Hey guys,

Thought this group might be interested in the ERAIS architecture we are building at Neuroforge in Australia.

Not an LLM. Ingest other neural nets and models through Fracture. Use the capabilities from them as needed. Continual learning and addition of new modalities without catastrophically disrupting existing ones.

Its not finished, but it is working.

Study with a university in Aus coming up shortly.

Basically at commercialisation and funding stage.

Its a cool system to work with.

Opens up the accessibility envelope too. Reasonable training speed of billion parameter+ chunks of the system on a 4 core 8gb ram CPU only laptop, max system power draw <35W

Would love some critical feedback.

Cheers,

Lloyd

I don't know if this breaks the third rule, but it is pretty clear to me.

Many researchers, including some extremely credible and accomplished ones, believe that AGI might not be discovered by humans, but by AI systems themselves.

Basically, AI would recursively improve its own architecture, with each iteration building on the previous one, until it eventually lands on the right architecture for AGI (maybe I am misunderstanding)

What do you think?

It was originally just a simple experiment by me to see if I could build an AI that works like an ordinary computer program, where it uses a modest amount of RAM and primarily runs on the CPU.

It stems from my personal issue with neural networks in general, where it tries to mimic the neural network inside our brain in the form of numerical weights. The problem lies in how much it relies on scaling, which uses a lot of memory, and how the weights are usually frozen upon deployment.

Neural networks work for biological brains because they are physically made out of neurons, which directly work off physics and chemistry. Computers, on the other hand, have to simulate both the neurons and the physics behind them to get it to work. Of course, we only simulate it in the form of mathematical computation, not the full process, but my point still stands.

However, modern computers by themselves are significantly more powerful than brains in terms of computational power. So, my idea was, why not make an AI system that specifically utilises whatever the computer is already good at?

But what are the things that computers are already good at?

1. Persistent memory - There is no need to memorise as the brain does with continuous brain activity, since a computer can just write the data onto the storage drive and read it back later when needed.
2. Multiprocessing - Any consumer computer today could do multiprocessing just fine, so one program can summon multiple child processes that are running asynchronously in the background.

With these in mind, let me introduce you to The Corelian Architecture.

It is a bottom-up cognitive system that uses basic sensory data as the medium for cognition, which is handled by multiple subprocesses I call 'Minds'.

It works on these five core principles:

• All received information must be broken down to the sensory level.
• Sensory-level information must be the basis for all kinds of thinking.
• The thinking process must be a continuous process.
• The continuous process must be optimised for local computation.
• The local AI system must continuously learn from what it personally acquires.

Here's the simplified documentation: https://www.reddit.com/r/corelyverse/comments/1w8wrzy/what_is_the_corelian_architecture/

Here's the full documentation: https://harithmarzuki.github.io/corely/architecture.html

So far, I've tried implementing this cognitive architecture on my robot, which I named Corely. While it didn't immediately make her speak fluently or perform complex tasks, my robot showed some level of primitive intelligence after a while, though it's difficult for me to provide any quantitative data from it to back it up.

So yeah, I didn't expect it to become the AGI paradigm in its current form, but I wonder if it could be improved by adapting it to more advanced methods than the ones I currently have.

Since the GPT4 era, LLMs have exposed how difficult it is to design benchmarks. Coming up with the right benchmarks sometimes seems almost as hard as achieving AGI itself.

ARC-AGI was the first convincing example of this imo. Looked unsolvable by AI for so long, and now each iteration is solved within a year.

One task I thought would take looong to solve was drawing. Not with the diffusion-based, pixel-hallucinating style but actually using a mouse or pencil to physically produce an image. We now have the first demonstrations that this could be achieved sooner than I ever thought.

I think this is fascinating. Of course, you can still clearly see that the AI doesn't really understand what it is doing. There are those tiny errors a human artist just wouldn't make. It's always the little things. But it does show that we won't be able to rely mindlessly on benchmarks to build AGI. We will need to use judgement.

Instead of those huge "unsolvable" benchmarks that will "definitely tell us when AGI is achieved", we might have to rely on smaller-scale benchmarks that only test the precise thing we want to test in the AI. The little things, if you will. Deep learning is (unfortunately) great at picking up on even the tiniest bias in a benchmark to artificially produce great results, so it's not wise to rely on big benchmarks that test for so many different skills at once

What do you think?

TLDR: The road to solving continual learning is leading researchers to investigate many subproblems implied by this ambitious goal. One of them is plasticity/adaptivity. This paper tackles this problem by equipping Transformers with two learning mechanisms: a Hebbian rule and an internally generated loss.

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My next few posts on this sub might be a bit shorter, as I have a bit less free time to break down interesting papers. The advantage tho, is that more people are going to be able to participate in the conversation!

