I've been thinking about the importance of time in the context of AI for about 15 years. The other day I formulated a simple thought.

Spikes are actions. When you store say a neuron ID and a timestamp of a spike in memory, it's not data. It is a suspended action waiting to be resumed.

This idea is so simplistic, it seems analogous to rediscovering the wheel. Yet, I bet it is the most controversial idea in artificial SNNs and it could change the way you think about computation.

It is also related to the symbol grounding problem. If you express all information in terms of actions (suspended actions / their timestamps), no symbols are ever created. Also see [The Chinese Room Argument](https://en.wikipedia.org/wiki/Chinese_room)

Let me know what you think.

ATOM Architecture: A Sustainable, Deterministic AI Computing Framework

We all know modern AI data centers are burning massive amounts of power, and traditional silicon is hitting a thermal wall. You can only shrink a transistor so much before heat becomes an impossible problem.

I wrote a technical architecture specification exploring the necessary next step: moving away from electricity and copper, and transitioning to optical-acoustic computing. By processing data using light and routing it with sound, we can eliminate transmission latency and turn waste heat back into usable power.

I’ve broken the core concepts down into plain English on the front end, and kept the heavy math, physics, and tensor mechanics in the appendices for the engineers. Would love to get some eyes on the mechanics of this.

I've been exploring a different approach to local AI with a project called Aura.

The basic idea is that the language model should not have to be the whole cognitive system. Aura uses a local ~27B model as one component, while memory, task continuity, perception, action, learning, self-modeling, verification and other processes exist in the larger architecture.

Two recent experiments are why I think the idea is worth discussing.

In one, Aura autonomously played 2048 for around 29 minutes and 968 moves. It maintained the same objective, planned ahead, changed strategies when they stopped working, recovered from mistakes and eventually reached 2048.

In a very different experiment, Aura spent about 42 minutes autonomously taking a 60-question personality assessment. It predicted its result beforehand, reasoned through every item using its persistent self-model/history, operated the website, submitted the test, read the externally generated result and then evaluated where that result did and didn't agree with its earlier model of itself.

Neither demonstration proves AGI, and I don't think two tasks are enough to establish general intelligence.

However...

If one frozen architecture can reuse memory, planning, self-modeling, world interaction and learning machinery across increasingly unrelated environments, at what point does the architecture itself become a meaningful source of generality rather than merely a harness around the model?

Full latest demo:
https://www.youtube.com/watch?v=LNlGUBeTIQY

Architecture/research repo:
https://github.com/youngbryan97/aura

What experiment would you all use to falsify the architecture hypothesis?

I have been seeing a lot of AI slop posts on this sub. While I don't mind people using AI to assist with writing, it's clear that the quality of posts on this sub has been subpar for quite a while, especially since I decided to loosen up a little bit.

As a reminder, the goal of this sub is to discuss new AI architectures. But not everything deserves the title "architecture." It needs to be something backed by rigorous science and testing. Lots of people just ask ChatGPT to spit out some generic architecture dressed up in fancy language.

I am looking for science. Sound methodology. Papers. It's okay to share your own untested ideas, but they need to be specific. Not just a bunch of random words (like recurrence, resonance, and other BS).

In particular, threads about AI consciousness have been a plague. I don't believe we are close to achieving AI consciousness, but I still consider myself open to the idea if the person making the thread makes a reasonable effort to present actual research with convincing findings. There have been maybe two threads that achieved this (like this one).

So I'm making the decision to ban threads on consciousness altogether, with exceptions at mod discretion. 95%+ will be deleted within the first few minutes of me reading the abstract.

I'm also starting to question the relevance of "neurosymbolic AI" posts. The concept itself has been explored by serious and reputable researchers (Francois Chollet), but right now, 90% of the architectures posted here are just LLMs with extra steps (those so-called "cognitive architectures")

I don't want to overdo this, though. Am I going too far? I really feel like this sub has been polluted with AI slop for a while now, but if you feel otherwise, please share your thoughts. I want community feedback on this.

