Mamba and FlashAttention Throughput on Long-Sequence Code Generation Benchmarks

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference throughput of Mamba-based selective state space models compare to FlashAttention-optimized Transformers on the HumanEval+ code generation benchmark for sequences exceeding 32k. 13 claims were extracted from source literature; 12 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference throughput of Mamba-based selective state space models compare to FlashAttention-optimized Transformers on the HumanEval+ code generation benchmark for sequences exceeding 32k tokens?

Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.