This research introduces a technique that lets large language models intelligently manage their own memory by identifying which information to keep and which to discard during processing of very long texts.

• Addresses a critical memory bottleneck when processing contexts of 128K-1M tokens
• Leverages the model's own attention patterns to determine which tokens can be safely evicted
• Demonstrates that LLMs implicitly "know" which information is important to retain
• Achieves significant efficiency gains without compromising performance

For engineering teams, this approach offers a practical path to deploying long-context models without requiring specialized hardware or drastic architectural changes, making advanced AI capabilities more accessible and cost-effective.

LLMs Know What to Drop: Self-Attention Guided KV Cache Eviction for Efficient Long-Context Inference