Show HN: Memory System Hitting 80.1% Accuracy on LoCoMo (Built in 4.5 Months)

I’ve been working on an independent memory-retrieval architecture for agent systems. I don’t have a CS background — previously worked climbing cell towers and doing handyman jobs — but I spent the last 4.5 months building a hybrid memory system from scratch.

The system combines FAISS, BM25, and a symbolic ranking layer (MCA). Answers are generated with GPT-4o-mini at temperature 0. The focus is determinism, transparency, and reproducibility rather than model size.

On the official LoCoMo benchmark (1,540 questions), the system reaches 80.1% average accuracy. To my knowledge, that’s above the publicly reported results for existing agent-memory stacks using small models.

Latency is ~2.5 seconds, and cost is ~$0.10 per 1M tokens. Memory is fully isolated and local, which makes it usable for offline or enterprise applications.

Repository (code + full reproducible benchmarking): https://github.com/vac-architector/VAC-Memory-System

Happy to answer technical questions, discuss the architecture, or hear critiques.

Summary

The article describes a memory system architecture called VAC (Versatile Adaptive Cache) that aims to improve performance and efficiency in modern computer systems. VAC employs adaptive caching techniques and a hierarchical design to optimize memory access and reduce power consumption.

Story

Show HN: Memory System Hitting 80.1% Accuracy on LoCoMo (Built in 4.5 Months)