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arxiv: 2604.26645 · v1 · submitted 2026-04-29 · 💻 cs.AI · cs.LG

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

Dianyu Liu , Chuan Qin , Xi Chen , Xiaohan Li , Wenxi Xu , Yuyang Wang , Xin Chen , Yuanchun Zhou
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Hengshu Zhu
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Pith reviewed 2026-05-07 10:48 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords AI-readiness evaluationscientific dataagentic systemmulti-agent workflowdata qualityAI compatibilityscientific adaptabilityevaluation framework
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The pith

SciHorizon-DataEVA is an agentic system that enables scalable AI-readiness evaluation of heterogeneous scientific data by defining Sci-TQA2 principles and executing them through a hierarchical multi-agent workflow.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets the absence of any scalable way to determine whether scientific datasets are suitable for machine learning models in AI-for-Science work. It introduces SciHorizon-DataEVA together with the Sci-TQA2 principles that divide readiness into four dimensions: governance trustworthiness, data quality, AI compatibility, and scientific adaptability. Each dimension is turned into atomic measurable elements. The system then applies a directed cyclic multi-agent process that profiles the data, selects appropriate metrics, builds custom evaluation plans, and runs assessments with verification and self-correction. Experiments on datasets from multiple domains support the claim that this produces consistent and general results.

Core claim

The central claim is that SciHorizon-DataEVA provides a scalable and principled mechanism for AI-readiness evaluation of heterogeneous scientific data. It organizes readiness via the Sci-TQA2 principles across four complementary dimensions and operationalizes them through Sci-TQA2-Eval, a hierarchical multi-agent workflow that dynamically constructs dataset-aware specifications using lightweight profiling, applicability-aware metric activation, and knowledge-augmented planning, then executes them with an adaptive tool-centric mechanism containing built-in verification and self-correction.

What carries the argument

Sci-TQA2 principles that organize AI-readiness into four dimensions (governance trustworthiness, data quality, AI compatibility, scientific adaptability) each decomposed into measurable atomic elements, carried out by the Sci-TQA2-Eval hierarchical multi-agent workflow with dynamic specification construction and self-correction.

If this is right

  • Enables fine-grained and executable assessment of scientific data readiness for AI use.
  • Supports scalable evaluation through dynamic construction of dataset-specific specifications.
  • Delivers generalizable results across multiple scientific domains as shown in the experiments.
  • Incorporates verification and self-correction to maintain assessment reliability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Data repositories could adopt the system to attach automated readiness scores that help researchers select suitable datasets for AI projects.
  • The same principle-decomposition and multi-agent pattern might apply to evaluating data readiness in non-scientific domains such as finance or healthcare.
  • Integration into data collection pipelines could flag weaknesses early and guide improvements before large AI training runs begin.

Load-bearing premise

The Sci-TQA2 principles can be decomposed into measurable atomic elements and the hierarchical multi-agent workflow will produce reliable, generalizable assessments across heterogeneous data without domain-specific failures or high error rates.

What would settle it

Running the system on a collection of scientific datasets whose AI-readiness has already been established by expert manual review and finding large discrepancies in the automated scores would disprove the reliability and generality of the assessments.

read the original abstract

AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypothesis generation workflows across domains. However, the effectiveness of these models is fundamentally constrained by the AI-readiness of scientific data, for which no scalable and systematic evaluation mechanism currently exists. In this work, we propose SciHorizon-DataEVA, a novel agentic system to scalable AI-readiness evaluation of heterogeneous scientific data. At the evaluation-criteria level, we introduce the Sci-TQA2 principles, which organize AI-readiness into four complementary dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability. Each dimension is decomposed into measurable atomic elements that enable fine-grained and executable assessment. To operationalize these principles at scale, we develop Sci-TQA2-Eval, a hierarchical multi-agent evaluation approach orchestrated through a directed, cyclic workflow. Our Sci-TQA2-Eval dynamically constructs dataset-aware evaluation specifications by combining lightweight dataset profiling, applicability-aware metric activation, and knowledge-augmented planning grounded in domain constraints and dataset-paper signals. These specifications are executed through an adaptive, tool-centric evaluation mechanism with built-in verification and self-correction, enabling scalable and reliable assessment across heterogeneous scientific data. Extensive experiments on scientific datasets spanning multiple domains demonstrate the effectiveness and generality of SciHorizon-DataEVA for principled AI-readiness evaluation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes SciHorizon-DataEVA, a novel agentic system for scalable AI-readiness evaluation of heterogeneous scientific data. It introduces the Sci-TQA2 principles organizing AI-readiness into four dimensions (Governance Trustworthiness, Data Quality, AI Compatibility, Scientific Adaptability), each decomposed into measurable atomic elements. These are operationalized via Sci-TQA2-Eval, a hierarchical multi-agent workflow using dynamic dataset profiling, applicability-aware metric activation, knowledge-augmented planning, and tool-centric execution with self-correction. The paper claims that extensive experiments across multiple scientific domains demonstrate the effectiveness and generality of this approach for principled evaluation.

Significance. If the experimental validation and reliability claims hold, the work would address an important gap in AI-for-Science by supplying a structured, automated framework to assess data suitability for ML models, potentially raising the success rate of AI applications in scientific discovery. The decomposition into complementary dimensions and the agentic workflow with built-in verification offer a more systematic alternative to ad-hoc checks, with potential for broad adoption if generality is confirmed.

major comments (2)
  1. Abstract: The central claim that 'extensive experiments on scientific datasets spanning multiple domains demonstrate the effectiveness and generality' provides no quantitative details on metrics, baselines, error rates, failure modes, inter-rater agreement with human experts, or cross-run consistency; this directly undermines the assertions of reliability and scalability for the multi-agent assessments.
  2. Sci-TQA2-Eval workflow (hierarchical multi-agent description): The assumption that the decomposition of Sci-TQA2 principles into atomic elements combined with dynamic specification construction and self-correction will yield reliable, generalizable scores lacks any reported stability metrics, ablation studies, or domain-specific validation, leaving the 'principled' and 'reliable' qualifiers unsupported given known variance in LLM-orchestrated agents.
minor comments (1)
  1. The abstract is information-dense; clearer separation of the four Sci-TQA2 dimensions and their atomic elements would improve readability and allow readers to assess executability without the full workflow details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for stronger quantitative support in the abstract and additional validation for the Sci-TQA2-Eval workflow. We agree that these elements will improve the manuscript and outline specific revisions below to address each major comment.

read point-by-point responses

  1. Referee: Abstract: The central claim that 'extensive experiments on scientific datasets spanning multiple domains demonstrate the effectiveness and generality' provides no quantitative details on metrics, baselines, error rates, failure modes, inter-rater agreement with human experts, or cross-run consistency; this directly undermines the assertions of reliability and scalability for the multi-agent assessments.

    Authors: We agree that the abstract lacks the quantitative details necessary to substantiate the claims of effectiveness and generality. In the revised manuscript, we will expand the abstract to include specific results from the experiments, such as average AI-readiness scores per dimension across domains, comparisons against baseline evaluation approaches, reported error rates and failure modes, inter-rater agreement metrics with human experts (e.g., Cohen's kappa values), and cross-run consistency measures (e.g., standard deviation across repeated evaluations). These additions will directly support the assertions of reliability and scalability. revision: yes

  2. Referee: Sci-TQA2-Eval workflow (hierarchical multi-agent description): The assumption that the decomposition of Sci-TQA2 principles into atomic elements combined with dynamic specification construction and self-correction will yield reliable, generalizable scores lacks any reported stability metrics, ablation studies, or domain-specific validation, leaving the 'principled' and 'reliable' qualifiers unsupported given known variance in LLM-orchestrated agents.

    Authors: We acknowledge that additional evidence is required to substantiate the reliability and generalizability of the hierarchical multi-agent workflow, particularly in light of potential variance in LLM-based systems. While the manuscript reports evaluations across multiple scientific domains, we will incorporate the following in the revision: ablation studies that isolate the contributions of dynamic specification construction, applicability-aware metric activation, knowledge-augmented planning, and self-correction; stability metrics including variance across multiple independent runs and inter-rater agreement with human experts; and domain-specific validation breakdowns showing performance consistency. These additions will provide empirical support for the 'principled' and 'reliable' aspects of the approach. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the proposed framework

full rationale

The paper introduces Sci-TQA2 principles and the SciHorizon-DataEVA agentic system as novel constructs, with dimensions decomposed into atomic elements and a hierarchical multi-agent workflow described from first principles. No load-bearing steps reduce by definition, fitted parameters, or self-citation chains to the inputs; the claims of effectiveness rest on external experimental validation across domains rather than internal equivalence. This is a standard proposal paper whose derivation chain is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no specific free parameters, axioms, or invented entities can be extracted or audited from the provided text.

pith-pipeline@v0.9.0 · 5578 in / 1148 out tokens · 30404 ms · 2026-05-07T10:48:34.031123+00:00 · methodology

discussion (0)

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Reference graph

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