arXiv:2606.02673v1 Announce Type: cross Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time. In this paper, we take a different view: the value of graphs for LLMs lie not only in supplying information, but also in organizing reasoning. Inspired by how humans use graph-structured mind maps to organize branching and converging thoughts, we ask whether graphs can serve as an internal form of reasoning assistance. We study this question on multi-hop question answering tasks, where
The continuous drive to enhance LLM reasoning capabilities is pushing research towards integrating more structured thought processes, evolving beyond simple retrieval augmentation.
Improving LLM's internal reasoning with graph scaffolds could lead to more robust, explainable, and less error-prone AI systems, accelerating agentic capabilities.
Traditional LLMs primarily rely on their pre-trained parameters for reasoning; this method proposes an internal, dynamic scaffolding that mirrors human-like structured thought, making LLMs more capable at complex tasks.
- · AI researchers
- · LLM developers
- · Enterprises deploying AI agents
- · Companies relying on brittle LLM applications
- · Unstructured data paradigms
LLMs demonstrate marked improvements in multi-hop question answering and other complex reasoning tasks.
The development of more sophisticated and reliable AI agents is accelerated as their underlying reasoning becomes more structured.
The boundaries between static knowledge bases and dynamic reasoning engines blur, enabling more adaptive and context-aware AI systems across various industries.
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Read at arXiv cs.LG