LLM-KG Collaborative Reasoning with Knowledge Probes

Large Language Models (LLMs) excel at reasoning but suffer from static knowledge and hallucinations in fact-intensive tasks. Knowledge Graphs (KGs) offer trustworthy evidence, yet existing KG-centric agents remain brittle: relevant evidence may be structurally unavailable due to missing entities or relations, or computationally unavailable when buried in dense neighborhoods that are too costly to traverse exhaustively. We propose a KG-first collaborative reasoning framework that improves access to existing KG evidence in dense neighborhoods while recovering from missing KG links without relinquishing factual control to the LLM. We introduce the Guidance Graph of Thought (GGoT) as the framework's state interface, exposing unresolved reasoning slots as probeable states, and build on this interface with a conditional KG-first protocol. This protocol preserves reasoning continuity when required KG links are absent, reduces unnecessary exploration when relevant facts exist in large neighborhoods, and admits LLM-generated knowledge only under strict factual control. Experiments on Freebase- and Wikidata-based QA benchmarks show consistent improvements over strong KG-LLM reasoning baselines across both large and smaller LLMs. Efficiency and ablation analyses further confirm that the framework reduces dense-neighborhood exploration while making controlled recovery from KG dead ends beneficial rather than permissive.