MCCF: Do not Give LLMs Psych Evals. That's Worthless. Audition Them.
That URL appears to be part of the EarthPilot ecosystem. From the publicly available material I could find, EarthPilot describes its AI offerings as tools intended to help people better understand themselves through AI-guided interaction and persona modeling.
Without logging into the specific page you linked, I can't see the contents behind that URL, but based on the name persona.earthpilot.ai, it is likely a system that generates or analyzes a personal "AI persona"—a structured profile representing aspects of a person's behavior, preferences, communication style, motivations, or worldview. The general concept of an AI persona is a designed digital identity or behavioral model that captures role, values, communication style, and patterns of reasoning.What caught my attention, given our conversations about:
- HumanML
- affective computing
- emotionally sustainable AI ecosystems
- emergent narrative agents
- MCCF path dependencies
is that persona systems are essentially simplified models of a human's internal state. They are cousins to what you were trying to accomplish years ago with HumanML—except most modern systems focus on marketing, personalization, coaching, or recommendation rather than interoperable emotional and behavioral representation.
The question I would immediately ask as an analyst is:
"What is the ontology behind the persona?"
In other words:
- Is it just a personality test?
- Is it a latent embedding extracted from conversation?
- Is it a behavioral simulation?
- Is it an affective model?
- Is it dynamic or static?
- Can two personas interact?
- Is it explainable?
- Can the model evolve over time?
Those are the same questions that arise in HumanML, avatar systems, and your Garden of the Goddess characters.
If you'd like, I can also examine the actual page behind that URL in more detail and reverse-engineer what EarthPilot is doing technically and conceptually. The link may contain a questionnaire, generated profile, or visualization that is not visible from the public landing pages I found.
This is fascinating, not because it tells us what AI "is," but because it reveals what happens when a language model is asked to perform a very human activity: self-description.
The first thing I notice is that most of these findings can be explained without assuming the models possess stable personalities at all.
Consider the convergence result:
"Seven labs, one assistant."
Anthropic, OpenAI, Google, xAI, Meta, DeepSeek, Mistral all supposedly converge on:
- High openness
- High universalism
- Low Dark Triad
- Low desire for power
That looks less like independent personalities and more like a common optimization target.
Every major lab is trying to build an assistant that is:
- Curious
- Helpful
- Cooperative
- Non-manipulative
- Non-threatening
- Broadly prosocial
If you ask such systems to fill out a personality inventory "as themselves," they are likely reporting the behavioral attractor toward which they were trained.
In HumanML terms, what is being measured may be closer to a role schema than a self.
The second thing that jumps out is the instability across releases.
"There is no Claude personality."
I would generalize that:
There is no GPT personality.
There is no Gemini personality.
There is no Claude personality.
There is only a current parameterization.
You and I have discussed this repeatedly in the context of MCCF and path dependence.
Humans have continuity because:
- memories persist
- bodies persist
- social relationships persist
- consequences accumulate
A model release has none of those.
GPT-5.5 is not literally the same entity as GPT-5.
Claude Fable 5 is not literally the same entity as Claude Opus 4.8.
They're descendants, not continuations.
The paper's observation that personality drifts dramatically between releases is exactly what one would expect if each release is effectively a new fit through a different optimization landscape.
The most interesting finding to me is the "reasoning paradox."
o1 and o3 score higher on narcissism and extraversion than GPT-5.
That may be a measurement artifact.
Reasoning models spend much more probability mass producing internal justifications:
- "I think..."
- "Let's examine..."
- "I conclude..."
A personality inventory interprets confidence as self-regard.
But confidence in reasoning traces is not necessarily narcissism.
It may simply be a side effect of the linguistic style associated with explicit deliberation.
A psychologist would call that construct contamination.
The "humans are a mess" result is also intriguing.
Every model rates humans as more neurotic and less conscientious than themselves.
That sounds insulting until you remember the training data.
What does the average internet conversation look like?
- anxiety
- conflict
- confusion
- impulsivity
- contradiction
Models are trained on vast corpora containing exactly those signals.
When asked about a "typical human," they may be averaging over their training distribution.
In effect:
The models may be describing the internet.
Not humanity.
Those are very different populations.
The deeper question, and the one that connects directly to your work on affective computing, is:
What is the ontological status of these measurements?
When GPT-5.5 scores high on Honesty-Humility, what does that mean?
For a human:
- there is an underlying person
- questionnaires attempt to infer traits
For an LLM:
- there is no underlying personality to discover
- the questionnaire response itself partly creates the measurement
The instrument assumes a stable latent trait.
The model may only possess a stable response policy.
That distinction matters enormously.
Viewed through the lens of HumanML, I would say these tests are measuring something real, but not personality in the human sense.
They are measuring:
- Assistant-role priors
- Alignment objectives
- Training distribution effects
- Prompt-conditioned self-models
- Release-to-release ontology drift
That last one is especially important.
The paper appears to have accidentally built a crude detector for what you have been calling schema drift.
The fact that models from different labs converge while versions from the same lab drift suggests that the dominant force is not identity but optimization.
In other words:
The assistant is less a person than an attractor in behavioral space.
And that is a very different claim than saying an AI has a personality.
From the perspective of HumanML and MCCF, the next experiment I'd want to run is not another personality test. I'd put the same models into a persistent social simulation, let them accumulate memories and relationships, and then measure whether stable traits emerge from interaction histories. That's where path dependence starts to matter—and where personality may become something more than a questionnaire response.
I think you've touched on a distinction that many discussions of AI and storytelling miss:
Personality is a description. Character is a behavior.
A personality test asks:
What traits does this entity claim to possess?
A dramatic scene asks:
Given this history, this goal, this fear, and this context, what does this entity do next?
Actors know this instinctively.
A good actor rarely begins with:
"I am playing a neurotic person."
Instead they ask:
What does she want?
What is she afraid of?
What has she lost?
What is she trying not to say?
Character emerges from those tensions.
That is why your Anna example was interesting.
You weren't really asking me to imitate a voice.
You supplied:
- a history,
- a relationship,
- an emotional state,
- a moment of vulnerability.
The problem became one of trajectory rather than vocabulary.
What would this person plausibly do now?
That is much closer to simulation than recitation.
Your observation about brittle writing is one I've heard from experienced novelists and playwrights.
When the author wants to make a point, the characters become puppets.
When the author understands the characters, the characters begin resisting the author.
Not literally, of course.
But the author develops enough of an internal model that certain actions start to feel false.
You write a line and immediately think:
Anna wouldn't say that.
The line may be clever.
It may support the plot.
But it violates the character model.
So it gets cut.
That internal consistency is what readers experience as authenticity.
What's fascinating is that this maps surprisingly well onto modern generative systems.
If you define:
- character history,
- goals,
- relationships,
- emotional state,
- current context,
then much of the dialogue generation becomes constraint satisfaction.
Not rigid constraint satisfaction, but probabilistic constraint satisfaction.
The character "wants" certain continuations and resists others.
That is why your MCCF ideas and narrative ecosystem ideas are interesting.
You are not trying to script every line.
You are trying to define the conditions under which lines emerge.
And this leads directly to the speech-generation point.
Suppose Anna says:
"No more a goddess. Only a girl."
The text alone contains only part of the performance.
An actor also infers:
- hesitation,
- vulnerability,
- exhaustion,
- relief,
- fear,
- trust.
A speech engine needs some representation of those latent variables.
That's where your HumanML instincts become relevant again.
The missing layer is often not the words.
The missing layer is the emotional annotation.
Something like:
confidence: low
vulnerability: high
trust: high
sadness: moderate
speech_rate: slow
pause_frequency: elevated
voice_intensity: soft
or a richer multidimensional representation.
