Safety claims require adversarial testing. Not cheerleading.
High stakes, limited review bandwidth
Safety claims matter. But the field moves faster than peer review capacity. Pre-submission critique is scarce.
Interpretability findings need validation
Mechanistic interpretability results can be fragile. Cross-architecture validation catches overfitting to specific models.
Methodology rigor is demanding
Reviewers at top venues scrutinize methodology. One weak assumption undermines the entire contribution.
Adversarial review from multiple architectures.
Multi-model critique
Claude and Gemini attack your claims from different architectural perspectives, producing an independent contradiction memo and claim-support audit. No single model's blind spots.
57% rejection rate
That's the feature, not the bug. Genuine adversarial pressure before reviewers apply it.
Contradiction surfacing
Cross-reference against 744 AI safety papers. Find where your claims conflict with existing literature.
What the pipeline produces.
Pre-submission adversarial review: You get the critique before the reviewers do. Claims that survive multi-model attack are stronger submissions. Claims that don't survive save you a rejection cycle.
Research audit for AI safety work.
Send one claim, draft section, or methodology question. We run it through cross-architecture adversarial review.