arXiv:2602.22769v3 Announce Type: replace-cross 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 of a continuous stream of agent-environment interactions that are primarily composed of machine-generated representations. To bridge this gap, we introduce AMA-Be
The rapid advancement and deployment of Large Language Models (LLMs) into autonomous agentic roles highlight the immediate need for robust evaluation frameworks, especially concerning long-horizon memory.
Improving agent memory evaluation directly impacts the performance, reliability, and trustworthiness of AI agents, which are increasingly adopted in complex applications across various industries.
The introduction of AMA-Bench shifts the focus of AI agent memory evaluation from dialogue-centric environments to continuous agent-environment interactions, reflecting real-world agent deployments.
- · AI agent developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · Benchmark providers
- · Developers relying on outdated evaluation methods
- · Early-stage AI agent companies with limited memory capabilities
More effective and reliable AI agents will emerge due to better evaluation of their long-term memory capabilities.
Increased adoption of AI agents across white-collar workflows, leading to automation of more complex tasks.
Enhanced AI agent memory could enable agents to manage projects and learn over extended periods, blurring the lines between human and AI supervision.
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Read at arXiv cs.LG