Risky Business: Measuring The Faithfulness-Safety Tension
Chain-of-Thought (CoT) reasoning offers a promising window into model monitoring. However, monitoring relies on faithfulness, i.e., the model output strictly derives from its reasoning trace. We identify an alignment tension where a model must be faithful enough to be monitored, yet robust enough to reject unsafe reasoning. We demonstrate that this counterbalance exists in current Large Reasoning Models (LRMs), and show ways in which it can be addressed. We introduce HazMart, a human-written dataset set in an autonomous AI shopkeeper scenario. Unlike prior work that relies on providing hints in prompts to test faithfulness (e.g., "A Stanford professor said it should be Answer A"), we prop...
| Authors | Dominik Meier, Luca Joshua Francis, Marco Bernhard Kaiser et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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