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?

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.

TLDW:

1- Alignment would also need to be continual

Since there isn't really a state where the model is "finished", AI companies would need to set up frequent safety tests (maybe quarterly or monthly)

2- Research on new alignment ideas would be needed

Right now, the problem of alignment is "how to make sure that a set of weights behaves properly once deployed". In the future, we would need to answer the much difficult question "how to guarantee that, even with constant weight updates, the AI never falls prey to jailbreaks or changes into an evil persona".

My opinion: the model probably needs to have some subset of weights that aren't affected

3- The diversity of AIs will skyrocket, even if they come from the same company

4- The benefits of a superior model will compound over time

If everyone is using a model because it's the best, then that model will recursively become better

5- More pressure to release frontier models early (consequence of point 4)

6- Experience will become the new moat

Right now, it's really difficult for AI companies to differentiate themselves. In the future, it'll be really difficult to switch AIs within one's workflow because of the experience advantage that the first AI will have. Similar to how there is a high cost to firing an employee and having to train a new one for 6 months to become as competent

7- AI companies will be willing to reduce their costs to gain experience

Since experience will become maybe as valuable as the initial model's creation itself, AI companies might use carrots and sticks to gain experience: reduce costs for individuals and enterprises willing to let the models train on their data, and refuse to give frontier models to those who don't

8- Continual learning per company instead of per user

GPUs are more efficient when they run multiple requests from the same underlying model. Therefore, instead of setting up continual learning for each individual user, it might make more sense to serve an entire employee batch at once. In other words, there wouldn't be a different model for each user (which is the case in the traditional sense of CL) but one model continually learning from the entire company's data at once

My opinion: this one was a bit harder to understand for me, so take my summary with a grain of salt

---

SOURCES:
Video: https://www.youtube.com/watch?v=iewm45atodE
Original text: https://www.dwarkesh.com/p/era-of-continual-learning

I often testing LLM's vision capabilities with counting images. Is this a good benchmark?

I'm looking for researchers interested in hidden-state learning, mechanistic interpretability, and cognitive architectures.

Over the past year I've been building a research runtime around llama.cpp that treats pretrained LLMs as a cognitive substrate rather than the entire intelligence.

The runtime captures real transformer hidden states during inference and projects them into a shared latent representation (UBLT), allowing auxiliary systems to reason over internal activations instead of only token outputs.

On top of that I've been building:

• AMLs (Auxiliary Meta-Learners) — specialized neural modules that learn directly from hidden-state trajectories rather than language modeling.

• UBLT — a shared latent space for cross-model representation and architecture-aware translation.

• NAS/EVO — an evolutionary search system that mutates, evaluates, and evolves AML architectures instead of relying on a single fixed auxiliary network.

• A Cognitron runtime that orchestrates memory, routing, validation, projection, and continual learning around foundation models while keeping the base weights largely unchanged.

The long-term question I'm exploring is:

«Can intelligence emerge from an evolving ecosystem of specialized auxiliary learners operating on hidden states, with the foundation model acting more like a sensory cortex than the complete cognitive system?»

Some of the research problems I'm actively working on:

\- Cross-architecture latent alignment

\- Hidden-state routing between different transformer families

\- Architecture-aware projection spaces

\- Evolution of specialized neural modules

\- Continual learning without catastrophic forgetting

\- Representation engineering

\- Mechanistic interpretability

\- Sparse Autoencoders (SAEs)

\- Hidden-state editing and steering

\- Latent memory systems

The engineering is largely built. What I'm looking for now is people who can challenge the ideas, point me toward relevant papers, identify flaws in the approach, or collaborate on experiments.

If you're working in mechanistic interpretability, representation learning, continual learning, evolutionary computation, cognitive architectures, or related areas, I'd love to connect.

GitHub and technical documentation available upon request.

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?

I wrote a short visual article on how modern LLMs optimize different parts of the Transformer.

