Partially, in the case of Story Generator, since the instruction passed to the LLM is outright "make four paragraphs, less than 400 words" as seen in the code, the output will be abruptly cut. A similar phenomena happens in AI Chat for example, where the order is "write ten paragraphs" but the code makes it so the displayed output is only the first one and the other nine are discarded. A "fun" consequence of this that happened repeatedly in the past with the Llama model and that still happens sometimes, was an output being literally just:

Bot: Bot:

As sometimes the LLM would put the input after a line skip, and the code would throw away the first paragraph due to how the pipeline works. Again, this is a very rare occurrence so it is not worth worrying about it.

Now... there is a bit more on this, but this is just speculation in my side, so take this with a grain of salt since I'm no expert in neural networks, nor in the particularities of some models.

DeepSeek (I still firmly believe that the new model is DeepSeek, even if some argue it may not be) takes some instructions more literally than others. Llama for example had absolutely no regard for length nor consistency in writing style, so you could have one output that was just a line or two, and then the next was a gargantuan thesis that would pretty much advance your story too far from comfort, to then go back to short replies. DeepSeek in contrast looks at the past inputs and tries to gauge how to control lengths. Ironically, something that DeepSeek does in longs runs is try to "extend" the output slowly, hence why if you audit summaries in ACC, AI Chat or AI RPG, you'll see first very short ones, while later they begin exploding into longer ones until reaching instability and derail in madness.

Also, believe it or not, the model takes all your input, it is not that it doesn't reach it, it's just that it decides to ignore it in favor of the context or where your story is because the primary instruction in Story Generator as well as in AI Chat or similar is "continue the story".

To me here is the biggest difference of the new model and the old one. Llama had almost "written in stone" what a story was meant to be and how to continue it from were you are standing (again, this is speculation from my side having a back catalog of massive logs done in AI Chat and seeing how things progressed there contrast to how they do now). The way Llama "thought" was the following:

• A story must follow the medicine/hero story formula.
• Check the last state and what was prior.
• If there are no stakes, nor clear goal, invent one via a "random happening".
• If there is a goal but no clear solution, present the "medicine" (random quest, magical MacGuffin, person to go kill).
• If the solution is being worked on, present a method (often "trials to obtain the MacGuffin")
• If all is solved, then there are no stakes, so rinse and repeat.

While on paper this should work flawlessly as you can put most stories under that formula, it was something that infuriated many users as doing something more "complex" such as adding unforeseen consequences to a method, betrayals, or stories that would not follow that formula was tricky. It was doable, but it required tricking the LLM into a state and making it do your bidding. And as it would require more maintenance and attention to context than just going "auto", it was something heavily complained in the past.

The new model however, has absolutely no concept of a "formula" for stories, allowing for absolute free-form, making DeepSeek process on how to deal with this task as follows:

• Check the state were the story stands.
• Parse the story prior until there is a precedent on how to continue it.
• If there is none, extrapolate from the data bank.

This is why two things happen: if you are in a state that is vaguely similar to something before, you'll experience endless deja vu, and if you are faced with the "unknown", there is the random chance of the LLM to pull a "dark scenario". Sadly, according to other users, the story itself seems to have precedence over explicit instructions of "no, do this instead", hence why running in circles forever is a bigger threat and can happen as early as a 20kB log as today (current record of mine at the fourth input in ACC Chloe).

We can hope that this all is improved in the future, but that's more or less why things happen in my opinion. At least with the new scheme, and seeing how some succeed where I and others fail, I can only deduce that the best way to make the new model "work" is via interpolation, meaning, give it a "target" in the description as "the story purpose is to X get Y, or Z to happen", so when parsing through the data bank, the LLM will select a similar case as were you are standing and work on it without derailing, granted, this removes completely the "surprise" element, but it's a decent workaround. Then again, always check the story as is, since the "running in circles forever" is a bigger threat I believe.

Anyways, sorry for the long posts, and good luck in your runs!