As LLMs become increasingly woven into everyday workflows, user queries send to cloud-hosted LLMs routinely mix task-essential content with task-non-essential sensitive disclosures, yet type-based PII redaction is context-agnostic and may raise two issues: over disclosing untyped sensitive context and over-removing answer-bearing spans. We recast privacy-preserving query rewriting under Contextual Integrity: a span should be forwarded only if it is necessary for the task. We build the first task-based CI benchmark for privacy conscious delegation, comprising 3,167 samples that combine high-quality synthetic data spanning 11 tasks and 20 task types, WildChat based real-user queries, and a medical challenge set with dense sensitive information. Building on this benchmark, we propose a CI guided reinforcement learning framework that converts essential and non-essential sensitive spans into verifiable optimization signals, and train a query rewriter to preserve task-critical information while suppressing unnecessary sensitive disclosure. Experiments show that our learned rewriter achieves the best privacy–utility trade-off, achieving up to +10.1 average utility over on-device baselines.

Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking.To address this issue, we propose Dual-Horizon Credit Assignment (DuCA), a framework that disentangles optimization across time scales. Its core, Horizon-Independent Advantage Normalization (HIAN), separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update.Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82% relative improvement in conversion rate, reducing inter-sentence repetition by 82.28%, and lowering identity detection rate by 27.35%, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation.