HyperspaceDB v3.1.4 introduces advanced quantization techniques for significant speed improvements, a drop-in replacement for Mem0 and Zep with lower latency, and built-in tracking for agent trajectories and stability analysis.
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 H33, 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 H33 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! 🌌
SuperLocalMemory V3.3 introduces a unified memory and learning system for AI agents with biologically-inspired forgetting, multi-channel retrieval, and P2P mesh coordination. The system achieves 74.8% on LoCoMo benchmarks and features triple-stream learning, lifecycle management, and EU AI Act compliance.
MemTrace is a new tool that makes LLM memory systems debuggable by tracing memory operations across multiple turns, addressing the black-box nature of current memory-augmented agents.
DimMem introduces a dimensional memory framework for LLM agents that represents memories as atomic, typed units with explicit fields, achieving state-of-the-art accuracy on LoCoMo-10 and LongMemEval-S while reducing token costs by 24%.
This paper introduces memory-augmented speculative execution for LLM agents, using three online memory systems to improve prediction accuracy by 19-39% on action prediction and up to 2.5x on observation prediction, all while being lossless with zero added wall-clock cost.