Many statistical facts are conveyed through charts. While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools. Recently, the advent of image-generating Large Language Models has facilitated chart generation; however, even this process often requires users to provide numerous constraints for accurate results. In this paper, we propose a loop-based framework for automatically evolving charts in a multi-agent environment. Within this framework, three distinct agents—Chart Code Generator, Chart Replier, and Chart Quality Evaluator—collaborate for iterative, user-tailored chart generation using large language models. Our approach demonstrates an improvement of up to 29.97% in performance compared to first generation, while also reducing generation time by up to 86.9% compared to manual prompt-based methods, showcasing the effectiveness of this multi-agent collaboration in enhancing the quality and efficiency of chart generation.

Recent advances in large language models (LLMs) have significantly improved mathematical problem-solving. Among various techniques, paraphrasing problem statements has emerged as a promising strategy to enhance model understanding and accuracy.We define twelve paraphrasing types grounded in mathematics education theory and analyze their impact on LLM performance across different configurations. To automate selection, we propose a Paraphrase Type Selector that predicts effective paraphrases for each problem.Experiments on MATH-500, SVAMP, and AIME shows consistent performance gain from paraphrased problems. On MATH-500 with LLaMA 3.1-8B, combining the original with the best five paraphrased problems improves accuracy by +8.4%, with the selector achieving an additional +1.33% gain.

Recent advancements in large language models have heavily relied on the large reward model from reinforcement learning from human feedback for fine-tuning. However, the use of a single reward model across various domains may not always be optimal, often requiring retraining from scratch when new domain data is introduced. To address these challenges, we explore the utilization of small language models operating in a domain-specific manner based on router mechanisms. Our three approaches are: 1) utilize mixture of experts to form a single reward model by modularizing an internal router and experts, 2) employing external router to select the appropriate reward model from multiple domain-specific models, and 3) the framework reduces parameter size by loading reward models and router adapters onto a single small language model using adapters. Experimental validation underscores the effectiveness of our approach, demonstrating performance comparable to baseline methods while also reducing the total parameter size.