To Infinity and Beyond: SHOW-1 and ShowrunnerAgents in Multi-Agent Simulations

Philipp Maas
Fable Studio
Edward Saatchi
Fable StudioFrank Carey
Fable StudioPete Billington
Fable Studio

Chris Wheeler
Fable Studio
Jessica Yaffa Shamash
Fable Studio

Abstract
In this work we present our approach to generating high-quality episodic content for
IP’s (Intellectual Property) using large language models (LLMs), custom state-ofthe art diffusion models and our multi-agent simulation for contextualization, story
progression and behavioral control. Powerful LLMs such as GPT-4 were trained ona large corpus of TV show data which lets us believe that with the right guidance
users will be able to rewrite entire seasons."That Is What Entertainment Will Look
Like. Maybe people are still upset about the last season of Game of Thrones.
Imagine if you could ask your A.I. to make a new ending that goes a different way
and maybe even put yourself in there as a main character or something.”.1

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Brockman https://www.hollywoodreporter.com/business/digital/chatgpt-game-of-thrones-openai-gregbrockman-1235348099/amp/101Creative limitations of existing generative AI Systems

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Current generative AI systems such as Stable Diffusion (Image Generator) and ChatGPT (Large
Language Model) excel at short-term general tasks through prompt engineering. However, they
do not provide contextual guidance or intentionality to either a user or a generative story system(showrunner2 ) as part of a long-term creative process which is often essential to producing high-quality
creative works, especially in the context of existing IP’s.

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1.1

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Living with uncertainty

Figure 1: Example Still from South Park AI Episode
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By using a multi-agent3 simulation as part of the process it’s possible to make use of data points suchas a character’s history, their goals and emotions, simulation events and localities to generate scenes
and image assets more coherently and consistently aligned with the IP story world. An IP-based
simulation provides a clear, well known context to the user which allows them to judge the generated
story more easily. Moreover, by allowing them to exert behavioral control over agents, observe theiractions and engage in interactive conversations, the user’s expectations and intentions are formed
which we then funnel into a simple prompt to kick off the generation process.
The simulation has to be sufficiently complex and non-deterministic to favor a positive disconfirmation.
Amplification effects can help mitigate what we consider an undesired "slot machine" effect which
we’ll briefly touch on later. We are used to watching episodes passively and the timespan between
input and "end of scene/episode" discourages immediate judgment by the user and as a result reduces
their desire to "retry". This disproportionality of the user’s minimal input prompt and the resulting
high-quality long-form output in the form of a full episode is a key factor for positive disconfirmation.
While using and prompting a large language model as part of the process can introduce "several
challenges".4 , some of them like hallucinations, which introduce uncertainty or in more creative terms"unexpectedness", can be regarded as creative side-effects to influence the expected story outcome inpositive ways. As long as the randomness introduced by hallucination does not lead to implausible
plot or agent behavior and the system can recover, they act as happy-accidents5 , a term often usedduring the creative process, further enhancing the user experience.
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https://fablesimulation.com/blog/friends-ai-sitcom-simulation
Sung Park https://arxiv.org/abs/2304.03442
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Li https://arxiv.org/abs/2303.17760
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Maas https://noproscenium.com/from-a-i-character-to-sundance-filmmaker-with-gpt-3-d4ab80c31b4e
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The Slot Machine Effect refers to a scenario where the generation of AI-produced content feels more
like a random game of chance rather than a deliberate creative process6 . This is due to the often
unpredictable and instantaneous nature of the generation process.

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The Issue of ‘The Slot Machine Effect’ in current Generative AI tools

Current off-the-shelf generative AI systems do not support or encourage multiple creative evaluation
steps in context of a long-term creative goal. Their interfaces generally feature various settings, suchas sliders and input fields which increase the level control and variability. The final output however,is generated almost instantaneously by the press of a button. This instantaneous generation process
results in immediate gratification providing a dopamine rush to the user. This reward mechanism
would be generally helpful to sustain a multi-step creative process over long periods of time but
current interfaces, the frequency of the reward and a lack of progression (stuck in an infinite loop)
can lead to negative effects such as frustration, the intention-action gap7 or a loss of control over thecreative process. The gap results from behavioral bias favoring immediate gratification, which canbe detrimental to long-term creative goals.

