Ghideon

1: how do you access "ChatGPT5"? (OpenAI has as far as I know not released a version 5 of their models yet) 2: what does "ChatGPT5" mean here? A service, a model, other? (I have seen this name in use for services that seem to be from parties that are not OpenAI*) *) I'm avoiding links to external sites unless asked.

Thanks! Then I have enough understanding of the setup to comment on: An LLM, AI (generative AI or any other kind currently available) is obviously not conscious so I don't see any connection to "consciousness" when discussing the hardware and software (in any combination AI or other). So dropping that question is a good move since the answer is trivially "no". As for the hypothesis I'm not sure what you are looking for here: 1: We know that the LLM is immutable when used for inference in for instance a chatbot; by definition the user prompts used as input to the LLM does not change the model. So there is no need to form a hypothesis or test since the answer trivial; prompting does not change an LLM; no matter how intricate or advanced prompts are. 2: One purpose of generative AI and the usage of LLM is to generate output* that mimics text a human could produce, and to do so based on user input. So of course the output of the LLM is influenced by the user's input. (This is an area of ongoing research for instance if impolite prompts result in poor performance or not) So the question is: what do you wish to form a hypothesis about? It can be the LLM in isolation, the complete system such as a publicly available chatbot service, or some other alternative. In the case of a chatbot there are mechanisms outside of the LLM that needs to be considered. For instance the system prompt has impact on how the users's conversational style influences the output of the LLM. Some notes about testing where the above distinction makes a difference: If the hypothesis is about LLMs we can run tests on one or more of the available open models in an environment where we have full control of all parameters (random number seed, system prompts, temperature, repetition penalty, ...) If the hypothesis is about a product or service we (most likely) do not have access to the system prompt and other mechanisms between the user and the LLM. Here tests will be a little more "black box" and maybe more related to jailbreaking** than parameter settings. Reproducing a test result may not be possible due to updates of the service or simply by the lack of access to random number seeds and temperature settings. Hope this makes sense, otherwise feel free to ask for clarifications. *) @TheVat provided a good summary above; no need to repeat **) Tricking or manipulating a large language model (LLM) chatbot into producing responses that it is normally programmed to avoid, often by circumventing built-in safety filters and ethical guidelines. This may not be allowed by license agreements and vendors will typically update software to prevent known issues.

That site seems based on OpenAI's GPT-4 architecture Ok! Do you use a predefined set of prompts? Or does the prompting (and hence the effect) depend on the human interpretation and prompting?

Just so I address this correctly; is the following hypothesis still something you wish to test using an LLM? Or is the above replaced by; Is the hypothesis about a specific LLM? LLMs with some specified properties? Any LLM? Since an LLM is not changing, what does "alter the internal logic pathways" mean? As for the approach: Is the following a correct overview description? I am interested in the core of the approach to get rid of misunderstandings (on my part) prepare a text based prompt Send the prompt as input to a software that uses an LLM to generate a response for the prompt. Show the result to the user. In your case the prompt is pretty advanced, by still "only" a prompt. The LLM acts only on the prompt given by the user; there is no interaction with other tools os systems and there's is interactions with other users' sessions. The output is not modified; all output to the user comes from the LLM

But that seems to contradict earlier statements and explanations.

Chat-based tools currently available to the general public (for instance ChatGPT*) allows selection of a language model (LLM), supplementary tools and integrations, user configuration (including for instance access to chat history or a persistent "memory") and the outcome and usefulness of the interaction is greatly affected by the selections made by the user. The same holds for local installations of for instance open source tools and models. In the context of Prajna's question it makes, as far as I can tell, a difference if the discussion is about mechanisms that change the LLM or not. *) Differences exists depending on market, type of license etc

No, the LLM does not do that. But there might be surrounding software that performs such actions*. The input to the LLM can then be the user prompt + whatever additional information the surrounding application(s) supply. (There's a lot of details and nuances to be added; I'll wait for Prajna to avoid going off topic) *) For instance a chatbot that behind the scenes access a web search or some stored information such as chat history. The user does not necessarily see it as anything separate.

As far as I know an LLM is static; a fixed set of weights and definition (transformer layers, attention heads, activations). Once trained, the LLM’s weights and it's definition do not change at inference time. So when you say "alter" do you refer to fine-tuning, training, destillation or some other means of altering the LLM? Or do you by "LLM" mean the complete system with LLM + one or more surrounding applications may maintain state (conversation history, retrieved documents, random seeds ...) and possibly integrate with other software systems?

