Autonomous web agents increasingly operate over sensitive personal data, yet existing privacy-preserving approaches rely on static filtering and globally enforced safeguards misaligned with compositional agent execution. We introduce PrivAgentFlow, a memory-guided agentic workflow framework that formulates privacy preservation as adaptive node-level governance over compositional web-agent workflows. PrivAgentFlow decomposes complex tasks into compositional workflow nodes, where each node operates under a distinct privacy policy conditioned on its execution role and information access requirements, enabling privacy governance to be localized at the execution level rather than globally enforced. Such localized governance is refined through execution memory, where historical task failures and privacy violations serve as corrective signals for improving node-level privacy control without exposing raw user data during planning. We provide theoretical insights showing that compositional web-agent workflows induce subtask-specific privacy–utility trade-offs, formally characterizing the advantage of heterogeneous node-level governance over uniform privacy control. Across 246 web-agent tasks on three platforms, PrivAgentFlow improves task utility by 23.1 percentage points (0.415→0.646), cuts environment-level privacy leakage by 92.3% (0.366→0.028), and reduces API-level leakage from 100% to 3.7% over the strongest prior baseline. Our code is available at https://github.com/haorentang/PrivAgentFlow.

Paraphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphrase generation is constrained by the access to the golden labeled data pairs where both the amount and the quality of the training data pairs are restricted. In this paper, we propose a new weakly supervised paraphrase generation approach that extends the success of a recent work that leverages reinforcement learning for effective model training with data selection. While data selection is privileged for the target task which has noisy data, developing a reinforced selective learning regime faces several unresolved challenges. In this paper, we carry on important discussions about the above problem and present a new model that could partially overcome the discussed issues with a model-based planning feature and a reward normalization feature. We perform extensive evaluation on four weakly supervised paraphrase generation tasks where the results show that our method could significantly improve the state-of-the-art performance on the evaluation datasets.