You MUST use a todo list to complete these steps in order. Never move on to one step if you haven't completed the previous step. If you have multiple read steps in a row, read them all at once (in parallel).

Add all steps to your todo list now and begin executing.

## Steps

1. [Read Paper Explanation Preferences]: Read the file at `./documents/research/papers/.preferences.yaml` and analyze its contents (Load existing preferences if any)

2. [Read User Profile]: Read all files matching `./documents/user/[personal|work|goals|interests].md` and analyze their contents (Check what we already know about user's background)

3. Help the user set their expertise levels for paper explanations.

Ask about their background in key academic domains:
- Machine Learning / AI
- Mathematics / Statistics
- Physics
- Biology / Life Sciences
- Computer Science (systems, theory, etc.)
- Other fields they mention

For each relevant domain, capture their level as: beginner, intermediate, or expert.

Also ask about preferences:
- Default explanation depth: concise, standard, or detailed
- Whether to include mathematical intuition or skip it entirely
- Any specific interests within domains


4. Write the user's preferences to `./documents/research/papers/.preferences.yaml` in this format:

domain_expertise:
  machine_learning: [level]
  physics: [level]
  [other domains]: [level]
preferences:
  default_depth: [concise/standard/detailed]
  include_math: [true/false]
  interests: [list of specific areas]

Confirm what you've saved: "Got it—I'll explain ML papers concisely and go deeper on physics. Saved for next time."