A new paper on arXiv introduces ChartEvo, a system designed to improve chart understanding in multimodal large language models (MLLMs). The abstract notes that while modern MLLMs handle common charts well, their performance is highly uneven across the full range of chart types.
ChartEvo is described as a self-evolving agent harness aimed at long-tail chart reasoning. This suggests a method that iteratively adapts to less frequent or more unusual chart formats, rather than relying solely on static training data.
The paper is available as arXiv:2610.28282v1. The abstract does not include experimental results or specific benchmarks, so the claims are limited to the proposed approach and its motivation.