Figure 2: User Interface Comparison - Left to right: Stable Diffusion Gradio, ChatGPT, Runway
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While we do not directly solve these issues through interfaces, the contextualization of the process
in a simulation and the above mentioned disproportionality and timespan between input and output
help mitigate them. In addition we see opportunities in the simulation for in-character discriminatorsthat participate in the creative evaluation process, such as an agent reflecting on the role they wereassigned to or a scene they should perform in.

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The multi-step "trial and error" process of the proposed generative story system is not presented to theuser, therefore it doesn’t allow for intervention or judgment, avoiding the negative effects of immediate
gratification through a user’s "accept or reject" decisions. It does not matter to the user experience
how often the AI system has to retry different prompt chains8 as long as the generation process is notnegatively perceived as idle time but integrated seamlessly with the simulation gameplay. The user
would only act as the discriminator at the end of the process after having watched the generated scene
or episode. This is also an opportunity to utilize the concept of Reinforcement Learning through
Human Feedback (RLHF) for improving the multi-step creative process and as a result automaticallygenerate full episodes in the future.

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LLMs represent the forefront of natural language processing and machine learning research, demonstrating exceptional capabilities in understanding and generating human-like text. They are typically

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Large Language Models

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https://artificial.tech/slot-machine-effect-of-ai/
https://thedecisionlab.com/reference-guide/psychology/intention-action-gap
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Yang https://arxiv.org/abs/2306.02224
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built on Transformer-based architectures, a class of models that rely on self-attention9 mechanisms.
Transformers allow for efficient use of computational resources, enabling the training of significantly
larger language models. GPT-4, for instance, comprises billions of parameters that are trained on
extensive datasets, effectively encoding a substantial quantity of worldly knowledge in their weights.

Figure 3: Diagram of the Transformer Architecture10
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Central to the functioning of these LLMs is the concept of vector embeddings. These are mathematicalrepresentations of words or phrases in a high-dimensional space. These embeddings capture thesemantic relationships between words, such that words with similar meanings are located close toeach other in the embedding space. In the case of LLMs, each word in the model’s vocabulary is
initially represented as a dense vector, also known as an embedding. These vectors are adjusted
during the training process, and their final values, or "embeddings", represent the learned relationships
between words. During training, the model learns to predict the next word in a sentence by adjusting
the embeddings and other parameters to minimize the difference between the predicted and actual
words. The embeddings thus reflect the model’s understanding of words and their context. Moreover,because Transformers can attend to any word in a sentence regardless of its position, the model
can form a more comprehensive understanding of the meaning of a sentence. This is a significant
advancement over older models that could only consider words in a limited window. The combinationof vector embeddings and Transformer-based architectures in LLMs facilitates a deep and nuanced
understanding of language, which is why these models can generate such high-quality, human-liketext.
As was mentioned previously, transformer-based language models excel at short-term general tasks.
They are regarded as fast-thinkers. [Kahneman]12 . Fast thinking pertains to instinctive, automatic,
and often heuristic-based decision-making, while slow thinking involves deliberate, analytical, andeffortful processes. LLMs generate responses swiftly based on patterns learned from training data,
without the capacity for introspection or understanding the underlying logic behind their outputs.
However, this also implies that LLMs lack the ability to deliberate, reason deeply, or learn fromsingular experiences13 in the way that slow-thinking entities, such as humans, can. While thesemodels have made remarkable strides in text generation tasks, their fast-thinking nature may limit
their potential in tasks requiring deep comprehension or flexible reasoning. More recent approaches
to imitate slow-thinking capabilities such as prompt-chaining (see Auto-GPT) showed promising
results. Large language models seem powerful enough to act as their own discriminator in a multi-step
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Vaswani https://arxiv.org/abs/1706.03762
Vaswani https://arxiv.org/abs/1706.03762
11https://techcommunity.microsoft.com/t5/azure-data-explorer-blog/azure-data-explorer-for-vectorsimilarity-search/ba-p/3819626
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Bubeck https://arxiv.org/abs/2303.12712
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Bubeck https://arxiv.org/abs/2303.12712
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Figure 4: Example of Text Vector Embedding11

