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.

Will do—hit me up if anything trips up. Thanks!