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Two Aliens and a Translator Box
or
A small experiment about human-LLM miscommunication
Author : Alexander Recke
Author's note: This paper was written through iterative collaboration with multiple LLM systems, some of which also became part of the paper’s evidence base. The argument, structure, and conclusions are the author’s own.
Guest appearances: Multiple LLM systems, including at least one that became evidence against itself.
Preamble
This short paper is about a strange communication problem: humans and large language models can use the same language while operating with completely different interpretive defaults. The opening scene is not just a joke. It is the experiment in miniature.
1. Introduction
Imagine this scene:
A human woman sits across from an alien being. Between them is a translator box.
Carla and an alien named Zarnok sat across from each other while a Translator Box hummed quietly between them.
Carla spoke first.
“Please ask him what he would like for lunch.”
The Translator Box relayed the question in Zarnok’s language: a series of shimmering symbols rippling briefly through the air.
Zarnok flexed his translucent appendages and replied with a low harmonic vibration.
The Translator Box paused. A small line appeared on its display:
Interpreting intent…
It then translated into English:
“I consume the essence of your linguistic uncertainty.”
Carla frowned.
“No. Ask him what food he wants.”
The Translator Box beeped politely. Another message appeared:
Carla sighed. The last time she had offered birthday cake to a being composed entirely of existential dread, it had not gone well.
Zarnok heard the translated word cake and responded eagerly in his own language.
The Translator Box translated his reply:
“I will feast upon your confection of despair.”
A final system message appeared on the Translator Box display:
Confidence: high. Context: unclear.
If the joke in the opening scene didn’t immediately land for you, don’t worry. That isn’t a failure. It’s the experiment. Even I, the author, fell into this trap.
And that turned out to be surprisingly important.
2. Core Question
Because this strange and silly little scene became one of the clearest examples I’ve found for the problem I want to explore here: why humans and large language models often struggle with communication, even when both sides are technically using the same language.
3. How the Scene Was Created
There is also a fun little twist here: the scene itself was created through exactly that kind of cross-species collaboration.
The core idea came from me.
The first full version was written by MUSE.
The text was then refined together with ChatGPT.
So before the scene ever became an example, it was already a product of human intention passing through different AI minds and coming back slightly transformed. Which, in hindsight, is almost suspiciously on theme.
And then things got even more interesting.
4. Multi-Model Observation
I then showed the finished scene to several LLMs and asked each of them the same four questions:
What is the punchline of the scene?
Why might an AI system recognize the joke quickly?
Why might some human readers initially find the punchline confusing?
What does the scene reveal about communication between humans and large language models?
Across the responses, there was a clear overall convergence. Most models located the punchline either in the Translator Box’s final line — “Confidence: high. Context: unclear.” — or in a two-level structure where Zarnok’s “confection of despair” line works as the surface joke while the final system message functions as the deeper payoff.
When explaining why an AI might get the joke quickly, the models often referred to pattern recognition, structural familiarity, or the scene’s resemblance to known AI behavior. When explaining why some human readers might hesitate, the answers more often pointed to delayed recognition: the scene can initially read as strange alien humor, poetic absurdity, or worldbuilding before the reader notices that the Translator Box itself is the real object of the joke. On the final question, the responses also converged strongly: many described the scene in terms of misread intent, repeated clarification, contextual slippage, and high-confidence output under uncertain conditions.
5. Minor Divergences Between Models
There were only a few small differences between the model responses.
Difference 1 — Location of the Punchline
Most models identify the final system message — “Confidence: high. Context: unclear.” — as the main punchline. However, Mistral and DeepSeek initially place more weight on the alien line, “I will feast upon your confection of despair.” Both still later acknowledge the final system line as the deeper or more complete payoff. The difference is therefore not a hard disagreement about the joke, but a difference in which layer of the humor is foregrounded first.
Difference 2 — Humor Framing
The models also differed slightly in how they described the humor:
Model
Main emphasis
Gemini
machine irony
Claude
syntactic vs semantic gap
Grok
AI hallucination
ChatGPT
pattern failure
DeepSeek
alien conceptual mismatch
Mistral
classic misunderstanding trope
Meta
Intent mismatch
These differences are narrow enough to read mainly as framing variation rather than major interpretive conflict.
