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WANDR Benchmark: Evaluating Research Agents That Must Search Wide and Deep
The WANDR benchmark is a new open-source framework designed to evaluate how well AI research agents perform complex, multi-layered data gathering.
What: WANDR (Wide ANd Deep Research) uses a hierarchical qualification system to test an agent's ability to maintain factual accuracy while performing broad, iterative discovery tasks.
Why it matters: Standard benchmarks often fail to capture the multi-step nature of actual knowledge work; WANDR addresses the need to measure reliability across long-running, self-directed research loops.
Decoder
- Benchmark: A standardized set of tests used to evaluate the relative performance of a computer system, model, or algorithm.
Original article
WANDR (Wide ANd Deep Research) is an open benchmark and evaluation harness built around realistic and challenging data-collection tasks for knowledge work. Wide and deep research allows agents to sustain broad discovery without sacrificing factual quality from one record to the next. WANDR captures that structure with a flexible, composable qualification key hierarchy.
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