarily by asking which model produced a result.

I suspect that question is beginning to lose some of its importance.

As models gain tools, memory, retrieval, evaluators, feedback loops, specialized roles and stopping conditions, the decisive unit is no longer the model alone. It is the harness: the architecture that determines what the model sees, what it may do, how its output is tested, what is remembered and when another iteration is justified.

The model will still matter. Different models—and combinations of models—will reveal very different strengths. But the model may increasingly become one component inside a larger cognitive system.

A weaker model inside a well-designed architecture might sometimes outperform a stronger model operating in a poor one.

So when an AI system produces an unexpected discovery, solves a difficult problem or shows something resembling emergence, will the important question still be:

“Which model did this?”

Or will it become:

“In which architecture did this become possible?”

Where do you think the decisive capability will come from—the model, the harness, or the interaction between both?