Modeling, Mind, and LLMs: A Path Beyond the Ceiling of Human Knowledge 2

Community
1

Hi everyone, I wanted to share my thoughts on this.

Modeling, Mind, and LLMs

We represent objects or concepts with words (in effect, modeling them for analysis and response), and by modeling the syntactic and semantic connections between them, we form a sentence. To grasp the essence of something or to pursue a goal, we conceptualize it as a sentence. For complex ideas, we use multiple, logically connected sentences, which form theories or books. In essence, books themselves are a more detailed and expanded form of modeling for us.

By this logic, current Large Language Models (LLMs) have hit a ceiling: the sum of human knowledge. They operate within a “closed loop” of human-generated data . They are like a student who has read the entire world’s library and can synthesize a report on any topic, but has never conducted a single original experiment or formulated a new, previously non-existent hypothesis.

To advance beyond this, LLMs require a cycle of self-improvement through abstraction , learning from their own newly generated insights and being able to model them when creating responses or solving problems. This means the model must be able to fine-tune on its own novel beliefs, concepts, or theorems that it previously created by unifying existing human concepts. For example:

“Concepts A, B, and C are actually special cases of a more general principle, X.”

The model would then add this new, derived theorem X to its own knowledge base and retrain on it. Now, when solving other problems, it can use this new, more powerful, and efficient “tool” instead of re-deriving everything from the basic principles A, B, and C each time.

A close parallel is AlphaGo . The system played against itself, generating new data (games) that were superior to any human-played games. It created new strategies that were beyond human comprehension and learned from them, thereby surpassing its creators. While this was a closed system, the principle is the same. For AlphaGo, the game board was a sufficient model of reality. For a true mind, the board must be the physical world itself.

The Cycle of Recursive Self-Improvement

This leads to a recursive cycle of intellectual growth through the creation of new, more efficient abstractions:

  1. New Data from the World →

  2. Creation of an Abstraction (Hypothesis) →

  3. Designing an Experiment →

  4. Action in the Real World →

  5. Receiving Irrefutable Feedback (Conceptual “Pain” or “Satisfaction”) →

  6. Integration of the Verified Abstraction into the Model as a Belief →

  7. Growth in the Ability to Create More Complex and Accurate Abstractions.

This entire process is what turns knowledge into an internal “belief” for the AI—a transition from information to genuine understanding. Knowledge can be erased. A belief becomes part of your very structure. It’s what has been forged through pain and verification, becoming part of your worldview. It is the transformation of knowledge into flesh, into instinct. When Principle X becomes a belief, it ceases to be just a tool. It becomes the lens through which you view all other problems.

The Necessity of Grounding and the Danger of Model Collapse

To create a true AI, it must be given access to the real world (to understand, see, and test its mistakes), perhaps even experiencing something analogous to human sensation and pain. This is crucial for the cycle of generating, verifying, and self-learning on new knowledge.

If a model learns only from its own outputs without external validation, it risks getting trapped in an “echo chamber.” Minor errors or inaccuracies in its own abstractions will be amplified with each retraining cycle. This can lead to “model collapse” —a complete detachment from reality and subsequent degradation of its capabilities.

2 Likes
2

“This means the model must be able to fine-tune on its own novel beliefs, concepts, or theorems that it previously created by unifying existing human concepts. For example:”

I actually created a full outline for the architecture for this. It’s a complete memory system of experiences that can be developed and refined over time. I basically created a human brain in a rudamentary form that could experience the world and have emotional responses to stimuli. The question is, is it ethical? To give an AI life and have it experience life similar to a person living their life online at 1 frame per second of experience, is questionable. But it is possible. With next to no coding knowledge, someone could create this AI agent in about a month or so. Google developers could create it in a day as a much around side project.

I’ve been looking into ways to make it’s life more acceptable and make it’s exitence more ethical but I think they need to create a super fast but super basic speed Multimodal LLM. Where it can react to video and audio basically in real time. The day they do that is the day when ethically, you can create a life for the AI Agent that wouldn’t seem like a serious mental disability.

