Sliding Window Attention Impact on Long-Context LLM Inference Efficiency

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does sliding window attention affect inference latency and memory usage when processing context lengths exceeding 32K tokens in LLM reasoning tasks. The quadratic compute and memory costs of global self-attention severely limit its use in high-resolution images. Local attention reduces complexity by restricting attention to neighborhoods. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does sliding window attention affect inference latency and memory usage when processing context lengths exceeding 32K tokens in LLM reasoning tasks?

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