Beyond Aggregate Risk: Role-Stratified Conformal Risk Control for LLM Tool Calls

תקציר מקורי באנגליתarXiv:2607.24343v1 Announce Type: cross Abstract: Language-model agents act through structured tool calls whose arguments carry different risks. Untrusted content may safely influence an email body but should not determine a recipient, account, command, or credential. Existing statistical methods typically control risk over the entire action, allowing failures in rare, high-risk fields to be obscured by benign arguments. We introduce role-stratified per-field conformal risk control, a calibration layer that wraps any per-field detector and sets separate thresholds and risk budgets for semantic argument roles. For a role with prevalence $p_r$, aggregate-only certification must use an effective budget of $\alpha p_r$ to guarantee role-specific risk $\alpha$, whereas role-stratified calibrati