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Transition rates:

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3.2. Temperature Function

We define repository temperature T(t) as a weighted Euclidean norm: State: (\mathbfs(t)) + current n , a , c

5.3. Reinforcement‑Learning (RL) Policy

• State: (\mathbfs(t)) + current n, a, c.
• Action: Discrete choices ( \textincrease/keep/decrease n, a, c).
• Reward: