PAIRBench: Complete Response-Function Coverage for Auditing Policy-Conditioned Safety Guards

Policy-conditioned safety guards decide whether a request violates a policy supplied at runtime. Policy revisions require correct updates on affected requests and stable decisions elsewhere. High cell-level scores can coexist with incomplete recovery of joint policy–scenario patterns, while selected relational tests leave other patterns unmeasured. We introduce PAIRBENCH, a benchmark covering all 16 deterministic binary response functions formed by two policies and two scenarios. Its human-reviewed panel balances 160 quartets; $EF{R}_{16}$ averages exact recovery equally across functions, and matched policy interventions test updates and stability with scenarios fixed. DynaGuard and SingGuard achieve cell accuracies of 0.969 and 0.986 but $EF{R}_{16}$ scores of 0.888 and 0.944. Complete stratification yields lower root-mean-square error for population-level exact recovery than the tested reduced panels at a matched 80-quartet budget. Four guard families meet prospectively frozen selective-update criteria, and a separate policy-replacement control confirms behavioral dependence on the supplied policy in three tested systems. On a harder document-rich panel, LPG and SingGuard show lower selective updating and transition recovery. The benchmark connects complete local function coverage with controlled tests of policy response, complementing per-decision scores with an explicit population audit.