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Research2026-08-15 · source-backed
RMM is training-free and input-adaptive, selecting informative slices along contraction dimensions under a single retention-ratio knob. Tested from 1B to 70B across discriminative, autoregressive and long-context settings, reduction tolerance often improved with scale. Custom A100 kernels turned theoretical savings into wall-clock gains, especially at long sequences. arXiv 2608.13426
Each link below shares sources, entities, or timing with this story.
RMM uses Transformer / Shared entity: Transformer / Same source domain / Shared topic / Earlier coverage / Downstream implication
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; reported by the same outlet (arxiv.org).
RMM uses Transformer / Shared entity: Transformer / Earlier coverage
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-06-22.
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-06-21.
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-03-23.
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-03-19.
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-03-07.
Linked by a graph relationship (RMM uses Transformer); both cover Transformer; earlier Transformer coverage from 2026-02-25.
Shared entity: Tested / Same source domain / Earlier coverage / Tension
Both cover Tested; reported by the same outlet (arxiv.org); earlier Tested coverage from 2026-07-30.