EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents

Researchers introduce EEVEE, a novel framework designed to enable test-time prompt learning for LLM agents operating within real-world, multi-dataset task streams, addressing the limitations of current single-dataset optimization methods.

Overcoming the Limitations of Single-Dataset Prompting

Current methodologies for prompt optimization in Large Language Model (LLM) agents are predominantly designed for single-dataset environments. While effective in isolated settings, these approaches often fail when deployed in real-world applications. In practical scenarios, agents must process heterogeneous input streams characterized by diverse domains, varying task distributions, and multiple datasets. This diversity typically leads to cross-dataset interference, where optimizations for one task distribution negatively impact performance on another, thereby limiting the scalability and practical applicability of self-improving agents.

Introducing EEVEE: A Multi-Dataset Framework

To bridge the gap between theoretical prompt learning and real-world deployment, the authors propose EEVEE. This framework represents the first multi-dataset test-time prompt learning system specifically engineered for LLM agents. EEVEE allows agents to adapt their prompting strategies dynamically during the test phase, ensuring that the model can evolve and improve its performance as it encounters new and varied task streams.

Mitigating Cross-Dataset Interference

A core innovation of the EEVEE framework is the introduction of a specialized router. This component is designed to mitigate the interference that occurs when an agent is exposed to heterogeneous data. By routing inputs effectively, EEVEE ensures that the prompt learning process remains stable and specialized across different task distributions, preventing the "catastrophic interference" often seen when a single prompt is forced to generalize across conflicting domain requirements.