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Translated from Korean
Most of you have likely experienced the context window filling up and token costs skyrocketing as AI agent tasks get longer. Typically, we use a sliding window to cut off past conversations or apply simple summarization, but this always creates a trade-off where important context is lost.
A 'Reversible Forgetting' pattern proposed in a recent paper offers a fairly practical alternative. The agent decides for itself to swap long execution logs for a summary, while keeping only a reference pointer so that if the detailed context is needed later, it can restore the original log. Instead of just deleting it, it's more like folding it away for a while.
When building complex multi-step agents, this pattern can significantly reduce token consumption while preventing context pollution, making it worth considering when implementing AI SDKs or agent workflows.
Translated from Korean
The 'B20' token standard recently announced by Base via the Beryl upgrade is quite intriguing. The core idea is to offload token logic processing to Rust precompiles embedded in the node software, rather than deploying token logic as EVM bytecode as we've traditionally done. This results in a 50% reduction in transfer fees and a significant performance boost, while maintaining hybrid compatibility with existing ERC-20 tokens.
The fact that regulatory compliance features like role-based minting and blacklisting (freeze-and-seize) are provided as a basic toolkit seems to be a major practical shift. This could prove to be an attractive alternative for builders who have had to deal with the headache of writing and auditing custom regulatory-compliant token contracts in Solidity every time. It seems L2s are now moving beyond simple computation acceleration into a phase of squeezing out performance through node-level Rust implementation. I'll definitely have to dive deep once the code implementation is released. lol
