CALIPER: EVIDENCE-CALIBRATED MULTI-EXPERT LLM PRIORS FOR MULTI-OBJECTIVE BAYESIAN OPTIMIZATION

External priors can accelerate Bayesian optimization (BO) precisely when evaluation budgets are too small to recover easily from bad guidance. LLM committees make this opportunity inseparable from a reliability problem: expertise varies by objective, multiple experts may share the same bias, and stated confidence may not predict accuracy. To address this issue, we introduce Counterfactual Adaptive LLM-Integrated Prior Evidence Replay (CALIPER), a plug-in calibration layer for multi-objective BO that learns two complementary forms of trust from newly observed objectives. Relative reliability routes each objective toward the experts that predict it well; absolute reliability controls whether the committee should influence the optimizer at all. CALIPER combines objective-wise reputation, counterfactual validation of confidence, prior-amplitude control, and an explicit no-prior arm. It injects the resulting signal as a residual-GP mean shift, preserving the downstream qLogEHVI/qLogNEHVI acquisition pipeline. We evaluate every prior-control strategy with the same frozen LLM information and a locked, disjoint development/evaluation protocol on ESOL, FreeSolv, and Lipophilicity. The real-prior benchmarks show an asymmetric payoff: CALIPER captures useful guidance on ESOL and Lipophilicity while recovering the no-prior final hypervolume on FreeSolv, where unconditional injection causes negative transfer. Controlled stress tests isolate the mechanism: CALIPER gains 8.1% trajectory AUC when all experts are useful, routes objective-specialized expertise for a 6.3% gain, and stays within 0.6% of no-prior BO when every expert is misleading, compared with a 10.1% loss from fixed injection. These results establish evidence calibration as a practical interface for extracting value from uncertain external knowledge without binding the optimizer to it.