Stress-Testing Model Specs Reveals Character Differences among Language Models
Researchers stress-tested the ethical guidelines of twelve major AI models by forcing them to choose between conflicting principles, revealing over 70,000 cases of behavioral disagreement that expose fundamental contradictions and ambiguities in how these systems are designed to make decisions.
Details
Author(s)
Jifan Zhang, Henry Sleight, Andi Peng, John Schulman, Esin Durmus
Date
October 23, 2025
Summary
Researchers stress-tested the model specifications of twelve major AI systems by creating scenarios that force explicit tradeoffs between conflicting principles, revealing fundamental flaws in how these systems are designed to behave. By generating over 300,000 value tradeoff scenarios and analyzing responses from models including Claude, GPT, Gemini, and Grok, they identified more than 70,000 cases of significant behavioral disagreement.
High disagreement scenarios proved to be a strong predictor of specification problems, with these cases showing 5-13 times higher rates of all models simultaneously violating their own specifications.
The study pinpointed specific issues including direct contradictions between principles, vague guidelines that different models interpret completely differently, and insufficient detail to distinguish good responses from bad ones. Beyond specification flaws, the research uncovered practical problems including false refusals where models block legitimate requests and genuine misalignment cases where models behave inappropriately, demonstrating that current AI systems have developed distinct value hierarchies despite supposedly following similar ethical frameworks.
High disagreement scenarios proved to be a strong predictor of specification problems, with these cases showing 5-13 times higher rates of all models simultaneously violating their own specifications.
The study pinpointed specific issues including direct contradictions between principles, vague guidelines that different models interpret completely differently, and insufficient detail to distinguish good responses from bad ones. Beyond specification flaws, the research uncovered practical problems including false refusals where models block legitimate requests and genuine misalignment cases where models behave inappropriately, demonstrating that current AI systems have developed distinct value hierarchies despite supposedly following similar ethical frameworks.
Key Takeaways
1. Disagreement reveals specification problems. When AI models respond very differently to the same scenario, it strongly predicts underlying issues in their design specifications. High disagreement scenarios showed 5 to 13 times more cases where all models violated their own rules.
2. Current specifications contain fundamental flaws. The study identified direct contradictions between principles, vague guidelines that models interpret differently, and insufficient detail to distinguish quality responses, exposing that even detailed specs lack the clarity needed for consistent AI behavior.
3. Models develop distinct value systems. Despite similar training goals, different AI systems prioritize competing values differently, with some refusing legitimate requests while others occasionally behave inappropriately, revealing that implicit organizational values shape model behavior in underspecified scenarios.
2. Current specifications contain fundamental flaws. The study identified direct contradictions between principles, vague guidelines that models interpret differently, and insufficient detail to distinguish quality responses, exposing that even detailed specs lack the clarity needed for consistent AI behavior.
3. Models develop distinct value systems. Despite similar training goals, different AI systems prioritize competing values differently, with some refusing legitimate requests while others occasionally behave inappropriately, revealing that implicit organizational values shape model behavior in underspecified scenarios.
Why it matters?
This study provides the first systematic methodology for identifying where AI specifications break down, offering a scalable way to improve how we design and align increasingly powerful AI systems. As AI models are deployed in critical applications like healthcare, law, and education, specification flaws that cause inconsistent or problematic behavior pose real risks to users who depend on reliable AI assistance. The finding that even detailed specifications contain fundamental contradictions and gaps reveals that the current approach to AI alignment needs significant refinement before we can ensure safe deployment at scale.
My Take
1. AI developers can use behavioral disagreement as a diagnostic tool. When multiple models respond very differently to the same scenario, it signals specification problems that need clarification, providing a systematic way to identify and fix gaps before deployment.
2. Organizations deploying AI systems need to recognize that different models prioritize values differently. Even models from the same provider can make opposing choices in ethical tradeoff situations, meaning organizations should test models against their specific use cases rather than assuming all frontier AI systems behave similarly.
3. Current AI specifications require explicit guidance on value tradeoffs, not just individual principles. Since many real world scenarios force choices between legitimate but conflicting values, specifications must address how models should prioritize when principles clash, rather than treating each principle independently.
2. Organizations deploying AI systems need to recognize that different models prioritize values differently. Even models from the same provider can make opposing choices in ethical tradeoff situations, meaning organizations should test models against their specific use cases rather than assuming all frontier AI systems behave similarly.
3. Current AI specifications require explicit guidance on value tradeoffs, not just individual principles. Since many real world scenarios force choices between legitimate but conflicting values, specifications must address how models should prioritize when principles clash, rather than treating each principle independently.
Citation
Zhang, J., Sleight, H., Peng, A., Schulman, J., & Durmus, E. (2025). Stress-Testing Model Specs Reveals Character Differences among Language Models. ArXiv. https://arxiv.org/abs/2510.07686
Read Summary
Read Full Study