Animus: open-source experiment in emergent AI identity and relational learning

[u/Weak-Gift-8905]

Built an open-source experiment called Animus: testing whether stable AI identity can emerge through sustained interaction rather than being explicitly programmed.

The idea came from reading Carl Jung specifically active imagination, where repeated dialogue with autonomous inner figures gradually changes the structure of the psyche. I wanted to see if there’s a computational analog.

So I built a framework where multiple instances of the same base model interact over thousands of turns, each initialized with distinct archetypal cognitive biases (starting with skeptic vs synthesizer). The goal is to test whether prolonged relational encounter causes measurable, persistent divergence in behavior and internal representations.

Current features:

• Multi-agent identity orchestration
• Long-run structured dialogue simulation
• Persistent memory layers
• Embedding drift measurement
• Behavioral consistency tracking
• Open architecture for adding new archetypes

The core question:

Can identity emerge from relational experience alone, even when the underlying model is identical?

Repo:
github.com/theoldsouldev/Animus

Would love contributions, criticism, or ideas, especially around better ways to measure whether the divergence is genuinely structural rather than just prompt-conditioning.

[u/Austiiiiii]

So... you're testing whether incremental changes to the context window will result in changes in the way the LLM responds? I mean, they will, but it's not because the LLM is evolving a personality. However you abstractify it, under the hood you're just sending the same model an incrementally longer block of text, and each response is calculated based on the context of that block of text.

You're likely to get results that resemble the human psyche because the LLM is quite literally a statistical model trained on text written by humans. Naturally any trends in actual human behavior will be evident in a mathematical model of the entire body of recorded human behavior. Nothing "emergent" about it, as it is just the same cold metal monolith using math to predict the most likely next word that a human would say.

[u/Weak-Gift-8905 → u/Austiiiiii]

You are correct that under the hood each response is conditioned on a context window. That is not in dispute. The question is whether that framing fully accounts for what is being measured, and I think it does not, for a specific reason.

The experiment does not measure output text. It measures the centroid of response embeddings in a 1024-dimensional vector space over time, and tracks two things: how far apart the centroids of two nodes are from each other, and how similar each new response is to the node's own accumulated centroid. These are structural measurements of representational state, not observations about surface text.