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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
Researchers at the National University of Singapore have developed a new framework for artificial intelligence agents called MRAgent. This framework differs from traditional approaches by allowing agents to dynamically process information, rather than relying on static retrieval and reasoning. The MRAgent framework uses a large number of tokens, specifically 118,000, per query to facilitate this dynamic processing. In contrast, another framework, LangMem, reportedly consumes a significantly larger amount of tokens, approximately 3.26 million, to achieve similar results.
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