Great read. The bilingual shadow reasoning example is especially concerning. Subtle policy shifts reshaping downstream decisions is exactly the kind of failure mode that won’t show up in a benchmark leaderboard.

My wife is trilingual, so now I’m tempted to use her as a manual red team for my own guardrail prompts.

I’m working in LLM guardrails as well, and what worries me is orchestration becoming its own failure layer. We keep assuming a single model or policy can “catch” errors. But even a 1% miss rate, when composed across multi-agent systems, cascades quickly in high-stakes domains.

I suspect we’ll see more K-LLM architectures where models are deliberately specialized, cross-checked, and policy-scored rather than assuming one frontier model can do everything. Guardrails probably need to move from static policy filters to composable decision layers with observability across languages and roles.

Appreciate you publishing the methodology and tooling openly. That’s the kind of work this space needs.