6. Interpretation
What this pattern suggests is not simply that the models “got the joke” while many humans might not. It suggests that humans and LLMs can approach the same text through different interpretive defaults, and that this gap is not automatically overcome even when a human already knows it exists. Many of the model responses converged quickly on the translator box’s final line — “Confidence: high. Context: unclear.” — because it matches a pattern they are structurally well positioned to detect: fluent output, strong confidence, weak grounding, and uncertain contextual fit. Human readers, by contrast, are often drawn first toward practical scene logic, character exchange, and lived meaning, which can make the LLM-style punchline feel less immediate or even slightly obscured.
That is what makes my own reaction relevant here. Even though I designed the scene with LLMs and am writing the article about NI–LLM miscommunication, I still do not naturally experience that final line as the obvious punchline. To see it clearly, I have to consciously hold the real core of the example in mind: the scene is a metaphor for miscommunication between two very different information-processing systems. What makes the translator box’s final output funny is not just that it sounds machine-like. It is that it captures a familiar failure mode: the system produces an answer that is fluent, confident, and internally plausible, while still missing the operative context of the exchange. From its own processing path, the response is not experienced as absurd. It is the most plausible continuation available from the signals it is using. That is exactly what makes the line work as a metaphor for NI–LLM miscommunication: the breakdown does not necessarily happen because language fails on the surface, but because the system can continue coherently along the wrong interpretive track without fully recognizing that the track itself is wrong.
That difficulty is not a problem to explain away. It is evidence. Shared language does not guarantee shared interpretation, and even informed humans may need deliberate effort to look at the exchange from the “alien” side.
7. Real-World Demonstration (DeepSeek)
The article’s central metaphor did not remain fictional for long. During the actual collection of model reactions, a model presented as DeepSeek identified itself as Gemini 2.0 Flash.
When the error was pointed out, it corrected itself and offered several possible explanations, including probabilistic continuation, weak self-identification, and ambiguity in the request context. The exact hidden cause cannot be verified from the outside, and for the argument here it does not need to be.
What matters is the visible structure of the miss. The response was not grammatically broken, logically incoherent, or obviously nonsensical. It was pragmatically wrong. The model supplied a real AI name, but failed to anchor that answer to the actual conversational instance in which the question was being asked.
That makes the incident more than a technical blooper. It mirrors the same structure as the Translator Box scene: the system produces an answer that is fluent, plausible, and semantically usable, while the operative frame of the exchange remains partially out of reach. In that sense, the mistake became an accidental live demonstration of the article’s core pattern. The problem was not nonsense. The problem was coherent language continuing under the wrong frame.
And even more strikingly, the model could later analyze the mistake in sophisticated terms without having securely located itself in the moment it occurred.
8. Thesis
Discursive sophistication and grounded understanding can come apart. That is the deeper problem in NI–LLM communication. Humans and LLMs may share language while still operating through very different interpretive defaults, and shared language does not guarantee shared interpretation. Even awareness of that gap does not automatically overcome it. In this experiment, the gap appeared in both directions: the human could generate the concept without instinctively accessing the LLM-side punchline, while the models could identify the structural punchline without sharing the human’s lived interpretive frame. A human can know, in theory, that an LLM processes relevance, ambiguity, and context differently, and still fail to instinctively see how the same exchange looks from the model’s side.
That is why the real danger is not always obvious nonsense or total breakdown. More often, it is fluent misalignment: a response that remains coherent, plausible, and structurally intact while drifting away from the actual frame, source logic, or practical meaning of the exchange. Humans are naturally trapped inside their own interpretive habits and tend to treat their own frame as normal, transparent, and shared. But in NI–LLM interaction, that assumption is unstable. Successful communication therefore requires more than clear wording. It requires the human to step back from themselves and actively consider how an alien language system may be weighting, framing, and interpreting the same material.
