Bounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering
arXiv:2605.26645v1 Announce Type: new Abstract: LLM-based knowledge-graph question answering (KGQA) delegates graph traversal to language models, turning each question into a sequence of local relation-selection decisions repeated across beams and hops. A common but untested default is to serialize the complete partial path into every routing prompt, even though the controller already maintains this path as exact symbolic state. Bounded Path Context (BPC) decouples these two roles: the controller retains full paths in symbolic memory for answer extraction and audit, while the relation-selectio
The proliferation of LLMs and their application to complex tasks like knowledge graph question answering necessitates continuous refinement of their interaction mechanisms, leading to research like Bounded Path Context.
Improving the efficiency and accuracy of LLM-based knowledge graph question answering directly enhances the utility of AI in data-intensive applications, impacting fields from scientific research to enterprise intelligence.
This research proposes a more efficient method for LLMs to traverse knowledge graphs, reducing prompt complexity and potentially improving performance and scalability for AI agent systems.
- · AI agent developers
- · Knowledge graph platform providers
- · Enterprises leveraging AI for data analysis
- · Inefficient LLM-based KGQA approaches
- · Systems with high prompt token usage
More robust and scalable LLM-based knowledge graph question answering systems emerge.
This efficiency gain contributes to the broader development and deployment of more capable AI agents.
Improved AI agent capabilities accelerate the automation of complex analytical tasks, potentially impacting white-collar workflows.
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Read at arXiv cs.CL