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New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.
Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal.
AI Summary
Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal. To solve this, researchers at the National University of Singapore developed MRAgent , a framework that abandons the static "retrieve-then-reason" approach. Instead, it uses a mechanism that allows an agent to dy…
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