Generalization of deep research agent results to broader grounded reasoning tasks

Determine whether the results reported for state-of-the-art deep research agents—systems that conduct multi-step research on the public internet using web search tools—generalize across other grounded reasoning tasks that operate over different data regimes and requirements beyond internet-based deep research.

Background

The paper contrasts internet-based "deep research" agents with enterprise-oriented grounded reasoning tasks. Deep research systems operate over publicly available information using web search, whereas many practical applications require reasoning over proprietary or closed corpora and different tool assumptions.

The authors introduce KARLBench to evaluate grounded reasoning capabilities across six distinct regimes, noting that success on web-based deep research does not automatically imply competence on other forms of grounded reasoning. This motivates the explicit uncertainty regarding cross-task generalization of deep research results.

References

However, deep research relies on publicly available, non-proprietary, knowledge, and black-box web search tools. Thus, it is not entirely clear whether the reported state-of-the art deep research results indeed generalize across other grounded reasoning tasks.

— KARL: Knowledge Agents via Reinforcement Learning  (2603.05218 - Chang et al., 5 Mar 2026) in Introduction

DeepResearch Bench \citep{du-etal-2025-drb} spans 22 domains and includes questions in both English and Chinese. In our experiments, we used only English examples and leave multilingual evaluation to future work.

— Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research  (2608.24306 - Hirsch et al., 25 Aug 2026) in Appendix, Section “More Experimental Details”