GraphDancer: Training LLMs to Explore and Reason over Graphs via Two-Stage Curriculum Post-Training
arXiv:2602.02518v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many real-world knowledge sources are organized as heterogeneous graphs rather than plain text. Reasoning over such graphs requires models to follow schema-defined relations through precise function calls and to aggregate evidence across multiple rounds of interaction. We propose GraphDancer, a two-stage post-training framework that teaches LLMs to reason over graphs by interleaving natural-language reasoning with graph function execution. The firs
The increasing reliance of LLMs on external knowledge necessitates more sophisticated methods for integrating and reasoning over complex data structures like graphs, especially as model capabilities expand.
This development addresses a critical limitation of LLMs in handling structured, real-world knowledge, moving beyond plain text to enable more accurate and context-aware AI applications.
LLMs can now be systematically trained to leverage heterogeneous graph data through precise function calls, enabling more robust reasoning and interaction with complex information.
- · AI developers
- · Data science platforms
- · Knowledge graph providers
- · Enterprises with complex data
- · LLM applications restricted to unstructured text
- · Manual data integration workflows
More accurate and reliable LLM-powered applications leveraging structured data.
Increased demand for tools and expertise in building and maintaining knowledge graphs for AI consumption.
The development of truly autonomous AI agents capable of navigating and synthesizing vast, interconnected information landscapes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG