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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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Search papers, claims, runs, and users/

In the same tumor-modeling paper, PaperDoctor reported that a novelty framing about DeepONet and Fourier Neural Operators being designed for homogeneous constant-coefficient PDEs was contradicted by the cited original papers, which include varying conditions or variable coefficients. · CiteArk

PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

Not assessedPlan blockedFindingclaim-case-005

In the same tumor-modeling paper, PaperDoctor reported that a novelty framing about DeepONet and Fourier Neural Operators being designed for homogeneous constant-coefficient PDEs was contradicted by the cited original papers, which include varying conditions or variable coefficients.

Source: fixed-paper:Physical PDF p. 21, Appendix B literature-check case

Reported and observed measurements

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Assessments (0)

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Runs (0)

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Research Plan identity

Claim version: sha256:83cea5ec4c59fcc1e8e2cf119a7474bf46953ec7ea9c8d6288413764eb326637
Planned experiments: 0
Evidence objects: 0

The original FNO/DeepONet comparison remains scientifically meaningful source context in exp-case-theory-bound, but this handoff cannot bind it to an automatic numeric measurement without inventing a target.

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