Systematic optimization of implicit-failure severity parameters

Investigate the optimal parameters for calculating implicit-failure severity, including the error-type and absorption-pattern weights used by the IF-based scoring and reinforcement-learning procedures.

Background

The paper assigns qualitative heuristic weights to implicit-failure error types and absorption patterns to compute trace-level IF risk and IF costs. A sensitivity study shows that candidate rankings remain stable under small perturbations, but the reported results also indicate that the chosen weighting scheme is not optimal for every process metric, particularly IF-count density and IF-cost density. The authors therefore identify a need for a more systematic investigation of the parameter choices governing IF severity.

This problem concerns determining weighting parameters that reliably reflect process risk and produce efficient training and selection behavior across different multi-agent systems, benchmarks, and evaluation metrics.