Unverified50% confidenceBenchmarkExact time
Under the same experimental setup, at context length 1000 the estimated FLOPs drop by about 1000x, while single-token latency drops only about 6x.
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10/18/2026
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UnverifiedThe architecture achieves a roughly 1.9× reduction in FLOPs while retaining a substantial portion of attention-level performance.71% similarUnverifiedFLOPs thresholds fail to reflect advances in algorithmic efficiency — capabilities that today require 10²⁶ FLOPs may in the future be achievable with less compute.67% similarUnverifiedA sampling rate of 1 frame per second will miss instantaneous actions lasting less than 1 second, which is a trade-off between temporal resolution and computational cost65% similarUnverifiedAttention computation on long sequences is a typical memory-bound operation, where the theoretical FLOPs utilization is very low but bandwidth is nearly saturated.64% similarUnverifiedReducing a model's floating-point operations (FLOPs) does not necessarily make the model run faster.64% similar
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