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.
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Context Engineering | Compaction & Agent Memory for Automated Malware Analysis