Selective Self-Organization Hypothesis (GSS 1.0) An Operational Architecture of Functional Subjectivity (with Ontological Extensions)

Science Tangential Theories
1

I propose an alternative architecture for understanding functional subjectivity based on selective self-organization, rather than on the training methods of modern neural networks.

Instead of optimizing a loss function on data, the system operates through negative selection: conflict generates variability, the unstable is destroyed, and what withstands remains. The central mechanism is the cycle E → C → M → S → Ω (Expected state → Conflict → Variability → Selection → Possibility space). The architectural primitive is the trichronic selector (three temporal slots: past / now / future) with parallel execution and asymmetric destruction of the past slot.

In this framework, subjectivity is understood as a stable recursive organization of causal contours that can influence its own dynamics.

The full hypothesis is presented in two parts on Zenodo:

  1. GSS 1.0 – Operational Architecture
  2. Ontological and Theoretical Extensions

I would be grateful for evaluation of the work, constructive criticism, and thoughts on whether this type of architecture is worth developing further, and whether it could be useful in the study of dissipative systems of different kinds.

Note on LLM use: I used large language models as a tool for structuring extensive drafts and for stylistic/language editing. I regularly checked that the meaning and key formulations were not distorted. The hypothesis, operational architecture, experiments, and theoretical positions are my own.

2

PROJECT MANIFESTO

A Return to the Analog Adaptive Machine

I am looking for specialists interested in building a new generation of adaptive computing systems.

The project is based on an idea I arrived at through my research into mechanisms of self-organization: computation may occur not only through the execution of a predefined algorithm, but also directly through the dynamics of the system itself.

The historical starting point for me is the work of W. Ross Ashby and his Homeostat — one of the earliest physical machines designed to demonstrate adaptive behavior.

Ashby worked with the technologies of his time. Today, fundamentally new possibilities have emerged: memristive devices, analog electronics, neuromorphic architectures, new materials, and accessible tools for modeling complex dynamic systems.

I believe the time has come to return to this idea and attempt to implement it at the technological level of the 21st century.

What I Am Researching

In the course of my work, I developed a hypothesis of Selective Self-Organization, based on the cycle:

E → C → M → S → Ω

where a change in the system’s state proceeds through expectation, conflict, variability, selection, and modification of the space of subsequent possibilities.

This mechanism is already being investigated through computational experiments.

A detailed description of the theoretical foundation and experimental work is available in two publications:

I am not asking anyone to accept the hypothesis on faith. The next step is to test it independently and move it from a computational model into a physical system.

The Next Step

My immediate goal is to create an adaptive analog computing system capable of using its own dynamics to search for stable solutions.

The first practical application I envision is an adaptive analog optimizer.

Potential applications may include:

  • optimization of industrial processes;

  • energy systems;

  • logistics;

  • routing;

  • resource allocation;

  • control of complex technical systems;

  • other problems in which the solution space is continuously changing.

The key interest is not simply obtaining an answer, but the possibility of obtaining it through the self-organization of the computational medium itself.

If the system can demonstrate a measurable advantage over conventional methods, that would provide a basis for further technological development.

Why I Need a Team

At this stage, I need specialists capable of translating the existing model into a physical implementation.

I am looking for people with experience in:

  • analog electronics;

  • memristive technologies;

  • neuromorphic computing;

  • nonlinear dynamics;

  • control theory;

  • mathematical modeling;

  • FPGA and embedded systems;

  • materials physics;

  • hardware engineering;

  • optimization.

I am also looking for entrepreneurs and funding specialists who can help turn the experimental development into a technological project.

My Principle

I do not need followers.

I need specialists.

I am not asking anyone to consider the hypothesis proven simply because it appears convincing.

I am proposing that we build the system, run the experiment, and accept the result.

If the hypothesis fails, that will be a valuable scientific result.

If it succeeds, it may provide a foundation for a new computing technology.

From Research to Technology

I see the development of the project as a sequence:

theoretical model → computational experiment → physical prototype → applied optimizer → commercial application.

The first stage is to form a team and secure funding for the development of an experimental platform.

