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AI agents write emails, summarise meetings, automate workflows, and provide investment advice. And yet, most of them share a fundamental problem: they forget.
Volatile memory, truncated context windows, barely any persistent understanding of a user's evolving intentions or environment. The result? Repetitions, hallucinations, and a loss of perspective on what truly matters.
This is precisely where temporal knowledge graphs come in. Unlike static knowledge graphs, they integrate time as a first-class dimension. They capture not only what happened, but also when – and how relationships evolve over time. Static knowledge becomes a living, evolving memory.
Michael Banf and Johannes Kuhn presented part of this research at Neo4j Nodes '25: "Building Evolving AI Agents Via Dynamic Memory Representations Using Temporal Knowledge Graphs." The talk demonstrated how temporal granularity in knowledge graphs enables applications ranging from personalised recommendations and industrial process monitoring to medical diagnosis assistants.
For us at Perelyn, this work is directly connected to the question of how AI systems become useful in the long run – not just at the moment of a query, but over weeks and months.
The full recording of the talk is available on YouTube.
Event
Liliya Imasheva presented a validation pipeline for evaluating AI summaries at Conf42 Large Language Models 2026.
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At the IOCMA 2026 conference, Perelyn presented our method, which transforms graph data such as supply chains or networks so that AI models can learn more reliably from it, even over long distances.
