From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents

Deep Research Agents (DRAs) answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneous sources. We introduce a category-theoretic framework for evaluating such agents. The framework treats deep research as a structured mapping from user intent to evidence-grounded conclusions, making retrieval traces, cross-source alignment, and final synthesis explicit. Guided by this view, we build a mechanism-aware benchmark of 296 bilingual questions covering four structural skills: following multi-hop evidence chains, verifying claims across sources, re-ordering fragmented information, and rejecting unsupported assumptions. We evaluate 16 systems with human verification and find that these tasks remain difficult: the best system reaches 19.9\% average accuracy. The results reveal complementary strengths across systems, but also persistent weaknesses in long-horizon retrieval and intersection-heavy verification. We further instantiate two theory-guided interventions, tracked search and category tools, in API-based agents. These variants improve over their corresponding baselines, suggesting that the framework is useful not only for diagnosis but also for modest, targeted system design.