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

ArXi:2602.22769v3 Announce Type: replace 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 of a continuous stream of agent-environment interactions that are primarily composed of machine-generated representations. To bridge this gap, we