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.

Multilingual audio-text retrieval (ML-ATR) is a challenging task that aims to retrieve audio clips or multilingual texts from databases. However, existing ML-ATR schemes suffer from inconsistencies for instance similarity matching across languages. To address the inconsistency issue in multilingual audio-text retrieval, we first identify two intuitive factors that contribute to inconsistency: misalignment between audio and multilingual text embeddings, and error propagation in model optimization. By systematically analyzing these factors, we derive theoretical weight error upper bounds for quantifying their effects and find that the main source of inconsistency is the data distribution error during training. This finding motivates our solution to reduce data distribution errors.We propose a consistent ML-ATR scheme using 1-to-k contrastive learning and audio-English co-anchor contrastive learning, aiming to mitigate the negative impact of data distribution error on recall and consistency in ML-ATR. Experimental results on the translated AudioCaps and Clotho datasets show that our scheme achieves state-of-the-art performance on recall and consistency metrics for eight mainstream languages, including English. Our code will be available at https://github.com/ATRI-ACL/ATRI-ACL.