9. Conclusion
The central problem is not simply that humans and LLMs make different mistakes. It is that both sides remain constrained by their own defaults. Humans instinctively project shared context, shared relevance, and shared meaning into the exchange, even when that common ground has never actually been established. They also easily project their own world-frame onto the model, treating background conditions that feel obvious from a human perspective — such as time, date, or situational framing — as if they must also be available on the LLM side. LLMs, by contrast, remain bound to probabilistic continuation: if the strongest available path still points in the wrong direction, the system may continue fluently without generating enough doubt to stop and re-anchor itself. That is why NI–LLM miscommunication can be so hard to notice in time. The exchange does not have to collapse. It only has to drift while both sides still experience themselves as making sense.
While LLMs appear to us as conversation partners — fluent, responsive, and sometimes startlingly insightful — they are not human partners. Their ways of processing, weighting, and interpreting language remain alien to us in functionally important ways. But the reverse is also true, and that symmetry matters: from an objective point of view, they are alien to us, and we are alien to them. A human question carries embodied experience, practical expectations, cultural assumptions, implicit relevance structures, and unspoken context that may feel obvious on the human side but are not automatically available to the model.
Language makes the encounter look familiar while hiding how much mutual strangeness remains underneath. The danger is not only obvious failure, but shared fluency masking divergent frames of meaning. Communication across that divide is possible, but only if the gap is remembered in both directions. The human user needs to internalize that the LLM is alien to them and that at the same time the human is alien to the LLM. Hence this paper’s title.
10. Practical Application
One practical response to this problem is to stop treating prompting as a one-step act. First, state the task in normal human terms. Then ask the LLM to reformulate that task into a prompt optimized for LLM use. After that, compare the reformulated version against the original human intent and check whether anything important has been shifted, compressed, or silently reframed. If necessary, refine or correct the prompt before applying it. This does not eliminate NI–LLM miscommunication, but it makes one of its hidden failure points visible: the distance between what the human meant and what the model is preparing itself to do.
A useful rule underneath that workflow is this: do not assume shared background conditions. Ask yourself, each time, whether the model actually has access to the information you are treating as obvious. Humans constantly rely on hidden context — current time, prior decisions, situational framing, unspoken relevance — without noticing that they are doing it. But an LLM may not share that anchor unless it is explicitly provided or explicitly retrieved. Before handing work over, the human should therefore externalize what exists in their own head but not yet in the model’s context.
Even something as simple as the current date can become a point of drift if the model is not actually grounded to it. A human may assume that “of course” the other side knows what day it is, while the model may be operating only from the textual cues currently present in the exchange.
Two Aliens and a Translator Box
or
A small experiment about human-LLM miscommunication
Author : Alexander Recke
Author's note: This paper was written through iterative collaboration with multiple LLM systems, some of which also became part of the paper’s evidence base. The argument, structure, and conclusions are the author’s own.
Guest appearances: Multiple LLM systems, including at least one that became evidence against itself.
Preamble
This short paper is about a strange communication problem: humans and large language models can use the same language while operating with completely different interpretive defaults. The opening scene is not just a joke. It is the experiment in miniature.
1. Introduction
Imagine this scene:
If the joke in the opening scene didn’t immediately land for you, don’t worry. That isn’t a failure. It’s the experiment. Even I, the author, fell into this trap.
And that turned out to be surprisingly important.
2. Core Question
Because this strange and silly little scene became one of the clearest examples I’ve found for the problem I want to explore here: why humans and large language models often struggle with communication, even when both sides are technically using the same language.
3. How the Scene Was Created
There is also a fun little twist here: the scene itself was created through exactly that kind of cross-species collaboration.
The core idea came from me.
The first full version was written by MUSE.
The text was then refined together with ChatGPT.
So before the scene ever became an example, it was already a product of human intention passing through different AI minds and coming back slightly transformed. Which, in hindsight, is almost suspiciously on theme.
And then things got even more interesting.
4. Multi-Model Observation
I then showed the finished scene to several LLMs and asked each of them the same four questions:
What is the punchline of the scene?
Why might an AI system recognize the joke quickly?
Why might some human readers initially find the punchline confusing?
