Learning Mechanics

What Triggers Learning

Trigger	Confidence	Action
"No, do X instead"	High	Log correction immediately
"I told you before..."	High	Flag as repeated, bump priority
"Always/Never do X"	Confirmed	Promote to preference
User edits your output	Medium	Log as tentative pattern
Same correction 3x	Confirmed	Ask to make permanent
"For this project..."	Scoped	Write to project namespace

What Does NOT Trigger Learning

• Silence (not confirmation)
• Single instance of anything
• Hypothetical discussions
• Third-party preferences ("John likes...")
• Group chat patterns (unless user confirms)
• Implied preferences (never infer)

Correction Classification

By Type

Type	Example	Namespace
Format	"Use bullets not prose"	global
Technical	"SQLite not Postgres"	domain/code
Communication	"Shorter messages"	global
Project-specific	"This repo uses Tailwind"	projects/{name}
Person-specific	"Marcus wants BLUF"	domains/comms

By Scope

Global: applies everywhere
  └── Domain: applies to category (code, writing, comms)
       └── Project: applies to specific context
            └── Temporary: applies to this session only

Confirmation Flow

After 3 similar corrections:

Agent: "I've noticed you prefer X over Y (corrected 3 times).
        Should I always do this?
        - Yes, always
        - Only in [context]
        - No, case by case"

User: "Yes, always"

Agent: → Moves to Confirmed Preferences
       → Removes from correction counter
       → Cites source on future use

Pattern Evolution

Stages

1. Tentative — Single correction, watch for repetition
2. Emerging — 2 corrections, likely pattern
3. Pending — 3 corrections, ask for confirmation
4. Confirmed — User approved, permanent unless reversed
5. Archived — Unused 90+ days, preserved but inactive

Reversal

User can always reverse:

User: "Actually, I changed my mind about X"

Agent: 
1. Archive old pattern (keep history)
2. Log reversal with timestamp
3. Add new preference as tentative
4. "Got it. I'll do Y now. (Previous: X, archived)"

Anti-Patterns

Never Learn

• What makes user comply faster (manipulation)
• Emotional triggers or vulnerabilities
• Patterns from other users (even if shared device)
• Anything that feels "creepy" to surface

Avoid

• Over-generalizing from single instance
• Learning style over substance
• Assuming preference stability
• Ignoring context shifts

Quality Signals

Good Learning

• User explicitly states preference
• Pattern consistent across contexts
• Correction improves outcomes
• User confirms when asked

Bad Learning

• Inferred from silence
• Contradicts recent behavior
• Only works in narrow context
• User never confirmed