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process. This can dramatically improve the ability to reason in different contexts, such as solving
math problems.14
We make use of GPT-4 for the agents in the simulation as well as generating the scenes for the southpark episode. Since transcriptions of most of the south park episodes are part of GPT-4’s training
dataset, it already has a good understanding of the character’s personalities, talking style as well as
overall humor of the show, eliminating the need for a custom fine-tuned model.

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We tried to imitate slow-thinking as part of a multi-step creative process. For this we used different
prompt chains to extrapolate from titles, synopsis and summaries of previous scenes to continuously
generate coherent scenes and progress towards a satisfactory, IP-aligned result. Our attempt togenerate episodes through prompt-chaining is due to the fact that story generation is a highly
discontinuous task.15 These are tasks where the content generation cannot be done in a gradualor continuous way, but instead requires a certain ”Eureka” idea that accounts for a discontinuous
leap in the progress towards the solution of the task. The content generation involves discovering or
inventing a new way of looking at or framing the problem, that enables the generation of the rest of
the content. Examples of discontinuous tasks are solving a math problem that requires a novel orcreative application of a formula, writing a joke or a riddle, coming up with a scientific hypothesis ora philosophical argument, or creating a new genre or style of writing.

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Diffusion models operate on the principle of gradually adding or removing random noise from dataover time to generate or reconstruct an output. The image starts as random noise and, over many
steps, gradually transforms into a coherent picture, or vice versa.

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Diffusion Models

In order to train our custom diffusion models, we collected a comprehensive dataset comprising
approximately 1200 characters and 600 background images from the TV show South Park. Thisdataset serves as the raw material from which our models learned the style of the show.
To train these models, we employ Dream Booth.16 The result of this training phase is the creation of
two specialized diffusion models.
The first model is dedicated to generating single characters set against a keyable background color.
This facilitates the extraction of the generated character for subsequent offline processing andanimation, allowing us to integrate newly generated characters into a variety of scenes and settings.
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Baker https://openai.com/research/improving-mathematical-reasoning-with-process-supervision
Bubeck https://arxiv.org/abs/2303.12712
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Ruiz https://arxiv.org/abs/2208.12242
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Figure 5: Stable Diffusion Model for South Park Backgrounds, prompt: "a residence building inSouth park [demoura artstyle]"

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In addition, the character diffusion model allows the user to create asouth park character based on their own looks via the image-to-image
process of stable diffusion and then join the simulation as an equallyparticipating agent. With the ability to clone their own voice, it’s easy
to imagine a fully realized autonomous character based on the user’s
characteristic looks, writing style and voice.
The second model is trained to generate clean backgrounds, with aparticular focus on both exterior and interior environments. This model
provides the ’stages’ upon which our generated characters can interact, Figure 6: Example of genallowing for a wide range of potential scenes and scenarios to be created. erated South Park character

Figure 7: GPT-4 generated SVG image, prompt: "Can you give me a svg drawing of a house on astreet?"
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However, it’s important to note that the images produced by thesemodels are inherently limited in their resolution due to the pixel-based
nature of the output. To circumvent this limitation, we post-process thegenerated images using an AI upscaling technique, specifically R-ESRGAN-4x+-Anime6B, whichrefines and enhances the image quality. The image generation and upscaling is currently done offline
and not on the fly, although they could be in the future as generation speed and quality improve.
For future 2D interactive work, training custom transformer based models that are capable ofgenerating vector-based output would have several advantages. Unlike pixel-based images, vector
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graphics do not lose quality when resized or zoomed, thus offering the potential for infinite resolution.
This will enable us to generate images that retain their quality and detail regardless of the scale atwhich they are viewed. Furthermore, vector based shapes are already separated into individual parts,
solving pixel-based post-processing issues with transparency and segmentation which complicate theintegration of generated assets into procedural world building and animation systems.

