Anthropic's global-workspace research describes J-space, a shared representation that can connect patterns across a model's layers. J-lens offers a way to inspect that space. The result is not a transcript of thought, but evidence about internal features that may never be visible in a response.
The EU AI Act's Article 13 is principally output-facing. Article 13 focuses on enabling deployers to interpret a high-risk system's output and use it appropriately. J-lens works at a different level: it can surface internal patterns that may not be visible in that output. That difference in scope does not alter the transparency requirement.
That creates a monitoring paradox. A team that does not look may remain unaware; a team that looks may identify a risk it can no longer ignore. Governance should not reward blindness. It should distinguish exploratory signals from confirmed findings, record proportionate assessment, and define who decides what remediation is warranted.
A safe harbour for good-faith detection would help. Subject to careful limits, organisations that probe models, document their methods and promptly address credible findings should not incur extra exposure merely because they sought to understand the system. Interpretability can then become a control, rather than a reason to keep the lens closed.