ControlLLM: Augment Language Models with Tools by Searching on Graphs
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Cited by 24 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods. The code is at https://github.com/OpenGVLab/ControlLLM.
Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Builds on 44 of its 55 references that are in the map. First posted on arXiv 26 Oct 2023.
Builds on
44 of 55 references.
References · 44
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Citing papers per year: 2023: 6; 2024: 25; 2025: 15; 2026: 13.
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