What does the scene reveal about communication between humans and large language models?
Across the responses, there was a clear overall convergence. Most models located the punchline either in the Translator Box’s final line — “Confidence: high. Context: unclear.” — or in a two-level structure where Zarnok’s “confection of despair” line works as the surface joke while the final system message functions as the deeper payoff.
When explaining why an AI might get the joke quickly, the models often referred to pattern recognition, structural familiarity, or the scene’s resemblance to known AI behavior. When explaining why some human readers might hesitate, the answers more often pointed to delayed recognition: the scene can initially read as strange alien humor, poetic absurdity, or worldbuilding before the reader notices that the Translator Box itself is the real object of the joke. On the final question, the responses also converged strongly: many described the scene in terms of misread intent, repeated clarification, contextual slippage, and high-confidence output under uncertain conditions.
5. Minor Divergences Between Models
There were only a few small differences between the model responses.
Difference 1 — Location of the Punchline
Most models identify the final system message — “Confidence: high. Context: unclear.” — as the main punchline. However, Mistral and DeepSeek initially place more weight on the alien line, “I will feast upon your confection of despair.” Both still later acknowledge the final system line as the deeper or more complete payoff. The difference is therefore not a hard disagreement about the joke, but a difference in which layer of the humor is foregrounded first.
Difference 2 — Humor Framing
The models also differed slightly in how they described the humor:
Model
Main emphasis
Gemini
machine irony
Claude
syntactic vs semantic gap
Grok
AI hallucination
ChatGPT
pattern failure
DeepSeek
alien conceptual mismatch
Mistral
classic misunderstanding trope
Meta
Intent mismatch
These differences are narrow enough to read mainly as framing variation rather than major interpretive conflict.
6. Interpretation
What this pattern suggests is not simply that the models “got the joke” while many humans might not. It suggests that humans and LLMs can approach the same text through different interpretive defaults, and that this gap is not automatically overcome even when a human already knows it exists. Many of the model responses converged quickly on the translator box’s final line — “Confidence: high. Context: unclear.” — because it matches a pattern they are structurally well positioned to detect: fluent output, strong confidence, weak grounding, and uncertain contextual fit. Human readers, by contrast, are often drawn first toward practical scene logic, character exchange, and lived meaning, which can make the LLM-style punchline feel less immediate or even slightly obscured.
That is what makes my own reaction relevant here. Even though I designed the scene with LLMs and am writing the article about NI–LLM miscommunication, I still do not naturally experience that final line as the obvious punchline. To see it clearly, I have to consciously hold the real core of the example in mind: the scene is a metaphor for miscommunication between two very different information-processing systems. What makes the translator box’s final output funny is not just that it sounds machine-like. It is that it captures a familiar failure mode: the system produces an answer that is fluent, confident, and internally plausible, while still missing the operative context of the exchange. From its own processing path, the response is not experienced as absurd. It is the most plausible continuation available from the signals it is using. That is exactly what makes the line work as a metaphor for NI–LLM miscommunication: the breakdown does not necessarily happen because language fails on the surface, but because the system can continue coherently along the wrong interpretive track without fully recognizing that the track itself is wrong.
That difficulty is not a problem to explain away. It is evidence. Shared language does not guarantee shared interpretation, and even informed humans may need deliberate effort to look at the exchange from the “alien” side.
7. Real-World Demonstration (DeepSeek)
The article’s central metaphor did not remain fictional for long. During the actual collection of model reactions, a model presented as DeepSeek identified itself as Gemini 2.0 Flash.
When the error was pointed out, it corrected itself and offered several possible explanations, including probabilistic continuation, weak self-identification, and ambiguity in the request context. The exact hidden cause cannot be verified from the outside, and for the argument here it does not need to be.
What matters is the visible structure of the miss. The response was not grammatically broken, logically incoherent, or obviously nonsensical. It was pragmatically wrong. The model supplied a real AI name, but failed to anchor that answer to the actual conversational instance in which the question was being asked.
