Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
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 co
The proliferation of reasoning language models in agentic workflows necessitates a deeper understanding of their failure modes, particularly concerning instruction hierarchies, to improve reliability and safety.
This research provides critical insights into diagnosing and repairing failures in AI agents, which are becoming central to automating complex tasks and workflows.
The focus shifts from end-to-end compliance to a granular understanding of where and why AI agents fail in following instruction hierarchies, enabling more targeted development and debugging.
- · AI developers
- · AI safety researchers
- · Organizations deploying AI agents
- · AI agent platforms
- · AI systems with poor instruction adherence
- · Organizations relying on simple end-to-end AI testing
Improved debugging and reliability of AI agents, leading to more robust autonomous systems.
Faster and more efficient development cycles for complex AI agentic applications.
Increased trust and broader adoption of AI agents in critical industries due to enhanced predictability and control.
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Read at arXiv cs.AI