Probably a key reason Walrus avoids weak participation has to do with how WAL rewards change over time based on real performance, instead of fixed expectations. It does not set all providers equal once they join the protocol. In other words, the flow of reward adjusts constantly in accordance with how well each provider performs the responsibility thrown at them.

@Walrus 🦭/acc monitors availability consistency, response reliability, and long-term behavior. Providers whose performance consistently meets the threshold receive predictable $WAL rewards. Those that fail checks or behave erratically see their reward efficiency decrease, rather than being immediately kicked out. This gradual punishment discourages tactics of short-term participation strategies while favoring providers that can operate reliably for a longer period.

Reward decay plays an important role here. If a provider's performance drops, $WAL earnings decrease proportionally.
This makes poor behavior economically unviable without instantly destabilizing the network. At the same time, providers that improve their performance can recover reward flow, allowing the system to self-correct instead of permanently punishing mistakes.

This mechanism protects the network from incentive abuse. Providers cannot maximize rewards by briefly joining, earning, and exiting. Since $WAL rewards depend on ongoing behavior, participation becomes a continuous commitment rather than a one-time opportunity.

For users: This makes them have stronger guarantees. The responsibilities of data continue to be enforced not on trust but on economic pressure that adapts automatically. The network does not depend on manual governance.

By aligning WAL rewards based on performance trends rather than fixed rules, #walrus incentivizes the economic system according to healthy network longevity. The dynamic reward system promotes integrity in addition to improved reliability as the protocol scales.