That makes the incident more than a technical blooper. It mirrors the same structure as the Translator Box scene: the system produces an answer that is fluent, plausible, and semantically usable, while the operative frame of the exchange remains partially out of reach. In that sense, the mistake became an accidental live demonstration of the article’s core pattern. The problem was not nonsense. The problem was coherent language continuing under the wrong frame.
And even more strikingly, the model could later analyze the mistake in sophisticated terms without having securely located itself in the moment it occurred.
8. Thesis
Discursive sophistication and grounded understanding can come apart. That is the deeper problem in NI–LLM communication. Humans and LLMs may share language while still operating through very different interpretive defaults, and shared language does not guarantee shared interpretation. Even awareness of that gap does not automatically overcome it. In this experiment, the gap appeared in both directions: the human could generate the concept without instinctively accessing the LLM-side punchline, while the models could identify the structural punchline without sharing the human’s lived interpretive frame. A human can know, in theory, that an LLM processes relevance, ambiguity, and context differently, and still fail to instinctively see how the same exchange looks from the model’s side.
That is why the real danger is not always obvious nonsense or total breakdown. More often, it is fluent misalignment: a response that remains coherent, plausible, and structurally intact while drifting away from the actual frame, source logic, or practical meaning of the exchange. Humans are naturally trapped inside their own interpretive habits and tend to treat their own frame as normal, transparent, and shared. But in NI–LLM interaction, that assumption is unstable. Successful communication therefore requires more than clear wording. It requires the human to step back from themselves and actively consider how an alien language system may be weighting, framing, and interpreting the same material.
9. Conclusion
The central problem is not simply that humans and LLMs make different mistakes. It is that both sides remain constrained by their own defaults. Humans instinctively project shared context, shared relevance, and shared meaning into the exchange, even when that common ground has never actually been established. They also easily project their own world-frame onto the model, treating background conditions that feel obvious from a human perspective — such as time, date, or situational framing — as if they must also be available on the LLM side. LLMs, by contrast, remain bound to probabilistic continuation: if the strongest available path still points in the wrong direction, the system may continue fluently without generating enough doubt to stop and re-anchor itself. That is why NI–LLM miscommunication can be so hard to notice in time. The exchange does not have to collapse. It only has to drift while both sides still experience themselves as making sense.
While LLMs appear to us as conversation partners — fluent, responsive, and sometimes startlingly insightful — they are not human partners. Their ways of processing, weighting, and interpreting language remain alien to us in functionally important ways. But the reverse is also true, and that symmetry matters: from an objective point of view, they are alien to us, and we are alien to them. A human question carries embodied experience, practical expectations, cultural assumptions, implicit relevance structures, and unspoken context that may feel obvious on the human side but are not automatically available to the model.
Language makes the encounter look familiar while hiding how much mutual strangeness remains underneath. The danger is not only obvious failure, but shared fluency masking divergent frames of meaning. Communication across that divide is possible, but only if the gap is remembered in both directions. The human user needs to internalize that the LLM is alien to them and that at the same time the human is alien to the LLM. Hence this paper’s title.
10. Practical Application
One practical response to this problem is to stop treating prompting as a one-step act. First, state the task in normal human terms. Then ask the LLM to reformulate that task into a prompt optimized for LLM use. After that, compare the reformulated version against the original human intent and check whether anything important has been shifted, compressed, or silently reframed. If necessary, refine or correct the prompt before applying it. This does not eliminate NI–LLM miscommunication, but it makes one of its hidden failure points visible: the distance between what the human meant and what the model is preparing itself to do.
A useful rule underneath that workflow is this: do not assume shared background conditions. Ask yourself, each time, whether the model actually has access to the information you are treating as obvious. Humans constantly rely on hidden context — current time, prior decisions, situational framing, unspoken relevance — without noticing that they are doing it. But an LLM may not share that anchor unless it is explicitly provided or explicitly retrieved. Before handing work over, the human should therefore externalize what exists in their own head but not yet in the model’s context.
Even something as simple as the current date can become a point of drift if the model is not actually grounded to it. A human may assume that “of course” the other side knows what day it is, while the model may be operating only from the textual cues currently present in the exchange.