Chart understanding plays a pivotal role when applying Multimodal Large Language Models
(MLLMs) to real-world tasks such as analyzing scientific papers or financial reports …

Contemporary evaluation techniques are inadequate for agentic systems. These
approaches either focus exclusively on final outcomes--ignoring the step-by-step nature of …

Given that Large Language Models (LLMs) have made significant progress in writing code,
can they now be used to autonomously reproduce results from research repositories? Such …

The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-
world coding tasks, such as data analysis and image editing. However, existing benchmarks …

We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free
multi-agent data analysis system that leverages the power of large models. LAMBDA is …

We introduce Agent K v1. 0, an end-to-end autonomous data science agent designed to
automate, optimise, and generalise across diverse data science tasks. Fully automated …

This paper explores the current landscape of visualizing large language models (LLMs). The
main objective was threefold. Firstly, we investigate how we can visualize LLM-specific …

The rapid evolution of large language models (LLMs) and their capacity to simulate human
cognition and behavior has given rise to LLM-based frameworks and tools that are …

We introduce the task of text-to-diagram generation, which focuses on creating structured
visual representations directly from textual descriptions. Existing approaches in text-to …

We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded
code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes …