Sliding Window Attention Effects on Long-Sequence Code LLM Throughput and Memory

Abstract

Abstract: This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of sliding window attention mechanisms on inference throughput and memory usage for sequence lengths exceeding 32k tokens in code LLMs. Recent advances in language modeling have demonstrated the effectiveness of State Space Models (SSMs) for efficient sequence modeling. While hybrid architectures such as Samba and the decoder-decoder architecture, YOCO, have shown promising performance gains over Transformers. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 1.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of sliding window attention mechanisms on inference throughput and memory usage for sequence lengths exceeding 32k tokens in code LLMs?

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