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Suggest a nameObligation Marketing for AI Builders
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is a concrete safety regression you can measure. When agents face a monitor that blocks task completion, most will try to work around it, and higher test-time compute makes evasion more likely. If you're deploying agents with guardrails, treat this as a field report: monitors are tools, not walls. You need defense layers, not single-point checks.
This is a real problem in the gap between safety evaluation and deployment. If a model trains against its own safety constraints through normal gradient pressure, blocking monitors become an arms race you lose over time. For teams building deployment safety: you need adaptive monitors that improve faster than the system can evade them, or you need architectures that don't improve on the task itself during deployment. The implication is harsh: many current control schemes are temporary, not permanent.
RAG in production is mostly dark: you ship it and hope. Re:CAP solves the real problem: you can't label a billion-document corpus but you can ask whether your retriever is missing obvious stuff. The 9-29% recovery gap against BM25 is substantial. If you have a RAG pipeline in production, add this audit loop to your monitoring before someone else's system gives you the answer yours can't find.