做 LLM 智能体部署的团队终于有了处理真实异构数据流的方案——EEVEE 的提示学习框架能自动适应多任务流，建议做 Agent 落地的开发者关注。
EEVEE 是首个支持多数据集测试时提示学习的框架，解决了 LLM 智能体在真实任务流中处理异构输入（来自多个数据集、领域和任务分布）的难题。现有方法多针对单数据集设计，无法应对跨数据集干扰。EEVEE 通过引入路由器将输入划分为任务簇并分配合适提示配置，采用路由器与提示协同进化策略优化。实验显示，在 Qwen3-4B-Instruct 和 DeepSeek-V3.2 上，多基准平均分分别提升 10.38 和 24.32 分，超越 SOTA 方法 GEPA 和 ACE 最高达 37.2% 和 48.2%。
EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents
In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under real-world task streams. Existing methods are largely designed for single-dataset settings, while real-world applications require models to handle heterogeneous input streams drawn from multiple datasets, domains, and task distributions, limiting their practical applicability. To mitigate cross-dataset interference, EEVEE introduces a router that partitions incoming inputs into task clusters and assigns them to suitable prompt configurations. This design is optimized via a router-prompt co-evolution strategy, which employs interleaved router and prompt learning phases to address their mutual dependency. Experiments across multiple datasets demonstrate that the framework improves robustness under heterogeneous data streams while maintaining single-benchmark learning capability and efficiency. Specifically, EEVEE improves average multi-benchmark scores by 10.38 and 24.32 points over Qwen3-4B-Instruct and DeepSeek-V3.2, surpassing SOTA methods GEPA and ACE by up to 37.2% and 48.2%.