Our memory engine scored record 79% EM at HotPotQA benchmark
Analog AI's Memory Engine has achieved a remarkable milestone: it scored a record 79.2% Exact Match (EM) on the HotpotQA benchmark, alongside an impressive 85.5% F1 score. This performance, combined with 91% precision in LLM evaluations, demonstrates near human-level comprehension in multi-hop question answering and reasoning tasks. At its core, the Analog AI Memory Engine works by connecting entities and relationships through a structured graph network. Nodes represent key entities (people, concepts, events, etc.), while edges capture the relationships between them. This graph-based approach under the hood enables rich, interconnected knowledge representation that goes far beyond flat vector embeddings or traditional retrieval systems. One of the standout advantages is speed: the engine is 5-6x faster at "remembering" and retrieving information compared to similar projects. This efficiency makes it highly feasible for real-life interactions, especially where living, agentic memory is essential—allowing autonomous agents to maintain persistent, adaptive knowledge over long sessions without slowdowns. The primary use case is powering autonomous agents. The memory output can seamlessly integrate with existing long conversation contexts from LLMs. By offloading persistent knowledge to this external graph-based memory, you avoid LLM context overflow entirely—no more token limits forcing truncation or loss of critical history. Overall, Analog AI's analog memory approach addresses three fundamental problems plaguing current LLM and RAG systems: 1. Self-learning — Unlike static models that require retraining, this memory enables continuous adaptation and learning from new interactions in a living, evolving way. 2. Black box (explainability) — The graph structure provides full traceability: you can inspect nodes, edges, and reasoning paths to see exactly how conclusions were reached. 3. Overconfident responses (hallucinations) — When information isn't sufficiently connected or present in the graph, the system can confidently output "I don't know" instead of fabricating answers. There are many more advanced capabilities that haven't even been fully tested by HotpotQA yet, including deductions across the graph, hypothetical (if-else) reasoning, spatiotemporal reasoning (understanding time and location dependencies), authority handling (weighting inputs differently from various team members or sources), and more sophisticated common-sense inference. The next steps involve pushing the engine against even harder benchmark tests to further validate and improve these emerging strengths. Best of all, setup is incredibly straightforward. You can deploy the memory directly on the cloud, test it in real time, and link it to your AI agents via a simple API with just a few lines of code. No need to configure databases, set up LLMs, or handle complex infrastructure—everything is pre-configured and ready to go. If you're building agentic systems that need reliable, explainable, and fast-evolving memory, Analog AI's engine offers a powerful leap forward. Check it out at analogai.net to get started! Just switch to HTML view in the Blogger post editor, paste the text above directly, and switch back to Compose view to preview. The bold parts will display correctly.
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