Distilling LLMs are a reversal of that.
I don't frame its prompt as antagonistic though - I've found in the past (with weaker models, so YMMV) that this can be overly officious, sometimes blocking more creative outputs that you'd want to retain.
The structure I've found that works best is to have six or seven agents chained, each roughly mimicking a part of the mind, or a role in film production. Broadly:
- A high-temp "Id" agent, tuned to output only vaguely related noise. This really helps creativity.
- An "Ego" agent, who receives the "Id" noise and is then given the initial response task.
- A low-temp "Super-Ego" or "script supervisor" agent, who can grep back across longer contexts to check detail, and is asked to ensure that the initial response is within narrative reason. Not telling it that one role of the dialogue was "user" and one was "assistant" really helps with it not siding with the user.
- A "continuity editor" agent, who is explicitly tasked with world and character lore-checking, building and updating character & world MD docs, etc.
- A "prose editor" agent, whose sole task is to ensure it's tonally in-line with initial guidelines.
You can add more as needed, depending on what is important to you.
I think expecting competent narrative from a single model is a big ask. When writing and telling or performing a story, you have to engage several different parts of the brain, with very different tasks. The creative part of the brain has to have lots of bad ideas in it to surface a compelling idea; the parts dealing with immersion and/or realism have to incredibly restrained.
The Id agent is very important. By appending 100 tokens of noise to a prompt asking: "Write a short story about [subject]", then asking an LLM to blindly score the short stories generated across a range of creativity metrics (such as they can exist!) I personally saw a ~40% score increase vs control over 3k short stories.
For the models that require context, I personally found combining a tiny sliding window with a lazy version of the "Recursive Language Models" approach broke immersion least often and had a significantly lower cost. That + the "Id noise" + the strict agents also allowed cheaper models to overperform for me personally.
My lazy version of the RLM approach is basically just giving the agent a grep tool across the full message history & "lore" documentation created by agents, combined with repeated, low-context turns, and a "submit answer" tool for when it felt like it had finished working.
When I looked at the internals of what each agent turn looked like, it did look like a complete mess - but the context window only needs to surface the things it actually needs to know each turn.
Short outputs help a lot with immersion, too - brevity means there is a lot less you can get wrong, and also aids response time & cost.
It does take me an awful lot of prompt tuning to get what I want creatively from LLMs in any format, especially weaker models working in this chain, but I think that's likely always going to be true. Art can have rules, but that doesn't make it science :-)
The RLM approach is detailed here, and I've found it really useful for any cost-sensitive/long-context task: https://alexzhang13.github.io/blog/2025/rlm/
Proper RLM looks like you’re allowing agents to directly modify their own context though, like closing browser tabs they don’t need anymore. I haven’t seen anyone actually doing this though.
If not, my agent-level chains just look like:
'''
Turn 1: OK, my task is X, so I should grep for it. Oh, it produced these results:
(Message pairs)
I should expand the context around those message pairs that look relevant.
(3 message pairs around search result)
I should save 1 of these, as it contains relevant information.
[Enforce Tool call limit]
[Delete all context added, except the search tool used already, and the relevant result(s) found.]
Turn 2: OK, my task is this, and it seems I already have this result, but I still need...
...
Turn 10: OK, after that search, my answer is:
[Response]
'''
I've never bothered to let agents self-remove from context, so I would guess it's 'lazy' in that sense. It seems more complex than the task requires in this case, though I can see the benefits on more complex tasks. If you're already saying "this is relevant info", I figure it's simplest to just enforce deletion of everything not marked relevant. In chained prompts, when you're trying to keep costs low and use weaker models, it seems best to limit decision-making as much as possible to make the models as deterministic as possible (on really dumb tasks like, "What is the colour of this goblin's hair?").
There are likely other parts of the actual paper's implementation where the ways I'm implementing it are lazy (because I'm doing this stuff for artistic/fucking around reasons, rather than to advance the field, or implement perfectly), and I think there are various interpretations of what "RLM" should mean. But I found the original paper very helpful, with lots of interesting ideas in, and think it's one where people can take what they need from.
I find agents will reveal information marked as "lore" (or similar) almost immediately once it's in-context.
One thing I've tried when playing with longform fiction or screen stuff, where you have an expected wordcount or page count to structure around, and the audience has less agency - I've not experimented with this for a DnD-like interactive narrative - is to use an agent that will design context additions like "this information is revealed" to be triggered in X number of words/pages, and simply do not include it in-context until that time.
This needs heavy quality control from new, separate agents with further turns, also, or you end up with incomprehensible constantly-twisting narrative soup.
I expect you could do something similar for message-pair-based participatory storytelling formats like DnD.
Another approach I've tried which I think would be more suited to interactive storytelling is to have the agent tasked with designing characters/setting information include the twists a % of the time, and to include a trigger for that reveal. "If asked about X, they say Y".
Then I remove these from the context for all agents.
Then I run an agent which is looking for the pre-defined triggers each turn.
When the agent sees a pre-defined trigger appear in the story, it adds the pre-defined reveal back in to the context/lore.
Again, you need to run a quality control / superego across that to check it still works, and amend or remove it suitably if it doesn't! It gets convoluted fast.
"Revealed information" is, I think, significantly more of a strain on general immersion, because it inherently contains surprise for the reader or audience. So, I think tasking the agents doing any initial character or world design with "adding twists" makes sense, so revealed plot information isn't random-feeling or out-of-the-blue, but has intent and logic that fits the character or setting.
