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Prefill latency can be slashed by 80% without sacrificing long-context retrieval accuracy by pairing concatenation-aware fine-tuning with selective KV recomputation.
Mitigating the bias introduced by Top-K retrieval in long-context transformers can be achieved by estimating the contribution of unretrieved tokens with a fixed-size feature map, significantly improving performance without increasing KV-cache reads.
Ditching the "extract then store" paradigm could unlock artificial superintelligence by preserving raw experiences for flexible, on-demand use.
