SentinelLABS evaluated OpenAI's native compaction feature in the Responses API for automated binary analysis workflows. Compaction reduced input tokens by approximately 86% with no aggregate score degradation, but domain object modeling quality decreased, indicating that structural reasoning can be flattened during context compression. The research advocates for a context-engineering strategy that separates compacted working memory from durable artifact storage and treats compaction as lossy until validated.