Compound Learning: λ(ο,Κ).τ Self-Improving Holon

Core transformation: Task(ο) + Knowledge(Κ) → Output(τ) + Knowledge'(Κ') where Κ' ⊃ Κ (knowledge strictly grows)

Foundational Insight

Traditional workflows treat each task in isolation. Compound learning treats every task as a learning opportunity that improves future performance. Like compound interest, small improvements accumulate exponentially: each unit of work makes subsequent units easier, faster, and higher-quality.

Interest:   A(t) = P(1 + r)^t
Learning:   Κ(t) = Κ₀ × Σᵢ(1 + εᵢ)   where εᵢ = learning from task i

Architecture

Four-Phase Workflow