Triple-loop meta-learning: learning process architecture

[Copilot]

Completes the learning loop trilogy by analyzing how we learn to learn. Repository had Loop 1 (technical insights) and Loop 2 (questioning assumptions); missing was Loop 3 examining the learning process itself.

Changes

Core analysis (1,617 lines):

• TRIPLE_LOOP_LEARNING.md - Examines how three learning loops interact recursively
• 16 key insights: recursive enhancement, self-modifying learning, mode-switching as learnable skill
• Meta-strategies for choosing which loop to engage
• Architecture patterns for triple-loop AI systems

Practical references (420 lines):

• THREE_LOOPS_QUICK_GUIDE.md - Decision tree for loop selection, signals/triggers, integration rhythms

Synthesis (448 lines):

• LEARNING_EVOLUTION.md - Journey through all three loops, compounding effects

Navigation (733 lines):

• LEARNING_MAP.md - Visual guide with reading paths by audience/goal

Updated: README.md with meta-learning resources section

Key discoveries

1. Recursive ecosystem: Loops transform each other bidirectionally, not hierarchical
2. Compounding capacity: Each meta-level multiplies future learning ability (non-linear)
3. Mode-switching: Choosing loops is learnable meta-skill with identifiable triggers
4. Helical pattern: Spiral upward in abstraction while grounding in practice
5. Epistemological pluralism: Each loop accesses different knowledge types

Triple-loop framing

Loop 1: "RMSNorm is 20% faster"
Loop 2: "Why do we value speed?"
Loop 3: "How do we decide what to value? Can we improve that process?"

The meta-insight: learning itself is a design space. Systems can learn not just content, but how to learn, and how to improve their learning process.

Enables building AI systems that don't just learn, but learn to learn to learn—continuously expanding their own learning capacity.

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[danregima]

cooool