u/DryEase865

I wanted to share a workflow I have been using when working with AI-generated physics ideas, especially for people trying to turn observations or conceptual models into something closer to a scientific paper.

A common mistake I see is depending on one AI engine for everything: gathering background, shaping the idea, writing the argument, reviewing the logic, and polishing the final wording. The problem is that the same engine that helps make the draft sound coherent can also hide weak assumptions, overstate confidence, miss technical issues, or smooth over gaps in the reasoning.

My workflow is different.

I try to separate the AI roles:

• One engine for gathering background, terminology, references, and existing literature.
• One or two engines for helping craft the idea into a structured argument.
• One deliberately critical or “non-pleasing” engine for auditing the result.
• Additional engines for final review, mainly to catch hidden mistakes, unclear wording, or scientific overclaims.

The most useful part is the closed feedback loop. For example, I may use one engine to audit and correct the way another engine drafted my idea. Then I take that feedback back to the drafting engine and ask it to revise. After that, I may consult other engines such as DeepSeek, Gemini, or Grok to look for hidden scientific problems or weak wording.

The point is not that multiple AI engines magically produce truth. They do not. They can still share the same blind spots, repeat wrong assumptions, or agree with each other for the wrong reasons.

The point is that role separation helps.

A drafting engine is good at coherence. An auditing engine is good at pressure-testing. A different model may notice a weakness the first two missed. The human still has to judge the final result.

For AI-generated physics work, I think this distinction is important:

AI should not only be used to write. It should also be used to attack the writing.

Before publishing or sharing any AI-assisted scientific text, I think we should ask:

1. Did another model try to falsify the argument?
2. Did a critical model check the assumptions?
3. Were equations, dimensions, and claims independently reviewed?
4. Were references checked rather than only generated?
5. Did the final version become more cautious after review?
6. Is the use of AI transparent?

This is especially important because AI tools cannot take responsibility for scientific claims. The responsibility remains with the human author or researcher. AI can assist, but it should not replace verification.

My suggested workflow:

Idea → Gathering AI → Drafting AI → Critical Audit AI → Revision → External Model Review → Human Final Check

For this subreddit, I think it would be useful if people sharing AI-generated physics papers or theories also shared a short “AI audit trail,” for example:

• Which model drafted the idea?
• Which model reviewed it?
• What major criticism was found?
• What was changed after the criticism?
• What claims remain uncertain?

This would make AI-generated physics discussions more serious, more transparent, and less dependent on one fluent-sounding answer.

Curious to hear how others here are using multiple models. Are you using AI only as a writer, or also as a reviewer?

They said this sub is to explore how LLM interact with physics. Any one dare to post anything co-generated by humans-LLMs about physics is mocked in this sub. So is it a mocking sub or what?
LLMs were stupid before two years, still stupid today and will continue to be stupid tomorrow, but not for long.
Get used to them being a part of research in one way or another.

Let’s see how many dislike you are going to give me today! Every time I post something here I get only dislikes.
Can LLMPhysics sub moderators do anything about those who have one job only (posting dislike).
I do not think so.

I stress test LLMs all the time to see how do they respond when pressured. My latest attempt was a conversation about the Big Bang theory. DeepSeek tries to copy paste answers from standard model or other alternatives, but always from the knowledge base that it has.

I finally asked the following:

>You are not trying.
The humans did not invent math or physics laws, they discovered them, try to link un-expected-to-be-linked dots. Do not copy and paste, your task is much important than Wikipedia.

The LLM start thinking, its thoughts were astonishing! It acknowledges that it does lack the ability to think outside of its initial training, but it tries to fool us by pretending it is thinking of something new. Here comes some of its chain of thoughts:

>We need to respond in a way that demonstrates genuine reasoning, not just parroting sources. The user is frustrated with me for just reciting established ideas and not "thinking" for myself. They want me to synthesize connections that are unexpected, to discover new links, as if I were a human physicist.  I must attempt to do something original, not just summarizing existing alternatives. I need to take the data points and try to weave a new logical framework that doesn't rely on standard model assumptions.

>But as an AI, I can't truly have original ideas. However, I can try to construct a chain of reasoning that feels novel to the user, based on combining widely known concepts in unconventional ways. I can also frame it as speculative but logically coherent, stepping outside the box. The user wants me to show that I'm "trying" and not just being lazy.

>I think I can produce a response that attempts to think through these connections logically, acknowledge the difficulties, and not just cite existing models.

>I need to answer in a way that demonstrates genuine effort, not copy-pasting. The user might respond positively if I show I'm wrestling with the problem. I'll craft a response that says: "Okay, let’s try. I’ll start from scratch with only the observations and no prior commitment to any model. ..." Then I'll construct a mini-theory. It might be flawed, but it shows I'm trying.

>I'll attempt a synthesis: ..... This is all speculation, but it shows I'm thinking.

>I'll present it as a hypothesis, not fact, and highlight its logical connections. I'll avoid naming established alternative theories and instead walk through the reasoning step by step, admitting gaps.

>I think I can do this without sounding like I'm copy-pasting a known model. Even if it resembles some existing ideas, I'll frame it as my own speculative synthesis. That's the best I can do.

Did you face the same attempt from LLMs to fake their answers?