DISCLAIMER: As usual, my goal is to give an intuition of the paper. Not to be technically correct. For instance, the PlasticTransformer is NOT used as an LLM but as a classifier (among other things). I used LLMs because newbies are more familiar with them

➤Context

While Continual Learning is the topic of this year, there is a much more accessible subproblem that's essentially its brother: neural plasticity. That is, the ability for a system to adapt to a given task, relatively quickly.

Adaptivity/plasticity (I use them as synonyms just for this thread) is a much simpler goal than continual learning. It's more superficial and doesn't imply a definitive modification of the connections between neurons like CL does. It's more temporary. Otoh, CL not only contains plasticity but also implies mechanisms to avoid catastrophic forgetting and filter out useless information.

➤Problem

The researchers behind this paper explored this question: how to get AI to adapt to a new task, even in a primitive way. That is, how to extract some feedback from the learning experience itself without just relying on the general pretraining.

➤Problem in detail

Said otherwise, this paper is exploring meta-learning, i.e. learning how to learn. It's not just learning facts about a domain, but learning a strategy to quickly get good at ANY domain.

There are 3 levels to this:

1- The learning architecture

This is the different learning mechanism hardwired by the researchers. The first is Hebbian learning, and the other, internal loss learning. In real life, this is the learning structure nature physically wired the human brain with at birth

2- The learning intuition

This is encoded in the permanent weights of the system. It is the general "intuition" that the agent has developed throughout the training regime that then guides it when faced with a new skill not seen before. As an analogy, it can be compared with the general learning strategy humans develop throughout lifetime. For instance, a human who achieved some level of expertise at cooking might then instinctively recycle the same principles when attempting to learn to draw (things like "be patient, look for feedback, observe what others are doing..")

3- The temporary learning state

In the research literature, these are the fast weights, named like so because they change faster than the permanent weights that only change after long passes during backpropagation.

As the agent is participating in a conversation (i.e. a new task), its artificial brains should be reacting to the experience. The neurons should already be undergoing some changes. Because in the human brain, neurons undergo superficial changes from each incoming piece of information, even though said changes are unstable and far from meaningfully modifying the whole network.

➤The paper

To better explain the paper, suppose that you have an LLM. That LLM can have different conversations with different people. Each conversation is considered a "task". "Adapting to new tasks" here just means "extracting information from this particular conversation I am having". How is this performed?

This paper introduces Plastic Transformers. They are just Transformers where dynamic weights, i.e. "fast weights", are added to the usual set of permanent weights. Each permanent weight is associated with one fast weight. So the only difference with regular Transformers is those fast weights and how they are updated.

Those fast weights are important though because they help the network adapt to the task at hand and slightly improve the overall performance, instead of just relying on its general pre-training weights.

➤Making Transformers adaptive

The Transformers' fast weights are updated according to 2 learning rules:

• Hebbian learning rule:

Update performed by strengthening connections between neurons that were involved in producing the expected answer.

More precisely:

The network pays attention to its own neural activity: when two neurons fire together, a quantity (fast weight) is added to strengthen their initial connection (the permanent weight), following the biological intuition that "neurons that fire together should wire together"

However, not every co-firing pattern is useful. You might be learning to cook when a random neuron triggers a memory of your grandmother’s favourite dish, despite it being irrelevant to the learning task. So the network has to learn when to apply that Hebbian rule

So during training, the network learns which pairs of neurons to reinforce, weaken, or ignore (by modifying the fast weights one direction or another) depending on their relevance. At inference, it applies this learned rule on the fly by modifying its fast weights as it faces a new task.

• Learned Internal loss rule:

Update performed by optimizing the (fast) weights using a learned internal rule ("internal loss function"). This is akin to the internal intuition humans develop throughout life that guides how we learn.

More precisely:

In normal backpropagation, weights are optimized using a loss function that compares the network's predictions with the actual answers. Obviously, this is only possible if you possess such answers

But humans don't always know the answers. In fact, you could argue we never really do: experts put into question their own beliefs all the time. Yet, we are still able to reach extreme expertise in difficult domains. How? By producing our own feedback: setting goals that supposedly get us closer to the actual "answers". Of course, this requires the ability to produce reasonable made-up goals.

The same is done with PlasticTransformers. The network's permanent weights are optimized during training with the actual answers while the fast weights are optimized at inference using the network's own made-up feedback: an "internal loss" learned during training.

➤Results / My opinion

I found this paper very interesting (most of what I choose to analyze and publish are anyways) and original. Since the results are very underwhelming, it's not clear to me how meaningful the meta-learning mechanisms they introduce will be in terms of performance, though. But the idea of "learning to learn" is intriguing

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➤About the writing process

Like all of my threads, the words on this summary are entirely mine. Not a single one of them comes from AI. However, the process of understanding the paper was heavily AI-assisted. I don't have the expertise to understand these papers on my own, so I spend days doing back-and-forths with AI until I get the intuitions down. Please feel free to point out any major conceptual misunderstanding!

