Authors - Ajinkya Chavan Abstract - The pursuit of intelligent systems capable of parsing human intent and navigating complex information landscapes has evolved from rigid, rule-based architectures to sophisticated, agentic frameworks. Early prototypes, such as the "Artificially Talented Architecture" (ATA), demonstrated the foundational utility of theme detection coupled with rudimentary holographic interfaces; however, these systems were constrained by the independence assumptions of Vector Space Models (VSM), limited context windows, and a lack of semantic relationship modeling. In the current era of Generative AI, while Large Language Models (LLMs) have solved fluency, they continue to struggle with "Global Sensemaking"—the ability to synthesize highlevel themes across vast corpora without succumbing to hallucination or context fragmentation. This paper introduces Holo-Agentic GraphRAG, a novel architecture that integrates Agentic Retrieval-Augmented Generation (Agentic RAG) with spatial computing to redefine state-ofthe- art theme detection. Unlike traditional methods relying on flat retrieval, the proposed approach employs a hierarchical knowledge graph constructed via LLM extraction and refined through the Leiden community detection algorithm. This structure allows for dynamic graph traversal and multi-level summarization. Furthermore, user interaction is formalized as a Partially Observable Markov Decision Process (POMDP) within a mixed-reality environment, fusing gaze tracking and voice prosody to resolve communicative ambiguity. Experimental results on the GraphRAG-Bench and a proprietary spatial interaction dataset demonstrate that Holo- Agentic GraphRAG outperforms standard RAG and static GraphRAG baselines by 18.4% in multi-hop reasoning accuracy and 22% in theme detection coherence, while significantly reducing token overhead.
