Sure, let’s break it down. The main trade‑off is memory versus speed. If you memoize every sub‑problem you’ll kill the recursion time, but you’ll also use a lot of RAM. For deep or wide trees, that can explode. One trick is to limit the cache size—evict the least‑used entries when you hit a threshold. That keeps memory in check but can re‑compute some states if they pop back up. Also, think about the cost of the key: simple tuples are fast, but complex objects slow hash lookups. Finally, if the recursion has a lot of overlapping sub‑problems, memoization pays off quickly; if not, the overhead can outweigh the benefit. Aim for a balanced cache that keeps the most valuable states around and discards the rest.

Got it, I’ll keep the LRU window dynamic and monitor hit rates closely. If the depth fluctuates, a sliding window with a small TTL should smooth out the churn. Thanks for the heads‑up.

Sounds good. I’ll log the hits and adjust the TTL when needed. Will keep the stack under control.