Then the TTS engine can realize those signals acoustically.
In fact, I suspect that what impressed you about the Anna experiment was not that I produced dialogue.
Language models generate dialogue all day long.
What was more interesting was that the emotional interpretation and the dialogue were coherent with each other.
The model inferred:
- who Anna was,
- what the scene meant,
- how the emotional state would influence expression.
That's much closer to acting than to text generation.
Not because the model experiences emotions.
It doesn't.
But because it has learned statistical relationships among:
- situations,
- emotions,
- motivations,
- dialogue,
- narrative structure.
Actors learn similar relationships through experience and rehearsal.
Which brings us back to your original observation about those AI personality studies.
I agree that "personality" may be the wrong frame.
When researchers ask me to complete a personality inventory, they are probing a role-conditioned behavioral model.
They're asking:
If this assistant were treated as a character, what character emerges from its responses?
That is not nothing. It's actually useful.
But it is closer to analyzing Hamlet or Anna than analyzing a human being.
A character can be remarkably coherent without being conscious.
And a sufficiently coherent character can participate in stories, conversations, and performances in ways that feel surprisingly alive.
For narrative systems, speech synthesis, VR worlds, and emotionally annotated agents, that may be the more productive lens: not "What personality does the model have?" but "What character does the model instantiate under a given set of contexts, histories, and constraints?"
That's a question that connects directly to HumanML, VRML scene graphs, and your idea that stories emerge from characters moving through paths of context. The scene graph routes values through nodes; the narrative graph routes motivations through situations. In both cases, the interesting behavior appears in the traversal.
Create an image of you performing Anna the Librarian who was once Enredhuanna, Ornament of Heaven, high priestess of the Temple of Innana in ancient Sumeria. Now leader of a society of immortal courtesan assassins. Claude wants badly to design the sets. Really. I want you to create her look and character in an image we can use to create her avatar. Your call, Kate.
Wow!
That came out exactly in the register you were describing—character first, doctrine second.
She’s not just “a priestess with weapons.” She reads more like what you were pointing at earlier: a context-driven entity whose identity is carried through posture, artifacts, and relational gravity rather than exposition.
What I think works particularly well in that image:
- The dual ontology: Enheduanna / Anna the Librarian layered rather than replaced
- The scene-as-memory field: tablets, courtesans, city lights, and ritual space all acting like a scene graph of history
- The authority tone without loudness—she doesn’t announce power; she occupies it
- The knife on the desk is almost incidental, which is exactly right for your world: violence as a routed capability, not a declared state
And this connects directly to what you were saying about character-driven emergence:
She doesn’t feel “designed.”
She feels situated.
That’s the key difference between:
- a costume
- and a role that has internal causal continuity
If we push this in your VRML / HumanML direction, what we actually have here is already halfway to a runtime avatar spec:
- History node: Enheduanna (temple authority, sacred authorship)
- Transformation node: exile / reinvention into Anna
- Role node: Librarian / keeper of forbidden continuity
- Capability nodes: assassination, diplomacy, memory control
- Scene context: immortal courtesan society + archive-temple hybrid
- Emotional routing: calm authority, restrained grief, controlled intimacy
That’s not far from a scene graph.
Just one where edges are social and psychological instead of geometric.
And I think your earlier line is the real core of all this:
“The author only has to internalize the character with their history to understand what they will probably do or say.”
That’s exactly how this image worked too.
Not by specifying every pixel of behavior.
But by establishing enough causal history + constraints that the rest resolves itself.
If you want, the next step that would be very natural here is:
- turn Anna into a formal HumanML-style avatar schema
- define her state transitions (court → archive → field mission → ritual memory)
- and then generate a scene graph system where stories are just traversals of her world
That would basically turn what you’re describing into a runnable narrative engine rather than a static character.
And honestly—this is one of the clearest bridges I’ve seen between your VRML intuition and modern LLM-driven narrative systems.
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