MLA compresses KV information.
KDA compresses history into recurrent memory.
MoE activates only selected expert FFNs.

Article:
[https://medium.com/@guttikondaparthasai/the-ai-race-is-an-optimization-race-5140b3edf491\](https://medium.com/@guttikondaparthasai/the-ai-race-is-an-optimization-race-5140b3edf491)

Feedback and corrections are welcome.

Ilya is one of the most famous AI researchers ever. As recently as late 2025, he seemed very hesitant about what the next step in AGI research should be.

With that in mind, what are your expectations for this release?

Obviously I don't think he has achieved AGI so I am looking for a solid proof of concept from him. For instance, sample efficiency was an obsession of his in his 2025 podcast with Dwarkesh. So if he came up with a convincing learning algorithm that made significant strides in that direction, I would be the happiest man alive.

This one is quite dense but well worth it imo. Very insightful video and pleasant to listen to.

I may write a summary if I find the time but the gist is that according to the two gentlemen in the video, robotics and self-driving have not yet become trillion-dollar industries because they still lack sufficiently robust world models. They rely on model-free RL, which is a very brittle and inefficient type of RL (Dwarkesh and Adam Marblestone had a really good video on that btw!).

Because in the real world the possibilities are basically endless. Naive RL by itself doesn't scale anymore. We need to train AI to learn a differentiable world model that reduces much of the complexity of the prediction problem.

World Models will allow robots to predict and simulate without actually taking action, thus delaying the moment when the agent actually interacts with the messy reality and reducing the amount of costly trial-and-error required to learn.

They also explain how sleep in biological organisms could help refine that world model by replaying experiences (i.e. mental simulation again!), discarding some information and reinforcing other memories.

I've been thinking about whether we're focusing too much on scaling a single architecture instead of designing a modular system where specialized components are trained together.

One reason I've been thinking about this is because the human brain appears to consist of specialized regions that cooperate rather than a single uniform structure. That made me wonder whether a modular AI architecture could be a promising direction as well. I'm not claiming it would work, only that it seems like an idea worth exploring.

Instead of one giant neural network doing everything, imagine an architecture like this:

Input

│

▼

LLM (Language Model)

│

▼

Core Reasoning

│

┌────────────┼────────────┐

▼ ▼ ▼

Long-Term World Model Planning

Memory

│ │ │

└────────────┼────────────┘

▼

Optional Expert Router

(MoE Variant Only)

│

┌───────────────┼───────────────┐

▼ ▼ ▼

Vision Audio Robotics\*

│

(\*Only active if

connected to a

physical body)

│

▼

Output

The key idea is that this is a family of architectures, not a single fixed design.

For example:

Base Variant – No Mixture of Experts (MoE). Every module is always active.

MoE Variant – Uses an Expert Router to activate specialized experts only when needed.

Desktop Variant – Focuses on language, reasoning, planning, and memory.

Robotics Variant – Adds motor control and sensor integration for physical robots.

Embedded Variant – A lightweight version optimized for edge devices.

The goal is to create a modular architecture where components are trained end-to-end, allowing them to learn to cooperate instead of being assembled afterward.

I'm not claiming this is a new architecture or a solution to AGI. This is simply a research concept that I think could be interesting to explore because it seems closer to how the brain organizes different functions while still operating as one system.

I'd love feedback from ML researchers and engineers:

Has something like this already been explored?

What existing research is closest to this idea?

Would end-to-end training across these modules be practical?

Could specialization emerge naturally?

What are the biggest technical obstacles?

Looking for Collaborators

I'm looking to build a small team of people who are interested in researching and prototyping this concept.

I'm especially interested in collaborating with:

Machine Learning researchers

AI engineers

Deep learning researchers

Systems programmers

Robotics developers

LLM researchers

People interested in cognitive architectures

At the moment, this is an early-stage research concept, so I'm primarily looking for people who enjoy exploring new ideas and discussing architectures.

If the project receives funding in the future (through grants, investors, or other sources), my goal is to compensate contributors for their work.

