How does context compression improve long-context ChatGPT performance?

Context compression reduces token usage by summarizing or encoding long history into compact representations.

As conversations grow, transformer context windows become saturated. Context compression techniques reduce memory and computation by summarizing older parts of the conversation or encoding them into dense embeddings.

Methods include hierarchical summarization, learned memory tokens, and retrieval-based compression. These approaches allow models to retain semantic meaning without storing full token histories.

This improves scalability and reduces latency while preserving long-term conversational coherence.