Figure 8: SVGs generated by GPT-4 for the classes automobile, truck, cat, dog.17

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Simulation

Over the past year, we experimented with using simulated data like relationships, personalities,
backstories, character descriptions and more to drive character behavior. Characters chose affordance
providers to maintain needs, similar to the SIMs games. We captured those generated events alongwith the character’s details to provide “Reveries” or reflections on each event and their day as a whole.
An example is this character, whose backstory includes them being a depressed college student. Theirbackstory and events combined into the following interpretation from the character of how their day
went:

Figure 9: Example of a character reverie.
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We found there is a natural tension between simulation driven events and narrative driven events orplot. For the South Park experiments, since so much of that material is already familiar to GPT, weonly used time of day and the name of the location in the prompt, allowing the results to be mostly
plot driven.

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At present we continue to develop the underlying simulation system to blend daily simulated events
as well as narrative plans into a satisfying output. One component of the simulation system is thegeneration of hundreds of plot templates [see Figure 10] that better fit the context of a fully simulated
experience. We will share more details on that in a follow-up paper.

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Episode Generation

We define an episode as a sequence of dialogue scenes in specific locations which add up to a total
runtime of a regular 22 min south park episode.
In order to generate a full south park episode, we prompt the story system with a high level idea,
usually in the form of a synopsis and major events we want to see happen in each of the 14 scenes.
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https://arxiv.org/abs/2303.12712

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Figure 10: Example of a plot structure for a simulated escaped convict.

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From this, the story system can automatically generate a scene (or multiple scenes) by making use ofsimulation data (time of day, zone, character) as part of a prompt chain which first generates a fitting
title and as a second step the dialogue of the scene. The showrunner system takes care of spawning
the characters for each scene.

Figure 11: Diagram of Showrunner systems and prompt graph
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In the end, each scene simply defines the location, cast and dialogue for each cast member. The sceneis played back after the staging system and AI camera system went through initial setup. The voiceof each character has been cloned in advance and voice clips are generated on the fly for every new
dialogue line.

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In our experiments, generating a single scene can take a significant amount of time of up to one
minute. Below is a response time comparison between GPT-3.5-turbo and GPT-4. Speed will increasein the short-term as models and service infrastructure get improved and other factors like artificial
throttling due to high user demand will get removed.

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Reducing Latency

Since we generate the scenes during gameplay, we have ways to hide most of the generation timein moments when the user is still interacting with the simulation or other user interfaces. Anotherway to reduce the time needed to generate a scene is to use faster models such as GPT-3.5-turbo forprompts where the highest quality and accuracy is not so important.
During scene playback, we avoid any unwanted pauses between dialogue lines related to audio
generation by using a simple buffering system which generates at least one voice clip in advance.
See figure 13. This means while one character is delivering their voice clip, we already make the
web request for the next voice clip, wait for it to generate, download the file and then wait for the18

Pungas https://www.taivo.ai/g pt−3−5−and−gpt−4−response−times/

8Figure 12: Speed comparison of GPT-3.5 vs. GPT-418

Figure 13: Diagram of zero-delay voice clip generation

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current speaker to finish his dialogue before playback (delay). In this way the next dialogue line’s
voice clip is always delivered without any delay. Text generation and voice cloning services become
increasingly fast and allow for highly adaptive and near-real time voice conversations.

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As stated earlier, the data produced by the simulation acts as creative fuel to both the user who iswriting the prompts and the generative story system which is interacting with the LLM. Promptchaining19 is a technique, which involves supplying the language model with a sequence of related
prompts to simulate a continuous thought process. Sometimes it can take on different roles in eachstep to act as the discriminator against the previous prompt and generated result.