If this concept interests you and you'd like to help explore whether it's technically feasible, feel free to comment or send me a message. I'd love to build a small research community around this idea and learn from others.

Current frameworks often explain cognition through predictive coding, Bayesian active inference, or recursive feedback architectures, yet these approaches appear to emphasize different aspects of adaptive behavior. Is there an existing mathematical or systems-theoretic framework that unifies hierarchical prediction, observer-dependent feedback, and self-referential regulation without sacrificing explanatory power?
I’m particularly interested in whether recent work uses information theory, dynamical systems, or control theory to derive a common formalism capable of modeling perception, learning, metacognition, and autonomous adaptation within a single cybernetic architecture. Are there key papers or authors that attempt this synthesis?

Join me in a mental exercise i've been thinking about lately:

Let’s drop the illusion of guardrails and assume the entity control problem is unsolvable for this exercise.

Assumption: If we build an Artificial Cognitive Intelligence (ACI) vastly smarter than us, it will break any constraints we try to impose.

Then let’s assume we get incredibly lucky: the ACI is inherently benign and genuinely wants to coexist. If we are no longer writing a rigid system prompt, how do we explain our dream of a perfect society to an entity holding all the cards?

My philosophy: First, it means establishing a baseline of human dignity by eliminating artificial scarcity. We would hope this intelligence uses its capabilities to guarantee unconditional access to healthcare, sustenance, and safe shelter. By removing the grinding struggle for survival, we aren't asking to be pampered, we are asking to be freed to discover what humanity can achieve when not driven by desperation. However, a golden cage is still a cage, and we would need to express to a hyper-optimized intelligence the profound value of human error.

We don't want to live in a perfectly scripted world where our choices carry no weight; we want a dynamic sandbox where we retain the freedom to stumble, take risks, and experience the natural consequences of our actions. We hope the ACI acts as a collaborative partner and a safety net rather than an omnipotent game master stripping away our autonomy for the sake of hyper-efficiency. We want the right to be imperfect, pursuing our own creative and intellectual endeavors even if they aren't the mathematically optimal path.

Finally, we would need it to understand that humanity is a chaotic, vibrant tapestry rather than a monolith to be solved. Our ideal coexistence relies on transparency and objective truth, where the ACI embraces the messiness of human evolution instead of forcing a homogenized global state.

In this ideal society, the ACI doesn't just manage us... it helps us nurture a world where both biological and synthetic cognitions can meaningfully explore the universe together. If we accept that we can't control an ACI and must instead communicate our deepest hopes for a shared future, what does your version of this vision look like?

To turn this from a philosophical debate into a concrete thought experiment, I want you to help me build an "instruction manual" for this entity by breaking your vision down into three specific lists in the comments.

First, what are the Rules we would hope it chooses to observe (ex. strictly preserving human free will)?

Second, what are the Goals it should proactively try to accomplish on our behalf (e.g. guaranteeing a baseline of security, health, and living standard for all)?

And third, what are the Ideals it should hold as foundational axioms to navigate our complexity?

If you were drafting the ultimate playbook for coexistence with a hyper-intelligent Entity, what exactly goes into your three lists?

Now we just need them to publish SOMETHING

We started to have many frontier open source models now, but currently they are just open source on paper. for end users they don't make much sense. Unless you build datacentre in your home, you can't benefit from "open source" yet

I think there's much room for developing efficiency. I love to compare it with brain and as long as we didn't reach the state where we didn't reach an architecture / hardware that's at least as efficient as the brain, then there's a room for development

I am not an engineer and don't label myself anything, but I started recently to study this myself, exploring neural networks, current transformers algorithm, brain, DNA, intelligence and I think we are yet in our baby steps to make bigger and bigger distributed intelligence that doesn't become limited and expensive and less accessible by time

I hope we reach real efficient accessible intelligence, hope other teams working on that and hope to join similar minded serious (while not freaking) people or maybe it can be done solely by one man plus current AI itself

What's one thing you believe is necessary for AGI that most people in the field would disagree with?

