AI RESEARCH
From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents
arXiv CS.LG
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ArXi:2603.25342v1 Announce Type: new 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.