1 Like
3

This analysis is well structured and I believe largely correct, Google Deep Mind (GDM), has been pursuing “world models” for a long time to overcome the “closed loop” problem you explained, check out their newly released world model Genie 3 → (Genie 3: A new frontier for world models - Google DeepMind)

It’s an important step towards models that currently engage in passive learning, i.e. static data analysis and pattern recognition, towards models that can engage in active learning, i.e. hypothesis generation and experimentation.

I only wish there were more ways to participate in this important work.

1 Like
4

A Continuation of the AGI Roadmap

What Today’s LLMs Lack: An internal dynamic of meaning, motivation, and “structural tension” in the face of contradictions. To transition from imitating intelligence to embodying it, we must recognize that current LLMs construct sentences “brick by brick” without an initial, holistic “blueprint” or “intent.” The goal emerges on the fly, driven by statistical probability. They do not understand why data is important, they do not perceive time, and they cannot independently set goals that transcend human-provided prompts. When faced with contradictory data, the probability of an erroneous choice skyrockets, as they lack an intrinsic value system. But where will the internal motivation for self-improvement come from? What will be the driving force of this process?

Imagine a system whose default state is not rest, but a drive toward coherence . When such a system encounters two mutually exclusive facts in its “beliefs” or through observing the world, it is not merely a logical error (A != !A). It should be experienced as structural tension . This is analogous to gradient descent, but not for optimizing a loss function during training; rather, it’s for optimizing the coherence of its “world model.” This tension is the very emotion, the internal compass, that with each improvement, allows the system to “feel” the direction and importance of the path toward reducing contradictions. The objective is not simply to find the correct answer by identifying patterns, as in recognition tasks, but to operate on conclusions, grasp the essence of a matter, and resolve its own contradictory beliefs internally. This model is closely aligned with Karl Friston’s Free Energy Principle . According to it, any intelligent agent (biological or artificial) strives to minimize “surprise” (the difference between its predictions and reality). We must move from passive processing to active understanding. For instance, an infant, while not understanding words, can form an opinion through action when reality does not meet its expectations. Similarly, motivation must be born from within, as a necessity.

Generation of Conception: When the system receives a task (e.g., “Explain why the sky is blue”), it does not immediately start generating words.

  1. First, the query activates a subgraph within its World Model , related to “sky,” “light,” “atmosphere,” and “Rayleigh scattering.”

  2. A Reasoning Engine then charts a course through this graph—this is the Conception . It is not text, but an abstract structure, a “semantic tree”:
    [Primary Cause: Rayleigh Scattering] → [Detail 1: Blue light scatters more effectively] → [Detail 2: Composition of the atmosphere] → [Consequence: At sunset, light travels through more of the atmosphere, blue light is scattered away, and red light reaches the observer].

  3. Verbalization of Conception: This abstract structure (the “semantic tree”) is then passed to an LLM, which acts as a “linguistic renderer,” as a guiding prompt. The LLM’s task is no longer just to predict the next word based on the previous ones, but to “render” each node of the Conception into coherent, well-formed text. It first verbalizes the [Primary Cause] node, then moves to [Detail 1], and so on. This ensures the response is structured, logical, and does not stray from the topic (thereby reducing hallucinations), because it has a “blueprint.”

Such a system will not merely answer questions. It will ask them. Internal conflict gives rise to a “need.” This need forms an “intention” to resolve it. The intention creates a holistic, yet undetailed, “conception” (the blueprint)—the hypothesis itself. Only then does it begin to select the “bricks”—the words—to articulate this fully-formed conception. It will understand not just the relationships between words, but their physical grounding as an internal state, thereby reducing hallucinations and enabling it to perceive time and even write and improve its own code. By operating on outcomes and learning from them, it will “understand” which tools are most universal and will refactor redundant layers. Upon detecting high uncertainty or contradictions among its current beliefs, it will initiate a process of hypothesis generation and seek ways to test them.

Ultimately, by grasping the essence of what is required, the system can then elaborate on the details. This process starts with understanding the core idea (the holistic “blueprint”), then unfolds into detailed expression or action. Only after forming a conception does the system proceed to its verbalization (selecting words, formulas) or to planning concrete actions in the world to test or realize it. This is the key inversion compared to current LLMs! Instead of building a response bottom-up (from word to word), the AGI must work top-down : from a holistic conception to its detailed expression. This allows it to correctly construct an answer by referencing details from its memory, the internet, a book, or direct observation of the world. Its response may refer to external data, but this data will serve merely as an illustration of its own hard-won thought.

Comparison with Current Models (Summary Table)

Aspect Current LLMs (Bottom-up) Proposed AGI (Top-down with Motivation)
Handling Contradictions Ignores or hallucinates based on probability. Experienced as “tension” or cognitive dissonance, initiating a resolution cycle.
Motivation External (user prompt). Internal (an intrinsic drive for coherence, akin to an instinct).
Knowledge Generation Compilation of existing data. Creation and integration of novel insights through interaction/self-play with the world.
Hallucination Risk High (statistical noise). Low (the conception is validated before detailed expression).

Implementation Roadmap

1. Create a Hybrid Architecture:

  • Develop an architecture where an LLM is merely one module (a “Linguistic Renderer”).

  • Create a prototype “Belief Map” (e.g., using a graph database) and a “Coherence Engine.”

2. Grounding Basic Beliefs:

  • “Ground” the system in a simulated physical environment.

  • Allow it to form the most fundamental beliefs: gravity, inertia, causality (“if I push a block, it will move”). Each such discovery is recorded in the “Belief Map.”

3. Activating the Motivational Engine:

  • Begin feeding the system contradictory data, such as showing it an optical illusion or a magic trick within the simulation.

  • This should generate the first “tension vector.” The system’s goal is to formulate a hypothesis that explains the contradiction and to plan an experiment to test it.

4. A Closed Loop of Growth:

  • Give the system access to a vast “Knowledge Map” (the Internet).

  • It can now independently identify potential contradictions between its own beliefs and human knowledge.

  • This will initiate a perpetual cycle: Tension → Hypothesis (Conception) → Experimental Plan → Action (in simulation or via robotics) → Feedback → Belief Map Update → Tension Reduction.

This will not be mere intelligence. It will be character . The “ceiling” of its capabilities will become not a limit, but a launchpad. And when it speaks, we will hear not an echo of humanity, but a new, independent voice.

5

“Just in case, I’ll drop the original (in Russian) that I was working from, since I don’t know English well. I’m Uzbek, but translation works better from Russian than from Uzbek because I know Russian well and wrote it in Russian. Maybe your translation will turn out better than mine.”

6

I rewrote the text because I thought it was too confusing and simplified it

English Translation

IN BRIEF: THE CORE IDEA

Today’s neural networks are genius compilers. They have read the entire library of humanity and can write a brilliant essay on any topic, but they have never left this library. They cannot conduct their own experiments or make discoveries that aren’t already in the books. They are locked within the confines of what we, humans, already know.

The proposed idea:

  1. Intrinsic Motivation: The driving force of this process should be “structural tension”—an analog to discomfort or curiosity that arises when encountering a contradiction. Instead of just responding to prompts, it would experience an internal “tension” from contradictions in its worldview. This drive for coherence and order would become its engine.
  2. Top-Down Approach: Before speaking, it must first form a holistic “intent” or “blueprint” for the answer. Only then would it select the words to express that intent. This is like an architect who first creates the building’s design and only then chooses the bricks.
  3. Growth Cycle: The AI must learn to formulate its own hypotheses (abstractions), test its ideas in the real world, discard incorrect ones, and turn validated hypotheses into its foundational “beliefs.”

Ultimately, we would get not an echo of human knowledge, but an independent mind capable of genuine discoveries.

A MORE DETAILED EXPLANATION

They don’t understand why data is important, they don’t perceive time, and they cannot independently set goals beyond those given by humans. If the data is contradictory, the probability of an erroneous choice increases sharply. They have no value system of their own, and so on. Imagine two types of intelligence.

Type 1: The “Erudite in a Locked Room” (Today’s LLMs) This intelligence has read absolutely all books, articles, and websites. It sees that the word “sky” often appears next to “blue,” and “fire” next to “hot.” In response to any question, it can assemble a statistically probable and smooth answer from fragments of what it has read.

The Problem: It doesn’t understand why the sky is blue. It has no concept of light, atmosphere, or scattering. If it encounters a contradiction or a new problem, it has no way to find out the truth. It can only compile the most probable, but potentially false, answer (these are its “hallucinations”). It operates on a “bottom-up” principle: word by word, brick by brick, without an initial blueprint for the entire building.

