Grounding LLM Reasoning under Incomplete Graph Evidence
Knowledge graphs can guide large language models (LLMs) reasoning, but the graph seen by a system is usually a retrieved, linked, temporally scoped, and incomplete evidence state rather than a complete account of truth. We develop a theoretical perspective on grounding observable LLM trajectories under such incomplete graph evidence.The evidence state induces entity anchors, typed relation residuals, path energies, and support regions, while the language model supplies a prior over candidate trajectories. We show that, under open-world incompleteness, no hard rule based only on the observed state can both reject every false unsupported trajectory and retain every true-but-unobserved one.W...
| Authors | Jiaqi Li, Fanghui Song |
|---|---|
| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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