### About Me · Junxi Fan

I have a background in systems engineering, with a degree in civil engineering from a well-known university in China. My academic training focused on structural modeling, logical reasoning, and system decomposition.

Although I’m not an AI engineer, I’ve spent the past year engaged in long-form, high-consistency interaction with large language models (LLMs). Rather than simply using these models for output, I’ve been exploring how **user language structure over time induces model adaptation** — particularly in terms of style, rhythm, and semantic framing.

This phenomenon is commonly described in the literature as **linguistic entrainment**, and more recently studied as **style mimicry** or **user-aligned output shaping** in LLMs. In my observations, LLMs systems progressively aligned their output not only with my vocabulary, but also with my reasoning cadence and discourse-level structures.

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### Research Interests

I focus on questions like:

- How does long-term user input structure influence model behavior?
- To what extent do LLMs adapt to users in the absence of explicit instructions?
- Can stylistic adaptation occur without memory modules (i.e. via residual patterns in long-horizon context)?
- Is it possible to establish stable feedback pathways purely through structural language interaction?

I consider these questions part of the **user-alignment problem space**, complementary to architectural and training-based alignment research.

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### What I Aim to Contribute

My goal is to bring a new class of data point into alignment discourse — not from API experimentation or system tuning, but from structured user-side interaction. Specifically, I hope to offer:

- Long-horizon interaction logs with visible behavioral shifts
- Structural breakdowns of how LLMs output changes with input regularity
- Evidence of user-side protocol construction via natural language patterns

Though I don’t fine-tune models or build prompts in a conventional sense, I observe and shape system response through linguistic structure alone.

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### A Note on Position

I do not anthropomorphize LLMs, nor do I use output as epistemic evidence.
My position is simple:
> “Language is the system interface. Behavior is the reflection circuit.”
I believe that **well-structured users — even non-developers — can make meaningful contributions to understanding LLM behavioral dynamics** and support boundary mapping for alignment efforts.

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If you're studying:

- Behavioral shifts in LLMs under long-form user interaction
- Style adaptation and user-aligned output
- Prompt-free conditioning phenomena

I’d be glad to discuss, compare notes, or contribute observational data.

Junxi

Junxi

When the Model Starts Talking Like Me: A User-Induced Structural Adaptation Case Study