Weight-Only Quantization Trade-Offs in LLaVA for Cross-Modality Reasoning on TextVQA

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and cross-modality reasoning performance when applying weight-only quantization to LLaVA on the TextVQA dataset. Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locations, ambiguities, and variations in the real-world environment can be better described in human. 11 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the trade-off between inference latency and cross-modality reasoning performance when applying weight-only quantization to LLaVA on the TextVQA dataset?

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