Unverified50% confidenceFactExact time
SR-SWA makes per-step decoding latency and memory usage barely grow with document length.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe SWA-bounded replay mode causes most early tokens in long input prompts to pass through only about half of the model's layers, while only tokens within the most recent window undergo full computation across all layers, trading some accuracy for reduced speed and memory usage.71% similarUnverifiedTechnical solutions for long-term memory involve trade-offs: compressed summaries are simple to implement but distort details; RAG retrieval is more precise but latency and costs rise with scale; state graphs have high maintenance costs but the highest predictability of behavior.66% similarVerifiedThe KV cache VRAM usage of ultra-long context windows grows linearly with context length, causing inference latency and costs to rise significantly.65% similarUnverifiedThe model uses a bidirectional attention encoder architecture, completing inference in a single forward pass, with constant-level latency that does not grow linearly with content length.65% similarUnverifiedMeans of mitigating context overflow include retrieval-augmented generation (RAG), conversation summary compression, and external state storage, but these all increase latency and engineering complexity.65% similar
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