Large Language Models (LLMs) like GPT-4 have shown remarkable abilities to generate text, including summarizing documents. But when it comes to complex analysis, LLMs sometimes miss the mark, struggling to connect the dots and draw meaningful insights. Researchers are exploring an interesting new approach: using visual workspaces to guide LLM summarization. Imagine a detective using a whiteboard to map out a complex case – connections between suspects, timelines of events, key pieces of evidence. This visual representation helps organize thoughts and identify crucial links. The same principle applies to LLMs. By creating a visual workspace before feeding information to the LLM, we can provide a structured, organized input. Researchers tested this by simulating a visual workspace based on a dataset about a fictional terrorist plot. They extracted information from the workspace, like highlighted text, clustered documents, and annotations, and included this in the prompt to the LLM. The results were impressive. The LLM-generated summaries were significantly more accurate and aligned with the 'ground truth' of the scenario compared to summaries generated without a visual workspace. This 'space-steered summarization' has several potential benefits. It helps LLMs to 'remember' key facts and focus on important details, and it enhances their ability to follow complex reasoning paths. Beyond crime analysis, this approach could be incredibly useful for other tasks, like literature reviews. By organizing research papers visually, highlighting key findings, and clustering related works, we can guide the LLM to produce more comprehensive and insightful summaries. This research points towards a future where humans and AI collaborate more effectively. By leveraging our strengths in visual thinking and organization, we can empower LLMs to unlock deeper levels of understanding, making sense of complex information more efficiently and accurately.