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

• URL: http://arxiv.org/abs/2603.25342v1
• Date: Thu, 26 Mar 2026 11:37:26 GMT
• Title: From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents
• Abstract summary: Deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis.<n>We formalize DRA behavior through the lens of category theory, modeling deep research workflow as a composition of structure-preserving maps.<n>We introduce a novel mechanism-aware benchmark with 296 questions designed to stress-test agents along four interpretable axes.
• Score: 25.080554451314683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution. To bridge this gap, we formalize DRA behavior through the lens of category theory, modeling deep research workflow as a composition of structure-preserving maps (functors). Grounded in this theoretical framework, we introduce a novel mechanism-aware benchmark with 296 questions designed to stress-test agents along four interpretable axes: traversing sequential connectivity chains, verifying intersections within V-structure pullbacks, imposing topological ordering on retrieved substructures, and performing ontological falsification via the Yoneda Probe. Our rigorous evaluation of 11 leading models establishes a persistently low baseline, with the state-of-the-art achieving only a 19.9\% average accuracy, exposing the difficulty of formal structural stress-testing. Furthermore, our findings reveal a stark dichotomy in the current AI capabilities. While advanced deep research pipelines successfully redefine dynamic topological re-ordering and exhibit robust ontological verification -- matching pure reasoning models in falsifying hallucinated premises -- they almost universally collapse on multi-hop structural synthesis. Crucially, massive performance variance across tasks exposes a lingering reliance on brittle heuristics rather than a systemic understanding. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formidable open challenge.\footnote{Our implementation will be available at https://github.com/tzq1999/CDR.

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