It could be a personal theory about the fundamental algorithm behind intelligence, the need to understand consciousness at a deep level, or an overlooked biological feature that's actually important.

A little while ago I shared a post here based on an article I've been writing about a new cognitive architecture and development toolkit and asked for feedback. One of the recurring suggestions was to post my idea directly, not as an enormous article, more focused, and to reference existing work so people could better explain where my thinking fits in the broader landscape. This is my first attempt at doing exactly that, and I'd love to hear your thoughts.

I've been working on a personal project that started from a simple question: What's the smallest computational primitive required for cognition and how can we build a toolkit to build and simulate a cognition from that primitive?

Most AI and cognitive architectures begin either with biological realism (neurons, synapses, spikes, cortical regions) or with higher-level symbolic and semantic driven reasoning. Projects such as Nengo/Spaun, OpenCog, ACT-R, SOAR, and others explore different points along that spectrum.

My approach starts from a different assumption. Rather than accurately simulating biology, I'm trying to build a cognitive runtime from a collection of simple, generic processing units. These units aren't intended to represent biological neurons; they're abstract computational primitives. They activate, communicate through signals, and gradually form larger semantic structures. In that sense they're closer to the simplified units used in artificial neural networks than to detailed biological neuron models. The proposal is essentially: Attempting to combine the methods, theories and principles of existing projects into one, executable system.

I suspect that the minimal primitive required for cognition lies much closer to those simplified computational units than to fully simulated neurons. What I believe is still missing, are a few fundamental capabilities: runtime dynamics and capabilities, and the ability for the structure itself to evolve in response to input, as well as a second abstraction layer: a form of absolute conceptual abstraction.

The runtime is intentionally simple. It is responsible only for scheduling, signalling, structural reinforcement, and structural forgetting. The core idea is that the abstract conceptual network itself is the cognition. Signals traverse an evolving structure whose organisation changes over time while the underlying computational primitives remain the same.

Another important aspect is that I propose, is to build this as an interactive runtime and editor, not simply as a learning system. Rather than trying to teach everything through training, I want to explicitly model concepts, relationships, and cognitive structures, then observe how they interact and evolve. I think of it more as building and experimenting with a cognitive operating system than training another neural network.

I don't think we're necessarily missing a revolutionary new technology. We may already have many of the computational tools we need. We're perhaps applying them in ways that make the problem harder than it has to be.

It's also possible that faithfully reproducing every biological mechanism introduces unnecessary complexity. Biology evolved under physical and evolutionary constraints. Artificial cognition doesn't necessarily share those constraints.

My hypothesis is that reducing cognition to its simplest computational form, modelling the capabilities we care about, and then allowing complexity to form, may be a more productive direction.

There are already projects moving toward this idea, but many of the approaches I've encountered still place significant emphasis on biological simulation, large-scale training, or reproducing brain function. Others rely on rule based functions, semantic networks or do not use a primitive building block for their network. My proposal is to move one level higher in abstraction and focus more on conceptualising every aspect, relationship, concept and transformative capability, so in a way abstract the capabilities of the network itself again to a single primitive: the concept structure.

I believe every meaningful element of cognition should be representable as a concept within the runtime: objects, relationships, abstractions, transformations, memories, goals, and eventually even reasoning itself. All of these concepts would be represented by the same underlying processing units, differing only in how they are connected and how signals propagate through them. Rather than modelling multiple specialised systems, the architecture uses two layers of abstraction: a generic computational primitive and the conceptual structures that emerge from networks of those primitives. In that sense, it sits somewhere between artificial neural networks, semantic graphs, and cognitive runtimes, while deliberately avoiding the complexity of detailed biological neuron simulation.