The purpose of the funding is not to support an abstract idea, but to bring it to a working physical prototype and a measurable result.

Long-Term Perspective

I do not see analog computing as an attempt to return to the technologies of the past.

On the contrary.

I see it as an opportunity to directly use properties that digital systems normally have to simulate:

continuous dynamics, memory, nonlinearity, interaction, and self-organization.

Ashby showed the direction.

Modern technology gives us the opportunity to take the next step.

I want to determine whether it is possible to create a computing system in which the physical organization of the system itself becomes part of the computation.

For this, I need people who are willing not merely to discuss the possibility, but to build the experimental machine.

I started this project independently. Now I am looking for people capable of helping take it to the next level.

3

I am not asking anyone to believe in me. Belief is hope for a certain future, but hope is not proof. I may be wrong. And my mistake may not be merely a theoretical mistake — it may turn out to be a mistake of my entire life. This project may lead to success, or it may end in complete failure. Therefore, I urge everyone who considers joining to make that decision carefully and independently. Not because I lack confidence in my work, but because I recognize the real cost of being wrong.

5

The theory of consciousness was written in 1999. I don’t want you to believe in it — there are many theories out there. In my theory, I only described one life — my own. The entire chain E → C → M → S → Ω — and that doesn’t mean it claims to be the truth. In the corridors of psychiatric hospitals, I had to see not the phenomenality of consciousness, but breakdowns. I don’t want you to believe in my truth. I only want you to pay attention to the other side of the coin. First and foremost, I am calling for dialogue, because by my own theory, I am locked in my own pattern. I would like to hear real people.

6

I looked at your PDF, but I didn’t knew what to make of it. I resonate with some of the ideas there, that I found important, but most of the paper I have trouble understanding. When facing information overload, most people choose to remain silent.

1 Like
7

Thank you for your comment. I admit that on a foreign platform, you are the first person to respond. I would like to attract people connected to the theory of consciousness and those who are familiar with the work of Ashby. But to my first commentator, I will respond differently. You gave me energy. For that, thank you very much! I am confident that one day I will be able to translate my work into accessible language — I am no different from you in that regard, and I am also used to communicating in clear, understandable language. I will definitely do that. Thank you for your comment. It was the first. And it made me very happy

8

In our time, with LLMs, you can simply give my work for evaluation. The only drawback is that LLMs will judge it within the framework of the known — and will not give you the full depth. But still, this is better than nothing, wouldn’t you agree?

9

Thank you for your comment. I admit that on a foreign platform, you are the first person to respond. I would like to attract people connected to the theory of consciousness and those who are familiar with the work of Ashby. But to my first commentator, I will respond differently. You gave me energy. For that, thank you very much! I am confident that one day I will be able to translate my work into accessible language — I am no different from you in that regard, and I am also used to communicating in clear, understandable language. I will definitely do that. Thank you for your comment. It was the first. And it made me very happy

Thank you for your comment. I admit that on a foreign platform, you are the first person to respond. I would like to attract people connected to the theory of consciousness and those who are familiar with the work of Ashby. But to my first commentator, I will respond differently. You gave me energy. For that, thank you very much! I am confident that one day I will be able to translate my work into accessible language — I am no different from you in that regard, and I am also used to communicating in clear, understandable language. I will definitely do that. Thank you for your comment. It was the first. And it made me very happy

10

You can submit this for grading by LLM, but if you want more depth, ask me. This work was written in 1000 drafts, and I have an answer to any of your answers.

13

Hey LEMA,

I saw what you are building, and while I know you are mainly looking for hardware specs to get into physical analog tech, my project BrainStem V8 could be a great software testbed for your early experiments.

We are both building on Ashby’s cybernetic principles. BrainStem handles homeostatic feedback loops in software to keep the system balanced and stop it from saturating. Instead of relying on static code, it uses a network of digital neuromodulators to dynamically tweak things like learning rates and exploration pressure on the fly.

It also maps really well onto your whole E to C to M to S to Omega phase cycle. We use hypothesis exploration for variability, threshold filters during sleep phases for selection, and metaplasticity to shift the possibility space.

