Compacting at all is a mistake. With 1m context window there is no reason for a single task to require compaction.

Much better to spend tokens breaking the task into chunks, documenting and storing them durably, then executing each one in clean context and just /clear after.

It’s a similar concept to compaction, just planned in advance. Much much more effective, and doesn’t burn tokens and time (“wall-clock”, Claude) doing the compaction.

This is the way.

Most models’ reasoning abilities drops off significantly between the 256K-1M token ranges of the context window. There’s too much stuff to “pay attention to” at once.