This paper presents a comprehensive architectural breakdown and longitudinal benchmark study of HiveRAG, a novel Retrieval-Augmented Generation (RAG) system built on nature-inspired heuristics. Rather than relying on traditional greedy vector search or rigid hierarchical trees, HiveRAG operates as a bounded retrieval organism designed to explore semantic neighborhoods efficiently while avoiding cross-topic hallucinations. The architecture introduces four core biological and spatial metaphors: Spherical Topic Bounding: Acts as a semantic habitat boundary, strictly gating retrieval to relevant concept clusters to eliminate cross-topic bleeding. Honeycomb/Hex Neighborhoods: Utilizes 2D spatial hash grids for localized, even neighborhood expansion. Spiderweb Graph Diffusion: Recovers missing cross-modal and explicit document links via damped signal propagation. Pheromone Adaptation & Energy Budgets: Implements Hebbian edge reinforcement and strict search-cost boundaries to prevent unbounded graph wandering. Key Empirical Findings (Versions 0.2 – 0.4 & NativeBench): Through progressive benchmarking across 96-case stress tests and multi-modal prototype tracks (evaluated locally via Ollama qwen2.5:7b using official RAGAS metrics), the study demonstrates that HiveRAG trades raw retrieval micro-latency for vastly superior context precision and source recovery. In the controlled v0.4 showdown against a standard vector baseline, HiveRAG achieved: Perfect Source Recovery: 1.000 Hit@K and 1.000 MRR (vs. Vector's 0.875 and 0.595, respectively). High Context Precision: 0.9409 Context Precision Proxy (vs. Vector's 0.5626). Wall-Clock Efficiency: Despite being ~25x slower at the individual retrieval step, HiveRAG completed the full end-to-end evaluation pipeline slightly earlier than the vector baseline, owing to reduced downstream evaluator friction caused by cleaner context windows. This research establishes HiveRAG as a highly promising precision-first retrieval strategy for local-first and heavily constrained intelligence engines.
Shivansh Mukhia (Thu,) studied this question.