Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
Quick Answer
This paper shows that A new framework identifies failures in reasoning language models like Gemma-4-31B-IT and Claude Sonnet 4.6, revealing that dominant failure modes vary by model and context.
Quick Take
Self-monitoring mechanisms significantly reduce non-compliance by up to 99%, enhancing instruction adherence in AI workflows.
Key Points
- Framework localizes failures to instruction identification, conflict resolution, and response realization.
- Evaluation on IHEval and IHChallenge shows varying failure modes across models and tasks.
- Self-monitoring mechanisms reduce non-compliance by 81-99% across multiple models.
- GPT-5.3 shows 86% reduction under static attacks and 45% under adaptive attacks.
- Findings highlight the need for improved instruction hierarchy adherence in AI systems.
Paper Resources
Source Excerpt
arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction. Existing benchmarks largely measure this behavior end-to-end, asking whether the final response is compliant.
However, a non-compliant response can arise from several distinct failures: the model may fail to identify the relevant instructions in context, fail to resolve conflicts among identified instructions, or correctly resolve the conflict in its reasoning while still producing a violating response. …
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