Moderation layers are increasingly a core component of many products built on user- or model-generated content. However, drafting and maintaining domain-specific safety policies remains costly. We present Deep Policy Research (DPR), a minimal agentic system that drafts a full content moderation policy based on only human-written seed domain information. DPR uses a single web search tool and lightweight scaffolding to iteratively propose search queries, distill diverse web sources into policy rules, and organize rules into an indexed document. We evaluate DPR on (1) the OpenAI undesired content benchmark across five domains with two compact reader LLMs and (2) an in-house multimodal advertisement moderation benchmark. DPR consistently outperforms definition-only and in-context learning baselines, and in our end-to-end setting it is competitive with expert-written policy sections in several domains. Moreover, under the same seed specification and evaluation protocol, DPR outperforms a general-purpose deep research system, suggesting that a task-specific, structured research loop can be more effective than generic web research for policy drafting. We release our experiment code at https://github.com/xiaowu0162/deep-policy-research.

Hierarchical semantic classification is essential for taxonomy-driven applications, yet existing classifiers often optimize flat label likelihoods and therefore weakly control parent-child validity, semantic error severity, coarse-to-fine coherence, and out-of-scope rejection. This paper aims to align LLM-based classifiers with the full structured utility of hierarchical prediction rather than treating taxonomy labels as independent targets. We propose TaxoAgent, a utility-aligned policy optimization framework that formulates taxonomy prediction as a verified parent-to-child decision trajectory. Given an input, TaxoAgent first predicts coarse parent labels, expands them into fine-grained child decisions, and verifies the resulting trajectory with a composite utility that jointly measures outcome correctness, hierarchy consistency, semantic proximity, and trajectory coherence; group-relative policy optimization then reinforces high-utility trajectories over low-utility alternatives. Across IOS-APP-ZH, Google-APP-EN, RCV1, WOS, and CLINC150, TaxoAgent consistently improves Micro-F1 and Macro-F1, reduces hierarchy inconsistency, strengthens long-tail classification, and improves OOS-F1 by up to 3.68 points over the strongest prior baseline. These results show that treating taxonomies as structured decision spaces offers a practical and extensible path toward more reliable, adaptable, and utility-aware semantic classifiers.