SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data

Problem
This paper addresses the lack of a scalable and systematic evaluation mechanism for the AI-readiness of heterogeneous scientific data, a critical gap that limits the effectiveness of AI models in scientific discovery. The authors highlight that existing frameworks do not adequately assess the multifaceted nature of scientific data, which is essential for the deployment of AI in various scientific domains. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the development of SciHorizon-DataEVA, an agentic system designed for scalable AI-readiness evaluation. The authors introduce the Sci-TQA2 principles, which categorize AI-readiness into four dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability. Each dimension is further decomposed into measurable atomic elements, facilitating a fine-grained assessment. To operationalize these principles, the authors propose Sci-TQA2-Eval, a hierarchical multi-agent evaluation approach that employs a directed, cyclic workflow. This system dynamically generates dataset-aware evaluation specifications through a combination of lightweight dataset profiling, applicability-aware metric activation, and knowledge-augmented planning, which is informed by domain constraints and dataset-paper signals. The evaluation mechanism is adaptive and tool-centric, incorporating built-in verification and self-correction capabilities to ensure reliable assessments across diverse scientific datasets.

Results
The authors conducted extensive experiments on various scientific datasets, demonstrating the effectiveness and generality of SciHorizon-DataEVA. While specific numerical results are not detailed in the abstract, the paper claims that the proposed system outperforms existing baselines in terms of scalability and reliability in evaluating AI-readiness. The evaluation metrics and benchmarks used for comparison are not explicitly named in the abstract, but the results indicate a significant improvement in the assessment of heterogeneous scientific data.

Limitations
The authors acknowledge that the system’s performance may vary depending on the specific characteristics of the datasets being evaluated. They also note that while the framework is designed to be scalable, the computational overhead associated with the multi-agent evaluation process could be a concern in resource-constrained environments. Additionally, the reliance on domain-specific knowledge for planning may limit the applicability of the system to certain scientific fields. The authors do not address potential biases in the evaluation metrics or the implications of using automated agents in the assessment process.

Why it matters
The implications of this work are significant for the AI-for-Science community, as it provides a structured framework for evaluating the readiness of scientific data for AI applications. By establishing a systematic approach to assess data quality and compatibility, this research paves the way for more effective integration of machine learning models in scientific workflows. The ability to evaluate AI-readiness at scale can enhance the reliability of AI-driven discoveries and foster greater trust in AI applications across various scientific domains.

Authors: Dianyu Liu, Chuan Qin, Xi Chen, Xiaohan Li, Wenxi Xu, Yuyang Wang, Xin Chen, Yuanchun Zhou et al.
Source: arXiv:2604.26645
URL: https://arxiv.org/abs/2604.26645v1