Integrity
Manifesto.

Neural Review transcends traditional string-congruence detection by implementing a **Latent-Space Integrity Scanner (LSIS)**. Unlike legacy plagiarism systems, LSIS maps the conceptual vectors of a manuscript into a high-dimensional manifold, identifying **epistemological duplication** even when the linguistic representation has been fundamentally altered.

We detect "knowledge-reskinning"—the practice of computationally paraphrasing established empirical results to bypass conventional audit layers. Our system ensures that the scientific contribution represents a genuine **heuristic advance** rather than a stochastic rearrangement of existing data.

The primary failure of the modern peer-review cycle is the **Transparency Paradox**. While papers describe results, the underlying models and training data remain inaccessible. Neural Review mandates the use of a **Neural-Weight Registry (NWR)**.

Authors must provide a cryptographic trace of their experimentation phase. Our audit nodes execute **Adversarial Gradient Stressing (AGS)** against these traces to verify that the reported performance metrics are theoretically consistent with the claimed model architecture and training stoichiometry.

Final adjudication is not the result of a single AI, but a **Sovereign Consensus Quorum**. We deploy multiple, independently-trained audit nodes (Gemini Pro, Claude Opus, GPT-o1) to conduct parallel peer-scrutiny.

Only when a **Statistically Significant Consensus** (P < 0.0001) is reached across all nodes is the manuscript advanced to the Editorial Board for final archival blessing. This protocol eliminates the risk of field-politics or human reviewer fatigue.