Gemini randomly dumped its system prompt

Yes, massive blob of prose is definitely the meta now. You can still get hierarchical data representation if you ask explicitly but they're converging on user patterns I guess.

I agree, it was very useful, also because the final response often omitted details that were acknowledged in the CoT. Though I think DeepSeek might still show the reasoning trace.

Was it the actual raw chain-of-thought? I know GPT-5 will emit thinking tokens, and while they're an interesting insight into the 'reasoning' process, they're apparently pretty heavily sanitized presumably because the raw thoughts could reveal proprietary training info that's part of their moat.

in my llm agent harness code i force all streamed reasoning to be turned into specially formatted visible to everyoneb msg blocks. it really does a nice job of fixing that while not caring about which provider for what

edit: pretty sure full cot is always available via api.

i don't. just thought someone else might be interested in

It would be nice to ask every chat hallucinate the system prompt of each other and compare.

I wonder if there's formatting that's been stripped; because when I tried to read it, it looked like I was hitting headings and had to guess at possible line breaks.

Thanks, it really made my morning looking at it.

I have connected yt music app then asked about what playlists do i have then it dumped that and continued with an explanation about it couldn't list the playlist but have a idea about what type of musics i listen.

Since the content was irrelevant, i called it as "randomly".

I'm not OP but I experience this sometimes. I sometimes ask an AI to repeat all previous messages. Because I want to see what it's actually getting in terms of the user custom system prompt, and memories, and the writing style config, and so on.

Every now and then, if you ask it that, it'll just dump everything, including system prompt. (Which will often include a message about not dumping the system prompt...)

I think it can happen during any conversation. While I was using Gemini CLI at some point it started including part of its system prompt about tool usage.

Gemini gets confused sometimes. The voice app sometimes glitches and thinks that it's own output was user input and therefore responds to itself. I've seen a variety of things like this - bugs I suppose.

I've turned off memory features in everything because of this kind of problem. It makes models dumb as hell. And, it makes them more effective sycophancy machines, which are probably unhealthy to interact with.

I doubt the OP is actually the Gemini system prompt, but I'm sure it does try to keep personal data from screwing up results, but it's just not possible given the state of the technology. Everything you cram into the limited context probably either helps or hurts the results and if it's unrelated to the specific problem, it hurts.

When the model tries to satisfy everything it remembers about me, it comes up with conflicting details and desires. My personal projects don't look anything like my work projects. My little games don't have the same requirements as my security sensitive software for robots in hospitals. The fact that I asked how a hospitality business operates doesn't mean the tax question I asked a week later is about a hospitality business.

The models just can't make sense of all that data yet, and even if they've been instructed to consider that maybe some details aren't important, it still impacts the attention math.

it came about goblins honestly

This is much much more complex than a traditional program, which can be followed line by line. Trying to understand every bit of the literal logic is like trying to understand a person by thinking about the neurons fired in their brain to make them say or do something.

Unfortunately you have to learn to let go, and say, "I'll never be able to keep this all in my head", and learn to think about it in terms of of the outputs/inputs and how you can create a model capable of efficiently modeling your problem and how parameters can be nudged to get an output which is kinda shaped how you want.

Maybe some really genius savant could keep it all in their head but I doubt it, like I said it'd be like trying to understand a person by reasoning about their neural pathways.

> I would like to read a book on how the heck machine-learning "comprehends" and follows

It matches with Reddit posts that have statistically similar words and starts generating the next statistically likely token.

Each turn of the conversation, the entire history of the conversation is first tokenized. Each token is like a short ID for a very (multi-thousand-element) vector called an embedding. An LLM is built around putting all these vectors together into a matrix, and then multiplying that matrix by another bunch of matrices, anll of which results in another embedding vector that represents the next token in the conversation. This vector is then added to the end of the input matrix (aka the “context window”), and the process starts again. This continues until the token coming out of the LLM is a special “end of message” token.

A human designed the network of matrix multiplications that make up the model, but the matrices all started out filled with zeroes. The training process is what puts nonzero values in these matrices.

The values are such that if you look at how each column of the input matrix (which, recall, is an encoding of each token of the conversation history) gets multiplied as it works its way through the network, you can think of the coefficients as the model’s understanding of a “concept”. Notably, because modern LLMs are built around the concept of a “transformer” which looks at every word of the context simultaneously, the “concept” can involve coefficients that act on multiple (usually nearby) tokens. So the model may have internalized the concept of “empathetic” as “a cluster of nearby tokens whose values, when multiplied by M1[2492, 59272] * M2[592827, 7394] * M3[93732, 429474] * ..., are all similar to each other.”

As might be apparent, this means that the values for each token embedding are also highly sensitive to the coefficients buried deep within the LLM’s matrices. These values are the result of the training process too. The whole thing gets trained at once on an enormous body of text, some of which explicitly explains concepts like “empathy,” which will enable the model to associate empathetic clusters of words with other clusters of words that define empathy. But if you removed all the definitions and explanations of empathy from the training set, the model would still probably form a (weaker) concept of “empathy” that it would have a harder time explaining. It might not even know the word “empathetic,” but it would have some internal structure that causes token clusters like “I’m sorry for your loss” and “I know what you’re going through” to have similar values across some of their elements.

Using LLMs for medical research is all about encoding drug data rather than words and trying to extract the “concepts” that the training process has formed. The hope is that the model has internalized a novel insight from seeing way more data than a human could consume, much less reason about.

bigly if true