Structured Token Retention (STR) introduces a probabilistic framework for dynamically managing how LLMs retain information, optimizing computational efficiency for long sequences.

• Replaces rigid token management with adaptive selection based on contextual importance
• Enables longer effective context windows without proportional computational cost increases
• Demonstrates improved performance on tasks requiring long-range memory
• Creates computational memory paths that mimic human cognitive processes

This engineering breakthrough matters because it addresses a fundamental limitation in LLMs: efficiently handling long contexts while maintaining coherence and reducing computational overhead.

Structured Token Retention and Computational Memory Paths in Large Language Models