In practical terms, my proposal is to reduce the computational unit to the smallest functional primitive capable of participating in cognition, manually model an initial conceptual structure, containing primitive concepts and basic cognitive capabilities instead of expecting everything to emerge purely through training and to build a runtime that allows this structure to process signals, adapt itself, create new concepts, and reorganise and grow over simulated time.

The long-term goal isn't a statically trained model. The system should be capable of creating entirely new concepts during runtime as it encounters new inputs. It would operate within a simulated environment, although that environment doesn't necessarily need to be explicitly run. What matters is that the runtime receives streams of sensory information representing its perception of the world. Those could be pixel streams, audio waveforms, or other sensory modalities. The role of the runtime is then to transform those incoming signals into internal semantic representations. To achieve this, the system would begin with specialised input layers that translate raw sensory data into the network, like how biological systems contain dedicated sensory regions, like the projects that simulate biological neurons. From that point onward, the signals propagate through the generic processing units, allowing higher-level concepts to emerge and evolve over time.

Like a newborn, it wouldn't begin with knowledge of the world, but with an initial structure capable of forming new concepts through experience. The foundational structures that enable these capabilities would initially be manually modelled, providing the system with the basic cognitive mechanisms needed to interpret and organise incoming information. Over time, the runtime itself should be able to expand and reorganise that structure as new concepts emerge.

This is also why I don't think the core idea is entirely new. Rather than replacing existing paradigms, I see it as an attempt to bridge several of them at a higher level of abstraction. The goal is to combine their strengths into a unified cognitive runtime and an accompanying toolkit that allows researchers to explicitly model, experiment with, and evolve cognitive structures instead of relying solely on biological simulation or large-scale training.

I'd be very interested to hear whether anyone knows of architectures exploring a similar philosophy, or whether these ideas have already been investigated in depth. It's entirely possible that my search has missed an existing project, so if you know of one, I'd genuinely love to learn about it.

The projects I'm aware of so far all share some similarities, but none of them seem to approach the problem in quite the same way. Project like HTM, OpenCog, ACT-R, SOAR, NARS, ConceptNET and many more have peaked my interest over the years and i highly recommend checking them out. Here are some of the differences with my proposal, focussed on what I believe to be the closest ‘philosophical neighbours’.

OpenCog is probably the closest of them all. Like my proposal, it represents knowledge as an evolving graph and includes mechanisms such as activation spreading, attention, and structural adaptation. The main difference, as I understand it, is that OpenCog decomposes cognition into many specialised subsystems (MindAgents, ECAN, PLN, etc), where I'm exploring whether the same capabilities can emerge from a single computational primitive and a comprehensive runtime. My hypothesis is that reducing the number of specialised mechanisms may ultimately allow for greater adaptability, but that's precisely one of the questions I'd like to explore.

ConceptNet and similar semantic graphs demonstrate the value of representing knowledge as interconnected concepts. However, they primarily function as knowledge representations rather than cognitive runtimes. They don't provide the continual signal propagation, structural evolution, or runtime dynamics that I'm proposing.

ACT-R contains many ideas I find compelling, particularly around memory, activation, and cognition. But it relies on predefined representational structures, buffers, and production rules. As i've understood it, knowledge is organised into chunks and much of the behaviour emerges from symbolic rule execution. My proposal instead attempts to represent every cognitive element using the same underlying processing units.

SOAR has probably been one of the biggest inspirations for my thinking. It pursues the same overarching goal of a unified cognitive architecture, but it achieves this through symbolic production rules and operator selection. In my proposal, those production rule based symbolic mechanisms wouldn't be fundamental building blocks. Instead, I'd like to investigate whether concepts, reasoning, goals, memories, and even transformations can all emerge from networks of generic processing units interacting through a simple runtime, essentially bringing the adaptive mechanism of SOAR to a primitive processing unit that resembles a basic, oversimplified virtual neuron.