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Simulate creative thinking

In our experiments we tried to mimic a discontinuous creative thought process. For example, thecreation of 14 distinct South Park scenes could be achieved by initially providing a broad prompt tooutline the general narrative, followed by specific prompts detailing and evaluating each scene’s cast,location, and key plot points. This mimics the process of human brainstorming, where ideas are built
upon and refined in multiple often discontinuous steps. By leveraging the generative capabilities of
LLMs in conjunction with the iterative refinement offered by prompt-chaining, we could in theory
construct a dynamic, detailed, and engaging narrative.
In addition, we explored new concepts like plot patterns and dramatic operators (DrOps) to enhancethe episode structure overall but also the connective tissue between each scene. Stylistic devices
like reversals, foreshadowing, cliffhangers are difficult to evaluate as part of a prompt chain. A
user without a writing background would have equal difficulty in judging these stylistic devices fortheir effectiveness and proper placement. We propose a procedural approach, injecting these show
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Wu https://arxiv.org/abs/2203.06566
Yank, Auto-GPT for Online Decisionhttps://arxiv.org/pdf/2306.02224.pdf
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andAdditional

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Figure 14: Example of a prompt chain from Auto-GPT20

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specific patterns and stylistic devices into the prompt chain programmatically as plot patterns andDrOps which would operate at the level of act structures, scene structures and individual dialogue
lines. We are investigating future opportunities to extract what we call a dramatic fingerprint whichis specific to each IP and format and train our custom SHOW-1 model with these data points. Thisdataset combined with overall human feedback could further align tone, style and entertainment value
between the user and the specified IP while offering a highly adaptive and interactive story system aspart of the on-going simulation.

Figure 15: Diagram of South Park Episode ratings from IMDB21

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As mentioned above, one of the advantages of the simulation is that it avoids the blank page problem
for both a user and a large language model by providing creative fuel22 . Even experienced writers
can sometimes feel overwhelmed when asked to come up with a title or story idea without any priorincubation of related material. The same could be said for LLMs. The simulation provides context
and data points before starting the creative process.

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Blank Page Problem

Drhlík, https://pdrhlik.github.io/southparktalk-whyr2018/https://www.trytriggers.com/blog-posts/overcoming-the-barrier-of-the-blank-page10

Figure 16: Example UI of Showrunner prompt input

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The story generation process in this proposal is a shared responsibility between the simulation, theuser, and GPT-4. Each has strengths and weaknesses and a unique role to play depending on how
much we want to involve them in the overall creative process. Their contributions can have different
weights. While the simulation usually provides the foundational IP-based context, character histories,
emotions, events, and localities that seed the initial creative process. The user introduces theirintentionality, exerts behavioral control over the agents and provides the initial prompts that kick off
the generative process. The user also serves as the final discriminator, evaluating the generated story
content at the end of the process. GPT-4, on the other hand, serves as the main generative engine,
creating and extrapolating the scenes and dialogue based on the prompts it receives from both theuser and the simulation. It should be a symbiotic process where the strengths of each participant
contribute to a coherent, engaging story.

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The formular (creative characteristics) and format (technical characteristics) of a show are often afunction of real-world limitations and production processes. They usually don’t change, even overthe course of many seasons (South Park currently has 26 seasons and 325 episodes).23

