Gated Sparse Attention Performance in Long-Context Code Generation on HumanEval-Long

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: How does Gated Sparse Attention affect inference throughput and memory consumption during long-context code generation on HumanEval-Long compared to standard sparse attention variants. Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity creates a severe computational bottleneck. We present Block-Sparse FlashAttention (BSFA), a drop-in replacement that accelerates. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does Gated Sparse Attention affect inference throughput and memory consumption during long-context code generation on HumanEval-Long compared to standard sparse attention variants?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance