An AI development team has a language model that was trained exclusively on documents with a maximum length of 4096 tokens. When they try to use this model for summarizing new, longer documents of up to 16384 tokens, they observe a significant drop in quality. The model seems to lose coherence and disregard information from the beginning of the longer documents. The team cannot afford to retrain the model from scratch. Based on this scenario, explain the fundamental issue with how the model is processing the positions of tokens in the longer documents. Then, describe the core mechanism of a technique that could resolve this issue by re-mapping the token positions.