Original Reddit post

We are thrilled to announce HyperspaceDB v3.1.4 — introducing cutting-edge True Turbo 4-Bit Lloyd-Max Quantization , 1-Bit Asymmetric Distance Computation (ADC) delivering a 107× speedup with 99.9% Recall@10 , the official hyperspace-memory drop-in replacement for Mem0/Zep in Python and TypeScript, and built-in Multi-Step Agent Trajectory & Lyapunov Stability Tracking ! 🚀 🚀 Key Highlights in v3.1.4

  1. ⚡ True Turbo 4-Bit Lloyd-Max & 1-Bit ADC Quantization (107× Speedup, 99.9% Recall) True Turbo Spherical Quantization ( turbo ) : Implemented non-linear Lloyd-Max centroid codebooks ( [-2.401, …, 2.401] ) combined with random orthogonal rotation and exact $L_2$ norm preservation. Delivers 7.8× RAM compression with 96.4% Recall@10 across all metric spaces. 1-Bit Rotated extreme with Asymmetric Distance Computation (ADC) : Enhanced 1-bit binary quantization with vector norm scaling $|V|_2$ and full-precision query projection: Single-Pass : 62.8% Recall@10 at 107× raw search speedup over float32. Two-Pass Cascade Top100-to-Rerank : Achieves 99.9% Recall@10 while preserving a 15–20× net throughput boost . Universal Block Quantization ( medium_plus ) : Extended 4-bit block-wise quantization ($B=16$) to non-Euclidean geometries (Poincaré, Lorentz H 33, MRL Hybrid 801D), achieving 10.6× RAM savings with 93.6% Recall@10 .
  2. 🧠 hyperspace-memory: Drop-In Mem0 & Zep Replacement (Python & TS/JS) 100% Mem0 API Compatibility : Migrate existing AI agents by simply replacing from mem0 import Memory with from hyperspace_memory import Memory — no prompt changes or pipeline rewrites required. 100× Lower Latency (< 0.5 ms) : Backed by native in-RAM MRL 129D cascades and hyperbolic indexing instead of heavy relational table lookups. Zero Mandatory LLM Overhead : Direct vector + graph episodic memory operations without forcing expensive LLM calls on every memory insert. 98% Storage & RAM Reduction : Native integration with extreme 1-bit ADC and turbo 4-bit quantization modes.
  3. 🎯 Multi-Step Agent Trajectories & Lyapunov Stability Analysis ($\lambda$) Agent Run Tracking Endpoints : Added /api/admin/runs/start , /api/admin/runs/step , and /api/admin/runs/end for tracking multi-agent execution graphs, tool calls, and step-by-step reasoning vectors. Lyapunov Thought Stability Exponent ($\lambda$) : Automatically computes exponential divergence rates of thought trajectories on the Poincaré disk H 33 to detect agent hallucinations, reasoning loops, and cognitive drift in real time. Interactive 3D/2D Visualizer : Added interactive trajectory viewer on /trajectory in the Hyperspace Dashboard.
  4. 🛠️ Zero-Code Cognitive Memory MCP Server (mcp-hyperspace-memory) Dedicated Agent Memory Server : Lightweight Model Context Protocol (MCP) server exposing 8 dedicated memory tools ( memory_remember , memory_recall , memory_forget , memory_update , memory_list_sessions , memory_explore_hierarchy ). Zero Configuration : Simply run npx -y mcp-hyperspace-memory@latest in Cursor, Claude Desktop, Windsurf, or Antigravity to grant autonomous agents permanent, structured memory. Thank you to all contributors, researchers, and node operators building the universal spatial memory for autonomous AI agents! 🌌 submitted by /u/Sam_YARINK

Originally posted by u/Sam_YARINK on r/ArtificialInteligence