Long-context anxiety is getting strange: the context window has already rolled from 128K to 1M, but the LLM's extraction accuracy on buried evidence hasn't risen with it. The UIUC team led by Yanjun Zhao, in arXiv:2607.02509 released on July 2, quantifies this rather harshly — in a 128K input, the top 0.1% of tokens carry 50%–80% of the cumulative question-relevant signal, meaning 128 tokens actually do the job of "reading the entire long document". The paper's solution, RECONTEXT, is a training-free, no-backbone-change inference harness. During prompt construction, it directly calls the model's own attention scores to circle the "most question-relevant" evidence spans, explicitly and verbatim reorders these spans close to the question, and only then has the model generate. The original 128K is left untouched, and forward-pass cost is essentially on par with standard inference. Across 8 128K long-document benchmarks and 3 backbones (Qwen3-4B / Qwen3-8B / Llama3.1-8B), RECONTEXT gets the best average rank, lifting average accuracy from 0.24 to 0.30, a relative gain of 24.6%. The accompanying theoretical analysis is also interesting: it compares context to associative-memory storage, the question to a retrieval cue, attention to cue-trace association, and replay to trace reactivation — and additionally proves the monotonic improvement of recursive replay on hidden representations. Two practical boundaries worth flagging: first, you must have access to attention weights, so black-box APIs are out — only self-hosted Qwen / Llama3 instances qualify; second, it is naturally incompatible with DAC and summary-style compression — running both at the same time causes "double replay / conflicting evidence-selection standards" — the paper doesn't provide a fix, so you must turn off upstream compression before going to production. ReContext is a rare "almost free" inference optimization, but whether it can actually land in RAG, long code agents, and compliance-audit pipelines depends on whether engineering is willing to leave it an attention hook.