Patent Filing: Structural Scientific Validator Stack for Pre-Review Integrity Assessment

Description This record documents a Structural Scientific Validator Stack: a pre-review integrity system for scientific manuscripts that evaluates how a paper is constructed, rather than what it claims. The system addresses a growing failure mode in scientific publishing, where manuscripts generated or assisted by AI, paper mills, or automated tooling exhibit high linguistic fluency and formal correctness while remaining structurally incoherent. Traditional safeguards—peer review, plagiarism detection, citation counts, and post-publication retraction—operate too late in the pipeline and rely on semantic or reputational signals that increasingly fail under scale. The disclosed architecture defines a modular, model-agnostic validator stack composed of independent integrity gates. Each gate evaluates a distinct structural dimension of a manuscript, including: Reasoning continuity (argument flow and logical progression) Core inquiry persistence (alignment between research question and conclusions) Method–claim consistency (whether described methods can plausibly support stated claims) Epistemic trace integrity (whether citations function as evidence rather than authority decoration) All checks are non-semantic, deterministic, and non-corrective. The system does not assess truth, novelty, factual accuracy, or author intent, and it does not produce accept/reject decisions. Instead, it outputs structural diagnostics designed to support editorial triage, integrity auditing, and scalable pre-filtering workflows. The approach enables early detection of structurally unsound manuscripts—including AI-generated papers and paper-mill outputs—without banning AI usage or performing content moderation. By operating upstream of peer review, the system reduces reviewer load and mitigates downstream retraction risk. This publication is intended as defensive prior art and architectural disclosure. Implementation heuristics, thresholds, and scoring mechanisms are intentionally omitted to preserve adaptability and prevent misuse. Intended Use Editorial pre-review filtering Publisher integrity workflows Research integrity audits Large-scale manuscript triage Not Intended For Fact checking or truth adjudication Content moderation Automated acceptance or rejection Author attribution or intent inference ⚖️ Disclosure Notice This repository constitutes a public technical disclosure of an invention for the purpose of establishing prior art under applicable patent and intellectual property frameworks. The disclosure is intended to prevent subsequent patenting of substantially similar subject matter by other entities. This publication is timestamped and made permanently available via public archival platforms, including Zenodo, GitHub, and IPFS/Pinata, to preserve authorship attribution and document the structural origin of the disclosed invention. 📄 License This repository is published for disclosure and reference purposes only.No license to implement, reproduce, or commercialize the disclosed invention is granted by this publication.