MemGPT

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1. Oct 20
   WorldMemArena benchmark tests multimodal agent memory through action–world loops

   Researchers from UC Santa Barbara, Stanford, ETH Zurich, Johns Hopkins, Carnegie Mellon and J.P. Morgan Chase have jointly proposed WorldMemArena, a multimodal agent memory benchmark, according to a 机器之心 report. The paper, whose co-first authors Chengzhi Liu and Yuzhe Yang are both at UCSB, was accepted at NeurIPS 2026. WorldMemArena evaluates memory inside an observe–act–environment-feedback loop and splits long-term memory into writing, maintenance, retrieval and use stages, with more than 400 multi-session tasks, 24,258 QA pairs and 15,595 images or screenshots. In the paper's results, M2A reached 86.83% memory recall but only 50.14% final QA accuracy, while MemGPT scored 85.20% and 57.81%, and systems tended to append new memories rather than revise outdated ones and did not reliably use visual information in cross-modal reasoning.

   机器之心 · 🔥 0

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