EEVEE: Multi-Dataset Test-Time Prompt Learning Framework for LLM Agents
Researchers have proposed EEVEE, a multi-dataset test-time prompt learning framework designed to help large language model (LLM) agents self-improve when exposed to heterogeneous, real-world task streams. Unlike existing methods limited to single-dataset settings, EEVEE uses a routing mechanism and a co-evolution strategy to assign inputs to appropriate prompt configurations across diverse domains. The work addresses a key gap between controlled benchmark performance and practical deployment, where models must handle varied and unpredictable input distributions.
EEVEE (introduced in a preprint submitted to arXiv on June 9, 2026) is presented as the first multi-dataset test-time prompt learning framework for LLM agents, targeting the challenge of adapting to heterogeneous input streams encountered in real-world deployments. Current test-time prompt learning methods are largely designed for single-dataset scenarios, which limits their applicability when models face inputs drawn from multiple domains and task distributions simultaneously. To address cross-dataset interference, EEVEE incorporates a router that clusters incoming inputs and assigns them to suitable prompt configurations, optimized through an interleaved 'router-prompt co-evolution' strategy that resolves the mutual dependency between routing and prompt learning. Experiments show the framework improves average multi-benchmark scores by 10.38 points over Qwen3-4B-Instruct and 24.32 points over DeepSeek-V3.2, while also surpassing state-of-the-art methods GEPA and ACE by up to 37.2% and 48.2%, respectively. The authors report that EEVEE maintains single-benchmark learning capability and efficiency alongside its multi-dataset gains. The work spans 19 pages and 6 figures, with a DOI pending registration via DataCite.
What's missing
As a preprint, EEVEE has not yet undergone peer review, so its reported performance gains should be treated as preliminary. The paper does not appear to discuss computational overhead introduced by the router component relative to baseline methods, nor does it address potential failure modes when the router misclassifies task clusters. Generalization to LLMs beyond Qwen3-4B-Instruct and DeepSeek-V3.2 is not evaluated. The datasets and domains used in experiments are not specified in the abstract, limiting assessment of how broadly 'heterogeneous' the test conditions truly are.
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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