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Short notes on continual learning, memory module design, and adaptive computation.
Why non-stationarity should be treated as the default setting for adaptive agents.
A fixed model is always smaller than the world it encounters. Data distributions move, environment dynamics change, and useful knowledge arrives after deployment. We therefore treat continual adaptation as part of the problem definition rather than as a repair step for exceptional cases.
Selective retrieval, consolidation, and controlled experience reuse as design problems.
An agent's memory module should not be an archive of everything that happened. It is a controlled mechanism for selecting useful experience, retrieving it as context changes, and consolidating it without erasing earlier capabilities.
Why an agent should spend more inference effort only when a task demands it.
Problems do not arrive with equal difficulty. Adaptive computation asks an agent to recognize when a routine response is sufficient and when additional search, verification, or reasoning is worth the cost.