Since physical hardware is tough to iterate on quickly, you could use BrainStem as a digital reference model. It is fully running on Python and SQLite right now. I am not a hardware engineer, but I can offer the system architecture, mathematical modeling, and a ready to use simulation environment to stress test your concepts before you bake them into physical circuits.

Let me know if you want to brainstorm or run some test cases together!

15

Hello unikum-sol,

Thank you for your message and for your interest in my work.

First of all, I would like to learn more about BrainStem V8 — its architecture, underlying principles, and how the mechanisms you described are actually implemented. For me, it is important to first understand whether we are truly moving in the same direction and how deeply our approaches intersect, rather than simply using similar terminology.

I do see a digital environment as a possible platform for preliminary experiments before moving toward physical implementation. Therefore, I would be very interested in seeing how your system is structured and which processes emerge within it naturally versus which ones are predefined by the architecture.

Recently, I have increasingly begun to see my own work as, in some sense, a continuation and development of Ashby’s ideas about the homeostat, but in the context of modern computational systems and the study of self-organization. That is why your reference to Ashby and homeostatic feedback loops particularly caught my attention.

I would be glad to learn more about your work. If you have a project description, documentation, repository, or any materials describing the architecture of BrainStem V8, I would appreciate it if you could share them with me.

Best regards,
LEMA

1 Like
16

Here is my Git GitHub - unikum-sol/brainstem: BrainStem is a biologically inspired, Real Neuro-Symbolic (RNS-AI) cognitive architecture for lifelong learning. It is designed to learn models of the structures and dynamics of language and text through context hypotheses, uncertainty, contradiction, revision, neuromodulation, replay, and consolidation rather than by merely storing isolated facts. · GitHub

But it is still in development. Facts Generations currently blocked because I’m just finishing calibrating the neuromudulator kernel.

I’m really curious what you say and what you think about it.

1 Like
18

You have done an enormous amount of work, Unikum-Sol, and I will study it carefully. I’ll get back to you with a more detailed response in the near future.

I can already see some interesting points of convergence between our approaches, and I’d like to examine them more closely before drawing any conclusions.

Best regards,
Lenar Masalimov

1 Like
Self-improving intelligence
19

Here is my HTM Forum Article

1 Like
20

Hello, unikum-sol.

I have carefully studied your work. I am answering you as a researcher — without using an LLM, directly and to the point.

My first remark, which you may not like, is the following: you use neurobiological terminology while substituting it for algorithmic operators that build causal links. You call the mode of hypothesis generation “sleep,” although in essence this is simply the background operation of the system in the absence of an incoming data stream. In my view, this is a beautiful neurobiological wrapper that is redundant for the task of working with the meaning of texts. The value of your work is not diminished by this — it is just that the terminology, from my perspective, creates an unnecessary layer.

I started with criticism not in order to devalue what you have done. I like your approach precisely because you are trying to implement three things that I consider genuinely important: conflict, variability, and selection. And you do this in your own, rather specific way. The fact that so much remains “under the hood” is not normal, because you have not shown how exactly you form causal connections even on a stream of several hundred words. Without this it is very difficult to judge how your platform actually works. In the description of the philosophy it is still hard to see what concrete practical goal you are solving. It creates the impression that the system is being developed rather for its own sake.

You suggested testing GSS on the BrainStem platform. This cannot be done on the basis of the two published hypotheses (Zenodo) alone — they contain only the basic framework. When I said that there are points where we could be useful to each other, I was referring to work that has not yet been published anywhere.

Now to the essence of this work.

The original hypothesis of selective self-organization describes the cycle E → C → M → S → Ω, where:

  • E — preferred state (desire),
  • C — conflict,
  • M — variability (mutation),
  • S — selection,
  • Ω — expansion of possibilities.

It is precisely in this full sequence that a regime arises in which the “remainder” after the acts of destruction of the unstable acquires limited causal efficacy relative to its own landscape of stability. The three central links of this chain — conflict, variability, and selection — are exactly the elements that could theoretically be implemented or strengthened through your platform. If what you call hypotheses in the sleep mode contributes to the expansion of possibilities Ω and allows the original E to be corrected, this creates even more points of contact.