The random words from my local epub library (leans toward postmodern fiction) were definitely more evocative than the dictionary words when I eyeballed them.
I randomised each turn but kept the story prompt request the same across control, dictionary, personal library.
I must stress that I'm not claiming scientific method or certainty here - just sharing an approach that seemed to work well enough for me, and seemed like a reasonable conclusion: introduce noise, get more interesting output.
I haven't done the math but I think you'd need a much larger sample size than 1k per category to prove the uplift!
""" In the following script, does this line make sense?
"Player: I put a cigar in the his mouth"
Script:
<Background, situational data, etc.>
Player: I raise my sword. DM: The kobold turns to you and says, "You're next", ax dripping with blood. """
And then, if it says no, ask it why and output that to the player. Or if it says yes, add it to the script and continue on.
1. You're sending your in-character inputs to an instruction-tuned model under the user role, in a multiturn chat. It's biased to treat these inputs as instructions and this behavior will show itself no matter what. Besides, the rigid structure of the assistant persona reply (usually tl;dr - explanation - "would you like to know more") is going to leak into such roleplay no matter what. To solve this problem on a generalist model you need to make your harness lump up all character turns into a seamless stream with formalized inline markers (e.g. screenplay-like paragraph prefixes or XML), use one of them as a custom stop string, send all this under one role (e.g. assistant), and prefill the assistant reply with a few messages from the past roleplay. This will break the rigid instruction-tuning structure (and also the cache, since caching breakpoints are based on chat turn boundaries in most APIs).
2. The models are simply not trained to "take incorrect actions back" in a story, this wouldn't make any sense. What happened is considered happened. This is a job for your harness, unless you want to make a specific finetune with a rigid format. You have to design and prompt it around the possibility of out-of-character user inputs, and think about how much freedom you want to give the user and how exactly you want to correct their actions. Validation with a second agent suggested in sibling comments is pretty good for this.
Well, this is something one might naively hope for, unfortunately it only works to a certain extent.
I think you're still confusing model and "LLM app" there.
I'm not that versed myself in these things, but you can, for example, look at the conversation templates, stop markers etc. in open weight models on HuggingFace, or play around with these things by yourself and modify them using llama.cpp or ollama (the things I mention in this paragraph are, AFAIK, not part of the model). These, and parameters like temperature, sampling etc. are just the things that can be controlled without touching the model.
Of course, frontier models and their uses have supposably a lot more machinery built around them to orchestrate their usage, apart from even chatbots defaulting to "agentic" behavior for many use cases.
And models still are specialized, and fine-tuned for instruction usage, so things like the conversation template, system prompts won't be enough to bend the characteristics of such a model in all desired directions. But "general-purpose model" has become a very fuzzy term by now.
If you want to use a custom chat scheme, use it as an overlay, don't break the default chat/tool use/reasoning template.
Matches my superficial experiments with trying to tweak Ollama's "modelfile" using some LLaMa- or gpt-oss-based instruction-tuned model as "base".
I need to experiment more with base models. The time from the end of 2019 onwards, when I first came across talktotransformer, it felt so magical.
Getting meaningful things out of these things can feel so... restraining.
And on the other hand: I'm tbh freshly stuck in the stage of being amazed at what current frontier coding models and apps can do.
1. The user should be able to prompt the AI to act differently from its default behavior. A human assistant is capable of role playing without always sounding like an assistant.
2. If the user asks the AI to follow the script and not allow unrealistic things to happen it should push back. The user is not always absolutely correct.
Even if it's not supported somewhere (e.g. z.ai API which isn't mature enough and has neither assistant prefills nor actual structured outputs), it's still better and more seamless than using the default user/assistant scaffolding for role alternation.
I think you could create an interesting benchmark for this, you could likely have models trying to to derail it and another scoring. Detecting when it’s happened shouldn’t be too complex for a model. I understand why LLMs do this, but ideally they wouldn’t.
Instead it's very context dependent - the DM might accept a player saying "I put a sausage in the NPC's mouth" if the player is in a tavern having his dinner, even if it was never explicitly stated that he's eating sausages. It's a judgement call as to whether the DM thinks this particular bit of improv will move the story in an interesting direction, even if they haven't written it upfront plus an attempt at balancing that magicking up an item out of thin air isn't conferring an unfair advantage.
I'm totally green when it comes to nlp,transformers, LLM training, etc, but I staunchly believe you can't produce real "reasoning" or consistent logic based on the predictions of byte pair encodings.
But in general, I've experienced things similar to you. I've also found that LLMs are bad at subtext, e.g. hinting at an NPC being a werewolf or vampire.
E.g.
- Tell the LLM that you as a player noticed a strange glow in an NPCs eyes -> the NPC becomes an enemy.
- In a fight, tell the LLM you put a sausage (or cigar or something) into the enemies mouth -> LLM usually allows it (even if it knows your inventory and that you don't have such an item) and turns the enemy into a confused enemy.
- Just say you visit some location that's not in the script -> LLM usually allows it
- During a fight, turn the story into some weird fell-good-love story (e.g. kiss or compliment the enemy or say something about the power of love) -> LLM turns enemy into friend
There are many more absurd things you can do and so far none of the LLMs I tried was able to stay inside the script or disallow or punish weird actions.
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I believe this behavior is telling about the LLMs susceptibility for being derailed.