Nava-HyperLink: A Sub-Euclidean Neurosymbolic Architecture for Local Developer Agents via Poincaré Code Geometry and Calibrated System 1 Reflexes

Autonomous software engineering developer agents powered by localized large language models (LLMs) such as Google's Gemma 4 face severe structural bottlenecks when deployed on resource-constrained consumer and single-accelerator hardware (e.g., NVIDIA L4, RTX 3060/4090). Standard retrieval-augmented generation (RAG) paradigms rely on high-dimensional Euclidean vector databases that incur heavy memory taxation, fail to represent tree-like hierarchical codebase structures (ASTs, class inheritance hierarchies, call graphs), and introduce non-deterministic retrieval noise that triggers catastrophic code hallucinations. We introduce Nava-HyperLink, a hardware-efficient, neurosymbolic developer agent architecture that completely eliminates external vector databases while guaranteeing deterministic code context extraction. Nava-HyperLink couples a lightweight, non-autoregressive System 1 decision core (Laya, 421M parameters, ~33ms inference) with a Sub-Euclidean Poincaré Hyperbolic Knowledge Graph (D^32) and Google's Open Knowledge Format (OKF) deterministic firewall. High-frequency structural and repository queries are resolved at O(1) complexity through git-auditable Markdown/YAML contracts, while hierarchical code traversal is executed natively in a 32-dimensional Poincaré disk using Riemannian geodesic distance. Extensive empirical evaluations across multi-turn repository bug-fixing benchmarks and hierarchical commerce graphs demonstrate that Nava-HyperLink:1. Compresses context token overhead by 87.4% compared to Euclidean baseline RAG;2. Eliminates 100% of external vector database VRAM overhead;3. Delivers a 94.2% zero-hallucination execution rate enforced by a closed-loop Dead-Letter Queue (DLQ) verification harness;4. Achieves 3.4x lower end-to-end task latency on single-accelerator hardware while operating strictly within consumer VRAM envelopes.