Sliding Window vs. Full Attention in Code Generation: Token-Level Accuracy on Long-Context Benchmarks

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does sliding window attention in code generation models affect token-level accuracy on long-context programming benchmarks compared to full attention. Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant attention, thanks to their. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does sliding window attention in code generation models affect token-level accuracy on long-context programming benchmarks compared to full attention?

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