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By formulating the indexer as a standard dual-encoder architecture, we train it independently using standard retrieval training frameworks without ever loading the massive backbone model into GPU memory.\n  We demonstrate that this \"less is more\" paradigm significantly maximizes serving efficiency while acting as an effective attention denoiser in tasks that rely on long-term globa","title":"FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention","url":"https://arxiv.org/abs/2606.09079","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.09079v1 Announce Type: cross \nAbstract: Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving. In this report, we propose Lookahead Sparse Attention (LSA), a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek-V4 architecture. 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