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AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
arXiv – CS AI|Yujie Zhao, Boqin Yuan, Junbo Huang, Haocheng Yuan, Zhongming Yu, Haozhou Xu, Lanxiang Hu, Abhilash Shankarampeta, Zimeng Huang, Wentao Ni, Yuandong Tian, Jishen Zhao||7 views
🤖AI Summary
Researchers introduce AMA-Bench, a new benchmark for evaluating long-horizon memory in AI agents deployed in real-world applications. The study reveals existing memory systems underperform due to lack of causality and objective information, while their proposed AMA-Agent system achieves 57.22% accuracy, surpassing baselines by 11.16%.
Key Takeaways
- →AMA-Bench addresses the gap between practical AI agent applications and current evaluation standards for agent memory.
- →Current memory systems fail primarily due to lack of causality, objective information, and lossy similarity-based retrieval.
- →The benchmark includes both real-world agentic trajectories and synthetic trajectories that scale to arbitrary horizons.
- →AMA-Agent introduces causality graphs and tool-augmented retrieval to improve memory system performance.
- →The new system demonstrates significant improvement over existing memory system baselines in autonomous agent applications.
Read Original →via arXiv – CS AI
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