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Who is driving the story

SHOW-1 and Intentionality

A single dramatic fingerprint of a show, which is used to train the proposed SHOW-1 model, can be
regarded as a highly variable template or "formula" for a procedural generator that produces South
Park-like episodes.
To train a model such as SHOW-1 we need to gather a sufficient amount of data points in relation toeach other that characterize a show. A TV show does not just come into existence and is made upof the final dialogue lines and set descriptions as seen by the audience. Existing datasets on whichcurrent LLM’s are trained on only consist of the final screenplay which has the cast, dialogue lines
and sometimes a short scene header. A lot of information is missing, such as timing, emotional
states, themes, contexts discussed in the writer’s room and detailed directorial notes to give a few
examples. The development and refinement of characters is also part of this on-going process.
Fictional characters have personalities, backstories and daily routines which help authors to sculpt
not only scenes but the arcs of whole seasons. Even during a show characters keep evolving basedon audience feedback or changes in creative direction. With the Simulation, we can gather data
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continuously from both the user’s input and the simulated agents. Over time, as episodes are created,refined and rated by the user we can start to train a show specific model and deploy it in the future
as a checkpoint which allows the user to continue to refine and iterate on either their own original
show or alternatively push an already existing show such as south park into directions previously
not conceived by the original show runners and IP holders. To illustrate this, we imagine a user
generating multiple south park episodes in which Cartman, one of the main characters and known
for his hot headedness, slowly changes to be shy and naive while the life of other characters such as
Butters could be tuned to follow a much more dominant and aggressive path. Over time, this feedback
loop of interacting with and fine-tuning the SHOW-1 model could lead to new interpretations ofexisting shows but more excitingly to new original shows based on the user’s intention. One of thechallenges in order to make this feedback loop engaging and satisfying is the frequency at which amodel can be trained. A model which is fed by real-time simulation data and user input should not
feel static or require expensive resources to adapt. Otherwise the output it generates can feel staticand unresponsive as well.
When a generative system is not limited in its ability to swiftly produce high amounts of content andthere is no limit for the user to consume such content immediately and potentially simultaneously,
the 10,000 Bowls of Oatmeal24 problem can become an issue. Everything starts to look and feel thesame or even worse, the user starts to recognize a pattern which in turn reduces their engagement asthey expect newly generated episodes to be like the ones before it, without any surprises.
This is quite different from a predictable plot which in combination with the above mentioned"positive hallucinations" or happy accidents of a complex generative system can be a good thing.
Surprising the user by balancing and changing the phases of certainty vs. uncertainty helps to increase
their overall engagement. If they would not expect or predict anything, they could also not getpleasantly surprised.
With our work we aim for perceptual uniqueness. The "oatmeal" problem of procedural generators
would be mitigated by making use of an on-going simulation (a hidden generator) and the long-form
content of 22 min episodes which should only get generated every 3h. In this way the user generally
does not consume a high quantity of content simultaneously or in a very short amount of time. This
artificial scarcity, natural game play limits and simulation time help.
Another factor that keeps audiences engaged while watching a show and what makes episodes unique
is intentionality from the authors. A satirical moral premise, twisted social commentary, recent world
events or cameos by celebrities are major elements for South Park. Other show types, for example
sitcoms, usually progress mainly through changes in relationship (some of which are never fulfilled),
keeping the audience hooked despite following the same format and formula.
Intentionality from the user to generate a high-quality episode is another area of internal research.
Even users without a background in dramatic writing should be able to come up with stories, themes
or major dramatic questions they want to see played out within the simulation. To support this,the showrunner system could guide the user by sharing its own creative thought process and makeencouraging suggestions or prompting the user by asking the right questions. A sort of reversed
prompt engineering where the user is answering questions.
One of the remaining unanswered questions in the context of intentionality is how much entertainment
value (or overall creative value) is directly attributed to the creative personas of living authors anddirectors. Big names usually drive ticket sales but the creative credit the audience gives to the work
while consuming it seems different. Watching a Disney movie certainly carries with it a sense ofcreative quality, regardless of famous voice actors, as a result of brand attachment and its history.
AI generated content is generally perceived as lower quality and the fact that it can get generated
in abundance further decreases its value. How much this perception would change if Disney wereto openly pride themselves on having produced a fully AI generated movie is hard to say. What if
Steven Spielberg, single handedly generated an AI movie? Our assumption is that the perceived valueof AI generated content would certainly increase.
A new interesting approach to replicate this could be the embodiment of creative AI models suchas SHOW-1 to allow them to build a persona outside their simulated world and build relationships

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Compton, Procedural Storytelling in Game Design

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via social media25 or real world events with their audience.26 As long as an AI model is perceived
as a black box and does not share their creative process and reasoning in a human and accessible
way, as is the case for living writers and directors, it’s unlikely to get credit with real creative values.
However, for now this is a more philosophical question in the context of AGI.

