Random Attention: How AI Gets Faster by Forgetting

Salesforce AI Research’s Random Attention method rethinks KV-cache eviction during long chain-of-thought reasoning. By protecting the original prompt and randomly discarding redundant generated tokens across attention heads, it matches sophisticated scoring methods while delivering 32–43% higher throughput—showing that, for AI memory, selective messiness can be remarkably efficient. Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information. Sponsored by Embersilk LLC