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EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents
Abstract
EEVEE is a novel test-time prompt learning framework for LLM agents that handles heterogeneous data streams through task clustering and co-evolving router-prompt optimization.
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%.
Community
EEVEE studies test-time prompt learning for LLM agents in more realistic settings, where tasks arrive as heterogeneous streams from multiple datasets and domains.
Instead of optimizing a single prompt for a fixed benchmark, EEVEE introduces a router-prompt co-evolution framework that clusters incoming tasks and assigns them to suitable prompt configurations. This helps reduce cross-dataset interference while preserving test-time adaptation ability.
The paper reports strong gains across multiple benchmarks, making it a useful step toward self-improving agents that can adapt continuously in the real world.
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Cite arxiv.org/abs/2606.11182 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.11182 in a dataset README.md to link it from this page.
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