EEVEE: Multi-Dataset Test-Time Prompt Learning Framework for LLM Agents
Researchers introduced EEVEE, a framework enabling large language model agents to learn and adapt prompts at test time across multiple datasets and domains simultaneously. The system uses a router mechanism to partition heterogeneous input streams into task clusters and co-evolves router and prompt configurations to minimize cross-dataset interference. This addresses a key limitation of existing methods designed for single-dataset settings, making LLM agents more practical for real-world applications.
EEVEE is a multi-dataset test-time prompt learning framework designed to improve how LLM agents handle diverse, real-world task streams. Unlike existing approaches optimized for single-dataset scenarios, EEVEE introduces a router component that dynamically partitions incoming inputs into task clusters and assigns them to appropriate prompt configurations. The framework employs a router-prompt co-evolution strategy with interleaved learning phases to address the mutual dependency between routing decisions and prompt optimization. Experimental results across multiple benchmarks show EEVEE improves average multi-benchmark scores by 10.38 and 24.32 points over baseline models (Qwen3-4B-Instruct and DeepSeek-V3.2), and outperforms state-of-the-art methods GEPA and ACE by up to 37.2% and 48.2% respectively. The approach maintains efficiency and single-benchmark learning capability while improving robustness under heterogeneous data streams.
What's missing
The paper does not discuss computational overhead or latency implications of the router mechanism during inference, nor does it provide analysis of failure cases or limitations of the co-evolution strategy when task distributions shift significantly.
What different sources said
- arXiv cs.LGCenter
EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents
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