Ok, take your time and read through the question, the quotes and the answer. I think it is rather obvious so I leave it as an exercise. I prefer to discuss here on the forum where other members can correct any misunderstandings or mistakes on my part.

Ok. But what you wrote contradicts the quotes I asked about.

What is this weird format? Copy/paste? (looks like markdown, byt why?)

Thanks, but I posted two quotes from your explanations; please explain how they are not contradictory.

Missed that question. I think the issue is quite a bit bigger than finding suitable words. Assuming the goal is to come up with an acceptable model for "pre-geometry" with properties such as: Recovers known physics Mathematically rigorous Avoids metaphysical slippage Then (with my background and current level of knowledge) I would probably need to do at least the following: 1. Study Philosophy Purpose: Understand scientific realism, instrumentalism, model-theoretic realism, ... Why: need conceptual clarity to avoid building metaphysical assumptions into a pre-geometric theory that claims to avoid them. Also need to understand the "big picture" where this kind of science fits into other areas and their solved or unsolved questions. 2. Study Mathematics Purpose: Build mathematical fluency in both quantum and geometric formalisms. Why: Pre-geometric models, as far as I know, often rely on non-standard mathematical structures that I think removes many standard tools. 3. Study Physics; the Established Theories Purpose: Gain full technical command of existing frameworks. Why: understand what needs to emerge from the model 4. Study Pre-Geometry: History, Branches, Key Issues Purpose: Avoid reinvention and engage existing debates. Understand known pitfalls; Recovering Lorentz invariance from discrete models, Matching semiclassical limit to GR, Preserving unitarity and causality 5. Study current Status of the Field, for instance researchers* such as Leonard Susskind Juan Maldacena Carlo Rovelli, Abhay Ashtekar Rafael Sorkin, Fay Dowker Renate Loll, Jan Ambjørn Daniele Oriti Brian Swingle, Mark Van Raamsdonk Programming and Simulation (One of the few areas where I to some degree could rely on existing knowledge) I believe (but I do not know) that numerical models, simulations and tests are needed I estimate this would span several years of full time work. The result is a knowledge level that allows one to choose between competing models and where to try to contribute. Part of it may require re-location and looking for a position at a university or similar institution. As far as I know no generally accepted and tested model exists for "Pre-spacetime" or "pre-geometry" despite all the efforts by researchers so far this phase of the work could be a life-long endeavour. I also do not expect the result to contain any "chortons". Note: this post is not meant to intimidate, it's just an honest attempt at describing the situation. Also, I do not think any current LLM or chatbot could significantly reduce the time and effort required. *) list from a quick googling of the scientific concepts I think is relevant.

and Using seconds (unit s-1) implies time? But before time there was no time (Note: I can live with a few spelling and/or grammar errors; can you post an explanation that is not produced or supported by an "AI ChatBot"?)

Your conclusion seems reasonable.

That may not work so well if it contains text; searching, quoting or highlighting does not work well in the forum.

As I said earlier: look up ontological bootstrapping

You have not provided any coherent model for the emergence of spacetime. Building new speculations (for instance about expansion) on top of that does not work.

Today I learned about the concept ”non-algorithmic” in relation to computability, Turing machines and Gödel. I don’t remember seeing that concept when studying the ”opposite” (algorithms as part of computer science); my focus was more on software engineering. I think this new knowledge will help me navigate when reading AI related material. Especially philosophical and more speculative stuff. (As always thanks @joigus for using words, concepts and examples that trigger searching, reading and learning)

Quick example: the post where the quote above is taken from. (If you understood “ontological bootstrapping problem” you would have spotted it before posting?)

To better understand the clear critiques other members have posted you may want to look up and read about “ontological bootstrapping problem” (using tools that can’t logically exist at the model’s starting point). That may give you valuable insight about the overall problems with the explanations you post. Also, there is a risk that AI usage reinforces this. The LLM does not contain any non-spacetime model about ”chortons” and will likely fall back on spacetime terms, even if they logically does not apply.

Layman question: Without units in mathematics, how do you distinguish between radians and degrees in geometry?

Testing an editor \[ \frac{\pi}{4} = \sum_{k=0}^{\infty} \frac{(-1)^k}{2k+1} = 1 - \frac{1}{3} + \frac{1}{5} - \frac{1}{7} + \cdots \] \[ 4* \sum_{k=0}^{\infty} \frac{(-1)^k}{2k+1} \]

I use the sandbox section in this forum to test mathematical formulas and latex.

That is absolutely a possibility! @swansont commented on that and a similar scenario in a recent thread https://scienceforums.net/topic/136269-flood-of-spam-12th-july-2025-why-would-someone-do-that/#findComment-1293102