By compacting context through selective truncation rather than rephrasing, agents can maintain performance on long-horizon coding tasks while reducing inference costs significantly—making test-time scaling more economical.
CliffCompaction is a technique that compresses long conversation histories for AI coding agents by selectively removing less important content while keeping everything else unchanged. This cuts costs by up to 50% while maintaining performance, enabling agents to work on complex coding problems that span millions of tokens across multiple sessions.