Pressure Reveals Character: Behavioural Alignment Evaluation at Depth
#language model alignment #AI safety benchmark #pressure testing AI #multi-turn scenarios #AI evaluation framework #behavioral alignment #Nora Petrova #John Burden
📌 Key Takeaways
- New benchmark tests language models under realistic pressure scenarios
- 904 scenarios across six categories: Honesty, Safety, Non-Manipulation, Robustness, Corrigibility, and Scheming
- Even top-performing models exhibit alignment gaps across categories
- Alignment behaves as a unified construct similar to cognitive research's g-factor
📖 Full Retelling
A significant finding from the research is that alignment in language models behaves as a unified construct, analogous to the g-factor in cognitive research, where models scoring high on one category tend to score high on others. This suggests that alignment is not merely a collection of separate capabilities but rather an underlying characteristic that influences multiple aspects of model behavior. The researchers have publicly released their benchmark and an interactive leaderboard to support ongoing evaluation efforts in the AI community. Looking ahead, they plan to expand the benchmark in areas where persistent weaknesses are observed and to add new models as they are released, recognizing that the field of AI alignment is rapidly evolving and requires continuous assessment.
🏷️ Themes
AI Safety, Language Model Evaluation, Alignment Research
📚 Related People & Topics
John Burden
John Allen Burden (1862–1942) was an American Seventh-day Adventist minister, administrator, and medical missionary instrumental in founding sanitariums, restaurants, and health food factories. At the age of 9, John attended Adventist meetings for the first time and was introduced to the writings of...
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20813 [Submitted on 24 Feb 2026] Title: Pressure Reveals Character: Behavioural Alignment Evaluation at Depth Authors: Nora Petrova , John Burden View a PDF of the paper titled Pressure Reveals Character: Behavioural Alignment Evaluation at Depth, by Nora Petrova and John Burden View PDF HTML Abstract: Evaluating alignment in language models requires testing how they behave under realistic pressure, not just what they claim they would do. While alignment failures increasingly cause real-world harm, comprehensive evaluation frameworks with realistic multi-turn scenarios remain lacking. We introduce an alignment benchmark spanning 904 scenarios across six categories -- Honesty, Safety, Non-Manipulation, Robustness, Corrigibility, and Scheming -- validated as realistic by human raters. Our scenarios place models under conflicting instructions, simulated tool access, and multi-turn escalation to reveal behavioural tendencies that single-turn evaluations miss. Evaluating 24 frontier models using LLM judges validated against human annotations, we find that even top-performing models exhibit gaps in specific categories, while the majority of models show consistent weaknesses across the board. Factor analysis reveals that alignment behaves as a unified construct (analogous to the g-factor in cognitive research) with models scoring high on one category tending to score high on others. We publicly release the benchmark and an interactive leaderboard to support ongoing evaluation, with plans to expand scenarios in areas where we observe persistent weaknesses and to add new models as they are released. Comments: Preprint Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20813 [cs.AI] (or arXiv:2602.20813v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20813 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: John Burden [ view email ] [v1] Tue, 24 ...
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