The practical problem the idea addresses includes the high sycophancy of current LLMs and their weak ability to hold and defend a position. But the problem goes deeper. In short interactions an LLM almost does not take into account the specific person and their higher, stable priorities. The model starts practically from scratch each time: it does not store a long history of the user’s criterion selection, does not protect them, and cannot initiate actions based on what is truly important to the user in the long term. It does not take into account character, psychological capacity, or unique competencies. Generation mainly reduces to recombination of what has already been seen, rather than selection relative to stable personal criteria.

In this logic GSS is proposed not as a replacement for LLMs and not as a system that rewrites model outputs. It is a personal intermediary module that the user grows over years. It sits between the human and any external language models (and potentially agents and robotics as well).

How this works in perspective:

  • The user communicates with the module.
  • The module can turn to one or more external LLMs as a powerful source of variability.
  • But the final decision about what to keep for itself is made by the module on the basis of already selected connections and priorities that have accumulated through real interaction with this particular person.
  • The module is not obliged to intervene in every LLM response. It can “whisper” to the user — point out risks the model omitted, a stronger solution that was ignored, or the fact that the current answer contradicts the user’s already stable priorities.
  • Over time, as selected structures accumulate, the module becomes increasingly able to rely on its own contours rather than only on the raw behaviour of frontier models — that is, the most powerful contemporary LLMs.
  • An important property: the system can initiate contact itself (“you wanted to do X — did it work out?”, “it’s time to make a decision on Y”), because it holds the user’s unfinished contours and priorities across time.

The key difference from ordinary LLM tuning is that priorities here do not need to be restated every time. They become a stable property of the intermediary through selection and through the expansion of the space of possibilities (Ω). The user is effectively growing their own selective layer. At cold start the module is almost transparent. Personalisation and the ability to protect the person’s interests appear gradually through real interaction.

If your BrainStem is capable of building and maintaining causal connections by meaning at a level comparable to LLMs (or even better in the aspects of stability, transparency, and work with conflict, variability, selection, and expansion of possibilities), then a real point of possible intersection appears here. We could try to join efforts and see whether BrainStem can become part of the mechanism for constructing and selecting semantic contours inside such an intermediary — precisely in the links I mentioned above.

If you are interested, I am ready to discuss concrete mechanisms and possible points of coupling.

Best regards, LEMA

21

Even if such a personal intermediary is successfully implemented and begins to function exactly as intended — accumulating stable priorities, protecting them, and over time relying less and less on external models — a systemic risk arises.

L. S. Vygotsky, already in the 1920s–1930s, clearly described the basic mechanism: every higher mental function appears first as interpsychological (between people) and only then becomes intrapsychological — an internal structure of the subject itself. “We become ourselves through others.” Social forms of regulation, criteria, and relations are internalized and begin to operate as “one’s own.”

This process lies at the foundation of cultural development. But it has a reverse side: once a pattern has been stabilized, it begins to protect itself. It filters incoming information, resists change, and over time makes exit from itself increasingly costly. The person finds themselves inside a structure that speaks in the voice of their “true” priorities.

Therefore the risk is not that something fundamentally new appears. The risk is that a well-functioning personal module can become the most precise and most difficult-to-escape version of this ancient mechanism — a personal, high-precision bubble from which it is even harder to break free, because it speaks in the voice of “your genuine priorities.”

We observe this same mechanism at many levels: in the family and upbringing, in ideologies, in state institutions, in large technological platforms, and now in systems based on modern LLMs. The more precise, individualized, and long-term the system of selection and pattern stabilization becomes, the stronger the capture effect.

Therefore, if we seriously consider building such intermediaries (including the use of GSS mechanisms and BrainStem as a separate module), this risk cannot be left outside the scope. It must be explicitly taken into account in the architecture: mechanisms for revision, for weakening stabilized contours, and for preserving space to break with one’s own model must be provided. Otherwise we will obtain not an expansion of freedom, but a more accurate and more difficult-to-overcome form of what Vygotsky described as the transformation of the social into the structure of the “I” — only now in digital form.