I mean, I know why, but do YOU know why? lol

The full model is in Section 8 in "Garvey, M. (2026). Shadows of Consciousness: An Investigation into Ionic Neural Networks Using the Neurotransmitter Ion Receptor Glial Endocannabinoid Network (NIRGEN) Paradigm. Available at SSRN 7414038." (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7414038

)

#neurons #ions #neurotransmitters #biology #neuroscience #computationalneuroscience #artificalintelligence #xor #logic #math #booleanlogic #ai #ann #perceptron #machinelearning #ml #datascience

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I’m curious about how major AI and robotics companies structure their data collection for foundation models. Do they organize their knowledge/data according to an academic structure (e.g., Natural Sciences → Mathematics, Physics, Biology; Computer & Information Sciences → Computer Science, AI, Information Science; Engineering, Arts & Humanities, Social Sciences, etc.), or do they use a completely different taxonomy? If anyone has insights or knows of papers/blogs describing how companies like OpenAI, Google DeepMind, Anthropic, Meta, or leading robotics labs structure their data and knowledge coverage, I’d really appreciate it.

I've been building PRIM, an experimental computational project based on a simple question:

Can persistent structure emerge from initially identical states through recurring interactions, without defining currencies, prices, groups, values or winners in advance?

The proof-of-concept starts with 24 identical anonymous states. Interactions occur over time, and in PRIM those interactions affect the probabilities of future interactions.

I compared it with a no-feedback control.

After 50,000 events:
PRIM: 2.67× highest-to-lowest activity difference
Control: 1.07×

I also tested persistence across checkpoints and independent restarts. Structure persisted strongly within individual histories, while different states emerged as strongest across separate symmetric runs.

The next stage is testing admissibility — whether the emergent structure itself can determine what actions become possible.

I've also launched a Kickstarter to fund that next stage.

I'd be interested in what people here think of the project, especially what you'd test next.

Hi, I just built Unique Host.

It’s a lightweight, non-LLM, roleplay-focused engine where characters have their own state, memory, and identity.

You can create a character, give them a world, and play with them — or just try the included characters, Delia and Joaquin.

The idea is that the character doesn't just remember the conversation. The character is supposed to remember what happened to them, and that history can affect how they behave in future interactions.

It can be run locally and uses no GPU, no API, and no LLM.

This is a very early v0.3, so expect bugs, strange behavior, and plenty of things that still need refining. 😅

You can try it here: https://huggingface.co/spaces/Bichini/Unique_Host

Or download it here: https://github.com/Bicheno1/unique-host

I built it as a small implementation of my Cognitive Coherence Model (CCM) architecture.

Theoretical framework: https://zenodo.org/records/20648800

I also created this addon as an experiment to see how the CCM architecture can control a body.

It's called Jellyfish AI.

It currently has a jellyfish, a turtle, and a fish. You just put them in the scene and see what they do.

You can also put several turtles, fish, or jellyfish together and see what happens.

They can perceive things around them, and their behavior comes from the architecture and their current state.

Jellyfish AI: https://github.com/Bicheno1/Jellyfish-AI

I’ve been experimenting with a different way of scaling AI.

Instead of primarily asking “how much bigger can the foundation model get?”, Aura asks whether persistent architecture can move useful cognition outside repeated LLM inference.

The resident cortex is a local ~27B model, but the running agent separately maintains things like:

memory, a self-model, learned environment dynamics, valuation, planning/search, procedures, task knowledge, computer embodiment, and persistent experience.

The attached demonstration is 2048. I don’t think 2048 itself proves general intelligence, and this is not a clean zero-shot task. However Aura is not trained on 2048. There is no 2048 runner being used. And this environmental work transfers generally. It is not limited to or fine-tuned to 2048.

What I find interesting is the hierarchy visible during the run:

local action evaluation → lookahead → standing strategy → strategy revision

The system also has internal epistemic resources: it can query previous experience, its resident cortex, local retrieval, or an offline Wikipedia corpus when it decides it lacks information.

I’m now trying to falsify that with matched-base-model controls and novel environments.

Repo: https://chatgpt.com/c/6ab632da-4bf4-83e8-bcec-9b1ec4ebf3e6

Full run: https://www.youtube.com/watch?v=wE5Dx5l3pjI

For years, the AI industry has largely followed the idea that more data + more compute + bigger models = better AI.

But is simply making models bigger really the future?

I recently watched Richard Campbell discuss neural scaling laws, and it got me thinking about where AI development goes next.

Should we keep scaling models, or should we focus more on efficiency, reasoning, and smarter approaches?

I'm curious what developers and AI engineers here think. Is scaling still the right path?

X-posting an article I published on GitHub that details a hypothetical framework to generate spontaneous random thoughts in autonomous agents.

For those who are happy with the progress so far, what papers or results have impressed you the most?

The world now has structured terrain, resources are distributed across different areas, and environmental forces affect how the creatures move.

There’s also a new Undercover mode. The researcher can enter the same world using a separate physical body, move around and interact with the creatures directly.
https://github.com/archonlab/mechanistic-mind