When talking about on-chain data, the first word that comes to many people's minds is "immutability." It sounds reasonable, but once you've been operating for a while, you'll encounter the awkward reality—sometimes you need to look back.

Not to modify the data, but to clarify what happened, trace the root cause of issues, and conduct risk audits. These are all normal business processes.

Here's the problem: if data can only move forward and the system can't clarify the sequence of events, its practical value is actually depreciating over time. Real applications need to understand how a certain state gradually evolved into the current one, rather than just focusing on the final result.

Walrus's approach is quite interesting; it doesn't take an aggressive route. It doesn't deny the value of immutability but instead incorporates "state evolution" into the verification mechanism. The result is: object IDs remain unchanged, each update can be traced back, data won't be casually overwritten, and historical versions won't be buried.

Based on publicly available information from the testnet, this scheme supports multiple updates to the same object, with stable reference addresses. A single object can reach MB-level size, enough to support real business data needs.

So, what do I think? Once applications start genuinely focusing on historical trajectories rather than just current snapshots, the advantages of this design will become more apparent. But there's a prerequisite—the network itself must be stable. If there aren't enough participating nodes, multi-layered evolution tracking might become a burden instead.

However, to put it positively, this approach indeed addresses a real pain point.