TL;DR of the paper, generated automatically.

The paper argues frequent context compaction keeps accuracy, so long-context models may be unnecessary.

• Method: frequent compaction, across long-horizon and long-input tasks.
• Results: frontier models lose little to no accuracy on short contexts.
• Cost: later tokens cost more; the gap may reach 7x on frontier open weight models.