Multimodal Enhancements in Qwen2.5 and Their Impact on Code Generation Benchmarks

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does the addition of multimodal capabilities affect Qwen2.5's performance on the HumanEval Pro and MBPP Pro benchmarks compared to text-only models. We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented with a base problem and a related, more complex problem. 11 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the addition of multimodal capabilities affect Qwen2.5's performance on the HumanEval Pro and MBPP Pro benchmarks compared to text-only models?

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