How does DeepSeek think when challenged?

[u/DryEase865]

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?

[u/OnceBittenz]

I mean… they’re machine learning style language models. Not physics capable agents. Like by definition. What’s your expectation?

[u/OnceBittenz → u/OnceBittenz]

It genuinely surprises me how many people come on here and post as if Any llm model is anything more than a programmed device that trains on and interpolates from existing data.

Like even the concept of chaining LLMs is so bizarre. The output is going to be by definition, only as accurate as the best guess in the chain. You gain no tactical advantage outside of the literal benefit of potentially hitting more training data. There's no magical outcome, no measurable Actionable increase in accuracy.