AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications 事件

AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications arXiv:2602.22769v3 Announce Type: replace Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical for achieving strong performance. However, a significant gap exists between applications and evaluation standards for agent memory: existing benchmarks primarily focus on dialogue-centric settings. In reality, agent memory consists