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 st
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- LinkedLinked via unknownKnowledge Distillation of Black-Box Large Language Models →
- LinkedLinked via unknownKnowledge Distillation of Black-Box Large Language Models (2024) →
- LinkedLinked via unknownRetrace-1.5B →
- LinkedLinked via unknownNew benchmark exposes reasoning gaps in top models →
- LinkedLinked via unknownNorthwind AI →
- PossiblePossibly related (embedding) · 49%benjaminzwhite/reasoning-models →
- PossiblePossibly related (embedding) · 54%douglasjordan2/c0 →
- PossiblePossibly related (embedding) · 53%Jeryi-Sun/LLM-and-Law →
