LLMs, thoughts and General Intelligence

I was thinking about LLM and what instructions it would require to make a certain LLM like say “I do not know” on a topic where the LLM actually doesn’t know something. But to actually have an understanding of something is not known or something is incorrect it’s not always straight forward. For instance, if there’s a statement “sun rises in the west”, a LLM can possible fact check and say it’s not correct but what about complex thoughts where it involves polynomial expressions and math where the the question about correctness is multi dimensional and one has to require multiple checks and point of views to accurately determine the solution. And this thought of checking correctness comes at the origin of “thought”. Being skeptic about one’s thought is where the road to truth lies. But that’s not how a LLM works, correct? If this is the case how does a LLM will reach general intelligence without actually having “thought”.

1. The Probabilistic Trap: Why “I Don’t Know” is Rare

An LLM is trained to predict the next token. If you ask a question, the model’s objective function drives it to produce the most plausible completion that resembles an answer found in its training data.

• The “Sun Rises in the West” Scenario: This is easy for an LLM because the negation of this fact is statistically overwhelming in the dataset. It is a retrieval task.
• The “Polynomial Expression” Scenario: This is a reasoning task. If you ask an LLM to solve a novel, complex polynomial, it doesn’t “solve” it; it mimics the pattern of a solution. It predicts the steps that look like a math proof.

Because the model is optimizing for plausibility rather than truth, it will often confidently hallucinate a wrong answer rather than outputting a low-probability sequence like “I do not have the capacity to verify this.”

2. The Missing Piece: Metacognition (Thinking About Thinking)

“Being skeptic about one’s thought is where the road to truth lies.”

In cognitive science, this is often called System 2 thinking (deliberate, logical, slow), as opposed to System 1 (intuitive, fast, pattern-matching).

• Current LLMs are almost entirely System 1. They are a “stream of consciousness” engine. They do not pause, reflect, or backtrack unless explicitly forced to.
• The Verification Gap: When a human solves a math problem, they might reach a step, pause, check it against a known rule, feel “uncertainty,” and try a different angle. An LLM generally generates the error and the subsequent steps with equal confidence (measured in log-probabilities), oblivious to the initial error.

3. How Can We Reach AGI Without “Thought”?

If LLMs remain pure statistical predictors, then they cannot achieve true AGI because they lack the mechanism for self-correction and truth-seeking.

However, researchers are building “scaffolding” around LLMs to simulate this “thought” process. Here is how the field is addressing the multi-dimensional correctness problem you described:

A. Chain of Thought & Self-Reflection

Instead of asking for an answer, we prompt models to “think step by step.” More advanced techniques, like Tree of Thoughts, force the model to generate multiple possible paths, evaluate them (self-critique), and discard the ones that look incorrect.

• Simulation: This mimics the “skepticism.” The model acts as both the generator (the thinker) and the discriminator (the skeptic).

B. Tool Use (The “Calculator” Approach)

For the polynomial example, we stop treating the LLM as a mathematician and treat it as a router.

• The LLM recognizes: “This is a complex math problem.”
• Instead of predicting the answer, it writes a Python script or queries a WolframAlpha API.
• The tool provides the deterministic truth. The LLM translates that truth back to you.
• Significance: The “intelligence” here isn’t knowing the answer; it is knowing how to find the answer.

C. Uncertainty Quantification

Engineers are trying to teach models to recognize their own statistical uncertainty. If the probability of the predicted tokens is below a certain threshold (high entropy), the model is instructed to trigger a refusal response (“I don’t know”) rather than guessing.

4. The Philosophical Hurdle: The “World Model”

The deepest debate in AI right now is whether LLMs build an internal World Model (a representation of truth and logic) or if they are just “Stochastic Parrots” (repeating surface statistics).

• The Skeptic’s View: If they are just parrots, they will never solve novel, multi-dimensional problems reliably. They will always need a human in the loop to verify.
• The Optimist’s View: At a certain scale, “predicting the next token” requires understanding the underlying logic. To perfectly predict the end of a math proof, you implicitly have to learn the math.

Summary

Current LLMs do not “think,” they “process.”

They lack the biological feedback loop of skepticism. To get to AGI, we likely won’t just make the models bigger; we will likely embed them in cognitive architectures—systems that allow the LLM to draft, critique, test, and revise its own output before showing it to you.

The “thought” won’t be in the neural network weights; it will be in the interaction between the network and its verification tools.