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Radboud University
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Neural retrievers, despite their success on standard benchmarks, fail spectacularly when forced to reason about set-theoretic constraints, revealing a reliance on spurious correlations rather than true compositional understanding.
Current benchmarks fail to rigorously evaluate deep research agents, but a new framework leveraging structured knowledge bases and synthetic data offers a verifiable and scalable solution.