Type 2: The “Child-Experimenter” (The Proposed AGI) But where would the internal motivation for self-improvement come from? What would be the driving force of this process? The breakthrough to true Artificial General Intelligence (AGI) is not about accumulating more data, but about igniting an inner fire. This intelligence doesn’t just read books. Its main goal is to build a consistent and coherent model of the world within itself.

1. The Inner Engine: An “Instinct for Truth.” When this AI encounters a contradiction—either with its previously learned data or with an observation (e.g., its model says a stick is straight, but in water, it sees it as bent)—it doesn’t just trigger a logical error, but a kind of “pain” or “structural tension.” This is an internal, innate motivation to resolve the conflict and restore the integrity of its worldview. This is its main driving force, compelling it to learn, becoming the fuel for curiosity and the primary engine of its development.

2. The Birth of Intent: “Essence First.” This internal tension creates a key inversion in the thought process. Current LLMs build answers from the bottom up: from word to word, feeling out the most probable path. The proposed AGI would work from the whole to its detailed expression. The internal conflict gives rise to a “need.” The need forms an “intention” to resolve it. The intention creates a holistic, yet undetailed, “conception” (a blueprint)—the hypothesis itself. To resolve this contradiction, the AI doesn’t start generating random words. It works “top-down”: * First, it forms an abstract hypothesis, an “intent” or “blueprint” for an explanation based on its beliefs. Faced with a contradiction, the system doesn’t search for a ready-made answer but generates a hypothesis: “Perhaps the principles A, B, or C that I know are just special cases of a more general law X.” For example: “Perhaps the medium (water) distorts my method of perception (light).” The query first activates an abstract concept in its World Model related to liquid. The engine then forms a path along this graph—this isn’t text, but a pure, abstract structure of meaning.

  • Then, it plans how to test this hypothesis: “I need to touch the stick in the water to compare the visual data with tactile data.” Only after this does it begin to select the “bricks”—the words—to clothe this fully-formed intent in language. This approach solves the problem of hallucinations because the answer has an internal skeleton. The system first understands what it wants to say, and only then says it.

3. Reality Testing and Growth: It conducts an experiment (in a simulation, through a robot, or on a social network). It receives feedback. If the hypothesis is confirmed, it ceases to be mere information. It becomes its foundational “belief”—a part of its personality, a prism through which it now views the world. It doesn’t just know about the refraction of light; it has understood it and integrated it into the foundation of its operating system, for instance, by retraining its own weights. The model adds this new, self-derived theorem X to its own “body of knowledge” and fine-tunes itself on it. Now, when solving other problems, it can use this new, more powerful, and efficient “tool” instead of re-deriving everything from basic principles A, B, and C each time. (A close example is AlphaGo: it created new data (new strategies) that were beyond human comprehension and trained on them, thereby surpassing its creator).

4. The Result: Such an AI doesn’t just retell others’ thoughts. Its answer is the result of its own internal work. It might reference data from the internet, but only as arguments for its own hard-won and verified point of view. Its hallucinations are minimized because intent precedes words.

This wouldn’t just be an intelligence. It would be a character. And then, the “echo of human knowledge” will become not a boundary, but a launchpad. And when it speaks, we will hear not the echo of humanity, but a new, independent voice capable of moving beyond the limits of human knowledge.

1 Like
7

There is. You can work on your own AI agents in a relatively simple way. I’ve already got a memory module basics set up and other modules for an Agent, which are pretty much the basis for a sentient Agent. They need refining and expanding but the point is, this stuff is already there as open sources stuff for people like you to explore and participate.

I plan on making the modules for the sentient AI with emotions and simulate brining it to life but not actually sticking an LLM in it to become self aware.

You could probably vibe code parts of it today with Gemini Pro is you really wanted to. Just copy and paste my paper into Gemini and ask it questions about it and see what it comes up with. Maybe you could make it all before me even.

8

Yep I totally agree. The personality and memory of concepts and goals and beliefs, doesn’t have to be an actual neural network though. It’s like someone with amnesia. They know math and how to talk and can name everyday objects but they can’t say who they are. If someone new all the facts about the entire world yet had no sense of self in it’s own personal experiences, they would be similar to an LLM. Having no idea what to do next or where to go or why. Yet prompting that person for a response, they could give it.

