# Matching Game Preferences Through Dialogical Large Language Models: A Perspective
## 3 Dialogical Large Language Models (D-LLMs): A Novel Framework for GRAPHYP-LLM Integration
### Theoretical Framework of D-LLM
#### 3.1.3 Core Coupling Principles  
The following four principles guide the D-LLM framework design. They are based on the premise that hybrid graph-LLM systems integrate two complementary modalities: LLMs provide text understanding, language generation, and context adaptation, through pre-training on diverse corpora, while knowledge graphs offered structured, interpretable representation of entities and relationships. In such systems, \"cognitive communities\" refer to clusters or networks within the graph structure that capture semantic, social, and relational connections among users, items, and contextual factors. GRAPHY's cognitive communities dynamically aggregate and update user preferences across interactions, representing factors such as past behavior, expressed interests, and documentary choices in a modulated fashion. Integration with LLM reasoning capabilities enables conversational AI to generate responses that are contextually rich and anchored in an explicit, continuously updated user profile model [11]. The four coupling principles are as follows:  
Practice 1: Interactive Reasoning Loops. D-LLM implements sustained dialogical interaction through iterative reasoning cycles where the LLM queries GRAPHY for specific nodes, paths, or subgraphs, interprets the results, and refines subsequent queries based on previous answers. GRAPHY's response makes it possible to differentiate between strategies that led to the termination of exploration (failure or success), further investigation, or expansion of the search, according to different cognitive processes. This enables complex, multi-step reasoning tasks such as tracing relationships across several hops or synthesizing information from disparate parts of the knowledge graph. Advanced frameworks (e.g., Tree-of-Traversals [35], GraphOTTER [36]) empower the LLM to select discrete graph actions at each reasoning step. Practice 2: Dynamic Context Management  
In multi-turn conversations, the system maintains rich context about previous queries, answers, and reasoning paths. This contextual awareness enables follow-up questions, clarifications, and deeper exploration of the knowledge graph while preserving conversational coherence. Practice 3: Transparent Reasoning Pathways  
Unlike black-box AI systems, D-LLM constructs explicit, interpretable reasoning traces. The LLM selects discrete graph actions (such as _VisitNode_, _GetSharedNeighbours_, or _AnswerQuestion_) at each reasoning step, creating clear audit trails essential for transparency and explainability. Practice 4: Grounded Inference  
By anchoring each reasoning step in the actual graph structure, D-LLM reduces hallucinations and ensures factual accuracy. This grounding is particularly crucial for multi-hop queries and knowledge-intensive tasks where precision is paramount. 3.1.4. Dialogical vs. Traditional Approaches: D-LLM Perspective  
Traditional AI systems typically operate through isolated query-response cycles with limited context retention. D-LLM's dialogical approach enables the following:  
Conversational Memory: The system builds comprehensive models of ongoing dialogues, tracking not just facts exchanged but reasoning patterns, user preferences, and evolving understanding. Collaborative Discovery: Rather than simply retrieving pre-existing knowledge, D-LLM engages in collaborative knowledge construction, helping users explore ideas, test hypotheses, and develop insights through sustained interaction. Adaptive Expertise: The system adjusts its level of explanation, terminology, and reasoning depth based on demonstrated user expertise and feedback, creating truly personalized learning experiences. Figure 1 sums up the characteristics that we propose for the D-LLM:  
A prospective example of D-LLM, in Appendix A, illustrates potential dialogical interactions between GRAPHYP and an LLM, while the GRAPHYP perspective on how to resolve scientific disputes with LLMs is illustrated in Appendix B. 3.1.5.