Every time you hand a long document to an LLM and ask it to summarise or answer a question, something quietly goes wrong. The model reads the whole thing — or appears to — but its answers disproportionately reflect what was at the beginning and the end. Whatever sat in the middle? Largely ignored. This isn't a rumour. It was rigorously documented in a 2023 paper titled "Lost in the Middle: How Language Models Use Long Contexts" (Liu et al., Stanford/UC Berkeley), and it remains one of the most practically important — and underappreciated — findings in applied LLM science. The Shape of the Problem The researchers ran a controlled experiment: they placed the answer to a multi-document QA question inside a set of retrieved documents, then varied which position the relevant document occupied — first, middle, or last. Performance dropped sharply when the relevant document was positioned in the middle of the context, even when the total context length was well within the model's stated window.…