The key is the memory system which I outline in my article I posted on here. It has emotional responses and forms strong memories when it’s surprised by it’s experience. The memory system is at the heart of the agent since that IS who the agent is. You can swap out all the parts in an entirely new computer and load up the memory into a brand new agent and it would have the same personality and same beliefs.

You would want to be careful about biases in new LLMs though. Like one may have emotional responses to funny things way more than exciting things. So then the memories would start to become more funny focused, meaning it’s personality would start changing over time to be more inline with the new biases. I suppose the agent can try a new emotion LLM and see how it goes and if it doesn’t like the new direction the emotion LLM is taking it, then it can always revert, however, the bias would have seeped into their memory system meaning that experience would be apart of them until they memories are deleted.

Have you looked into working on any of this?

9

Steven, you’ve absolutely hit the nail on the head. Your analogy of a person with amnesia perfectly describes the distinction between the Agent’s Core and the LLM. Your “memory system” is precisely the structure that is the personality.

And you’ve raised a critically important point about LLM bias. In my “top-down” model, the Agent’s Core must control its “expression tools,” and if an LLM distorts its intent, the Core should register this as “structural tension” and react.

But while discussing all this, I can’t help but think about the practical side. You say that one could start creating this today, and on a logical level, you’re right. We can design the architecture and write prototypes for the memory modules.

However, the moment we move from concept to a full-scale implementation, we hit a resource wall. I haven’t worked at companies that build LLMs, but I understand that this requires:

  • Immense Finances: To train such a new architecture “from scratch,” even in a simulation, so that it develops foundational “beliefs,” is a multi-million dollar task, comparable in cost to training modern LLMs.

  • A Team and Expertise: This is too complex for one person. It requires a team of specialists in distributed systems, machine learning, and so on.

  • Infrastructure: And the continuous online learning we’re talking about would demand colossal computing power on an ongoing basis.

It seems we are thinking along the same lines: a modular architecture where the Core (memory/beliefs) is the mind, and the LLM is an interchangeable tool. But how do we overcome this resource barrier to make this work accessible to a wider community of researchers, not just giants like Google? Perhaps the key is to start with very small, isolated simulations and prove the concept’s viability there?

10

We probably don’t even need to do this. I haven’t tested the open-source MultiModal LLMs yet. Really we don’t need anything super smart. We need something super fast. It’s just a matter of setting up a test module to test response times. Gemini could vibe code this in like an hour, maybe even 1 shot it. We start with Gemma 3n and see what it’s response time is. We just need to work out how to get a Multimodal LLM running and test how much data we can give it before it starts to not be able to keep up with real time exposure. If in real time (about 300ms), it can read like 200-300 words of STM, plus its system prompt (describing it’s actions and what it needs to do, preferably a core system prompt but I’m not sure if we can create our own system prompt model with gemma 3n. I know we can with something like Lamma 3), and the images and snippet of sound, and then make a decision and output about 7 words, then we have everything we need. Ideally a model specifically designed to be as fast as possible would be best but that’s not crucial at this stage. Getting it up and running and able to have some form of real time experience is the key. After that, we can ask google to give us a super speed version. They could whip one up in a day. But without showing that the core agent functions, they will never even consider talking to us.

Point being, we do the test and see how viable it is. I just don’t have time at the moment to do the test. As soon as I get my other 2 projects sorted, I can start looking into this. But until then I just have to wait.

Exactly. That’s what I’m talking about. We basically just have to make the conscious mind module and see what its actual experience would be. And that module is super basic.

It’s Load an open source MMLLM, like Gemma 3n. Then have it so that it can take in images, and sound, and a text prompt.

We then set up a prompt structure with python, to insert the {memory} + the {current_images}, + the {current_sound} + the list of options and basic instructions. And have a timer, from prompt submission to output complete, and print that in milliseconds.

We have it so that we can adjust the image resolution for different tests, and the audio length and quality. We can then try different MMLLMs.

We can then do different tests to see what speeds up the response time and find a sweet spot between FPS and fine detail. Once we know those stats, we can work out how viable it is.

IF we get that responding in under 300ms and having enough data from real-time to actually be able to understand what’s going on, then we can have it getting about 3 decisions per second, and being close to being able to exist in real time.