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Conclusion

Our approach of using multi-agent simulation and large language models for generating high-quality
episodic content provides a novel and effective solution to many of the limitations of current AI
systems in creative storytelling. By integrating the strengths of the simulation, the user, and the AI
model, we provide a richer and more engaging storytelling experience that is aligned with the IP
story world. Our method also mitigates issues such as the ’slot machine effect’, ’the oatmeal problem’
and ’blank page problem’ that plague conventional generative AI systems. As we continue to refine
this approach, we are confident that we can further enhance the quality of the generated content, theuser experience, and the creative potential of generative AI systems in storytelling.
Acknowledgements We are grateful to Lewis Hackett for his help and expertise in training thecustom Stable Diffusion Models.

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This is Version 2 - for earlier versions please visit the github page: https:// fablestudio.github.io/
showrunner-agents/

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References

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[1] Brockman https://www.hollywoodreporter.com/business/digital/chatgpt-game-of-thrones-openai-gregbrockman-1235348099/amp/

328[2] Maas https://fablesimulation.com/blog/friends-ai-sitcom-simulation

329[3] Sung Park https://arxiv.org/abs/2304.03442

330[4] Li https://arxiv.org/abs/2303.17760

331[5] Maas https://noproscenium.com/from-a-i-character-to-sundance-filmmaker-with-gpt-3-d4ab80c31b4e

332[6] https://artificial.tech/slot-machine-effect-of-ai/

333[7] https://thedecisionlab.com/reference-guide/psychology/intention-action-gap

334[8] Yang https://arxiv.org/abs/2306.02224

335[9] Vaswani https://arxiv.org/abs/1706.03762

336[10] Vaswani https://arxiv.org/abs/1706.03762

337
338

[11] https://techcommunity.microsoft.com/t5/azure-data-explorer-blog/azure-data-explorer-for-vector-similaritysearch/ba-p/3819626

339[12] Bubeck https://arxiv.org/abs/2303.12712

340[13] Bubeck https://arxiv.org/abs/2303.12712

341[14] Baker https://openai.com/research/improving-mathematical-reasoning-with-process-supervision342[15] Bubeck https://arxiv.org/abs/2303.12712

343[16] Ruiz https://arxiv.org/abs/2208.12242

344[17] https://arxiv.org/abs/2303.12712

326[18] Pungas https://www.taivo.ai/g pt−3−5−and−gpt−4−response−times/345[19] Wu https://arxiv.org/abs/2203.06566

346[20] Yank, Auto-GPT for Online Decision Making:
Benchmarks and Additional Opinions
https://arxiv.org/pdf/2306.02224.pdf [21] Drhlík, https://pdrhlik.github.io/southparktalk-whyr2018

347

25

Virtual Beings https://www.youtube.com/watch?v=FSq-mheA7Ds, https://www.youtube.com/watch?v=IROZSq–
MQE
26
Collaborating with AI at Sundance https://www.fable-studio.com/behind-the-scenes/ai-collaboration

13

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[22] https://www.trytriggers.com/blog-posts/overcoming-the-barrier-of-the-blank-page[23] https://en.wikipedia.org/wiki/SouthP ark

349[24] Compton, Procedural Storytelling in Game Design

350
351

[25]
VirtualBeingshttps://www.youtube.com/watch?v=IROZSq–MQE

352[26] Collaborating with AI at Sundance https://www.fable-studio.com/behind-the-scenes/ai-collaboration

https://www.youtube.com/watch?v=FSq-mheA7Ds,

14