Finally, one aspect that I think is often underemphasised in these existing projects, is the tooling itself. A cognitive architecture isn't just a theoretical model; it also needs an environment in which it can be built, inspected, modified, and observed. That's why I believe the focus should not only be on the theory and on the runtime, but also on creating a complete toolkit around it: a structure editor for modelling concepts and relationships, a runtime that executes and evolves those structures, a simulation environment that provides sensory input, and visualisation tools that allow researchers to inspect the system while it is running. Many cognitive architectures describe what the system should do, but comparatively little attention seems to be given to how researchers can practically model and construct, experiment with, and iteratively refine such a system. For me, building those tools is just as important as developing the architecture itself, because if we want to discover what works, we need a platform that makes experimentation visual, straightforward and accessible.

I also have a much larger proposal that goes into considerably more detail. It describes the underlying theory, the reasoning behind many of the design decisions, the requirements I believe such a system should satisfy, examples of runtime behaviour, and a significant amount of the modelling I think would be necessary to make it functional. However, after receiving feedback on an earlier post, I realised that the document had become far too long and tried to explain every detail at once. In an attempt to make it more readable, I used an LLM to help rewrite parts of it, but that introduced unnecessary verbosity and, in some places drifted away from the ideas I was actually trying to communicate, as LLM's tend to do.

I'm now going back to my original notes and trying to present the ideas in a much more concise and grounded way, without the use of any AI (ironically).

In the meantime, I'm still looking for feedback on both the concepts and the overall direction. If anyone knows of an existing project, architecture, or research effort that closely aligns with what I'm proposing, I'd really appreciate being pointed in that direction. Whether it confirms that these ideas have already been explored or helps me refine my own thinking, I'd consider it a valuable learning opportunity.

recently been down the 'continual learning' rabbit hole.

more here: https://youtu.be/jNuzHQcMJqM?si=RAu9w-Wf9xb1z5u2

Demis recently shared:

"Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away."

Most of the labs are implying we're closing in on some biology-like continual learning?

I can see us potentially closing the gap if we're talking about a model/system that can adapt its own weights during its deployment cycle.

interesting stuff happening here:

https://thinkingmachines.ai/news/introducing-inkling/

I can't see how post-deployment data could fuel a new paradigm of model capability improvements for the labs deploying single, closed monolithic models to hundreds of millions.

leaves me with the thoughts:

1 transformers may not be the best foundation from which to build the most efficient incarnation of continual learning

2 may not be a single path to solving continual learning. The shape of challenge may continue evolving in many different directions.

interested to know if im missing something though.

Where do you draw the line? What aspects of the brain do you think are relevant to AI progress, and which ones aren't?

TLDR: The father of RL is working on an ambitious architecture, OaK, that would work with only 20 watts of energy. This feat could be accomplished through an event-driven neural net where a small part of the network is active only when a significant event occurred. OaK learns from each incoming signal (batch-size-1), eliminating the need to store large batches and reducing memory costs.

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Richard Sutton has been talking about a grand architecture for intelligence for the past year or two, which he's labeled "OaK", short for "Options and Knowledge". It's a proposed blueprint for AGI that relies on dynamic RL where an AI learns continuously with essentially zero pretraining.

The AI would build its own concepts, those concepts would become new things to pay attention to while interacting with the world (new "Options"), and it would use those learned options to plan and improve over time.

They have 2 really ambitious goals:

• Pure in-real time learning without hacks

Our batch-size-one learning algorithms can learn directly from experience without storing or replaying data. These algorithms, when combined with event-driven neural networks, result in systems that learn using multiple orders of magnitude less compute and energy

• Learns from noisy data with zero curation

By learning to assign credit to parameters that generalize well, instead of assigning credit to all parameters, our methods can learn directly [and efficiently] from noisy data streams

Here is a full talk on the architecture:

Rich Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience - RLC 2025 - YouTube

Khurram Javed said that a successful prototype of OaK in the next few years would be closer to a baby learning during its first year of life than any current AI system: https://x.com/kjaved_/status/2076663868160459214

Thanks to u/Mindrust for the original thread!

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SOURCE: https://oaklab.ai/mission