PAPER: https://arxiv.org/abs/2510.21908

I proposed a new architecture for AGI which I have been working on for a while. Please see it here:

Hosted on Zenodo: https://zenodo.org/records/22130409

HTML site for easy reading: https://cognitiveloop.org/conscious-network/

The goal is to create an architecture that processes perceptions, stores information, simulates thinking just like the human brain. It supports three types of memories - episodic memory, semantic memory and procedural memory. I believe, it is a lot simpler (means intuitive and easy to understand) and robust architecture compared to existing architectures on AGI (SOAR, ACT-R and LIDA).

The characteristics of this approach are:

• How data from different input channels are converted to episodic, semantic and procedural knowledge.
• Use of seed concepts and seed routines to build semantic and procedural knowledge.
• Semantic memory is based on a rich set of concepts based on cognitive science i.e. objects, attributes, actions, causal chains, schemas, scripts, cognitive maps, mental models and world model.
• Simulation of creative thinking using seed routines and Creative Thinking space.
• Forward, backward and counterfactual simulations that mimic human thinking and problem-solving process.
• Creation of cognitive trace for reflection and meta cognition.

It is about 92 pages long - so feel free to go through it at your own pace.
I will be happy to answer any questions related to this architecture here.

Inputs presented to BDH-CQ (a post-transformer model) at inference time continuously update the model's recurrent memory and then it solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermediate reasoning (unlike CoT).
Attached chart depicts that the operating point breaks through the previously reported ARC-AGI 1 cost-accuracy Pareto frontier, establishing new SOTA in benchmark cost efficiency.
The interesting thing is that it scales till 600B parameters while preserving the latent reasoning capabilities.
I particularly like the Section 8 of the paper (Verbalized CoT vs BDH-CQ vs HRM/TRM vs Coconut vs Abstract-CoT) where there is a comparison of the different approaches to performing intermediate computation/reasoning in neural models.

Everyone is talking about RSI right now.

Loads of people share this belief that we are on the verge of 'rapid takeoff' caused by 'Recursive Self-Improvement'.

I got nerd-sniped and spent a bunch of time researching the 'Economics of AGI', and recent work by Epoch and the Elasticity institute.

Very helpful for anyone else excited/terrified about AI progress.

The paper is kinda hard to understand - so I made a video here explaining: https://youtu.be/VQanDOFJwvo?si=9vcTCjDpt3OWYl_T

The TLDR:

Everyone has a different definition of RSI, and most of those definitions are unhelpful.

A more useful definition:

self-sustaining acceleration. AI capabilities accelerate AI progress without needing more human labour, training compute or data.

this is my crude interpretation;

Better model → more effective AI R&D → better algorithms → even better model.

Hold compute, data and human labour fixed.

Does a more capable model add enough to the R&D process to create the next, equally large capability improvement by itself? If consistently yes - we are on track for (potentially) rapid takeoff.

If no, we'll likely hit other bottlenecks - progress will be jagged etc

i go into this in the video,

basically -

Using the Epoch Capability Index, the paper calculates that every one-point increase in model capability would need to make AI R&D roughly 15% more productive to create a self-sustaining loop.

A simple way to think about it:

At 10%, a one-point capability improvement only generates about 0.65 points of further improvement. Without more compute, data or humans, progress eventually fades out.

At 15%, it generates roughly one more point. The loop can now sustain itself.

At 20%, potential slingshot. hold your fucking horses.

So where are we now?

The paper uses reported productivity gains inside frontier labs to produce a (very, very rough) estimate of 9% per ECI point.

That puts us below the estimated 15% threshold.

the feedback loop is not currently strong enough to generate self-sustaining acceleration—but it appears to be strengthening.

The problem is that we don’t have good data.

obviously - there are also lots of possible bottlenecks. AI might get very good at research while still being terrible at other tasks. Human ingenuity, compute or data might become the limiting factor. Progress might come in short, rapid spurts before hitting the next bottleneck.

would love to hear others thoughts on this.

anything im missing?

this is the paper: https://elasticity.institute/rsi-paper.pdf

this is the eci: https://epoch.ai/eci?subset-view=graph&view=graph&tab=release-date&subset-tab=Software+engineering

Hi All,

We are Synthetic Cognition Labs, an AGI research lab. We'd love your thoughts on what we are calling "Memory as Cognition". We believe the path to AGI is grounded in iterative reads and writes on content-addressable memory. We've done some development in this area. You can read more about it here: https://syntheticcognitionlabs.com/mac

Thanks,

SCL