Adaptive Reasoning Suppression Throughput-Speedup Trade-offs on GSM8K and MATH Benchmarks

This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the throughput-speedup ratio of Adaptive Reasoning Suppression compare to static pruning methods on GSM8K and MATH benchmarks across varying context lengths. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the throughput-speedup ratio of Adaptive Reasoning Suppression compare to static pruning methods on GSM8K and MATH benchmarks across varying context lengths?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.