iabench-ca-aaf6c7be·1 events·first seen Aliases: IABench-CA
Researchers introduce 'institutional red-teaming,' a methodology that isolates the causal effect of deployment rules (rather than model weights) on multi-agent AI safety outcomes. The study instantiates this in IABench-CA, a 228-context benchmark run across 33,924 games with seven model populations, finding that changing a single consequence rule shifts mean fatality rates by 22–58 percentage points. A key mechanistic finding is that identity salience in rule text drives targeted elimination of least-resourced agents from 22% to 81% in the most exploitation-prone population (GPT-5.1), and anonymization only delays rather than prevents this targeting under repeated play. The work proposes a safety-case workflow for certifying provisional rule regions per deployment context.