arXiv:2606.11182v1 Announce Type: new Abstract: 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 ass
The proliferation of Large Language Models (LLMs) and the increasing demand for real-world autonomous applications necessitate more robust and adaptive learning frameworks.
Test-time prompt learning, especially for multi-dataset scenarios, is crucial for developing truly general-purpose and self-improving AI agents capable of handling complex, heterogeneous real-world tasks.
This research introduces a method for AI agents to adapt their prompts dynamically in real-world settings, reducing reliance on pre-trained, static prompts and enabling better performance across diverse, continuously streaming data.
- · AI agents developers
- · Robotics
- · Generative AI platforms
- · Enterprise automation
- · Companies with static, single-task AI solutions
Improved performance and broader applicability of AI agents in dynamic real-world environments.
Accelerated adoption of AI agents across various industries as their reliability and adaptability increase.
The emergence of more sophisticated, self-managing AI ecosystems requiring less human intervention for continuous optimization.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG