Across the three models and five workloads, the paper summarizes ASPIRE as delivering 1.70–4.58× AutoRegressive decoding throughput and roughly 27% higher average speedup than the strongest prior self-speculative baselines.

Across the three models and five workloads, the paper summarizes ASPIRE as delivering 1.70–4.58× AutoRegressive decoding throughput and roughly 27% higher average speedup than the strongest prior self-speculative baselines. · CiteArk

Measurement results

0/3 assessed

0 supported0 disputed or conflicting3 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Reported	Latest observation	Evidence judgment
1.7 × AutoRegressive	—	Not assessed
4.58 × AutoRegressive	—	Not assessed
27%	—	Not assessed

Across the three models and five workloads, the paper summarizes ASPIRE as delivering 1.70–4.58× AutoRegressive decoding throughput and roughly 27% higher average speedup than the strongest prior self-speculative baselines.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results