WANDR benchmark: Evaluating research agents that must search wide and deep

WANDR (Wide ANd Deep Research) is an open benchmark and evaluation harness containing 500 realistic knowledge-work tasks that test whether research agents can both discover large sets of qualifying entities and gather evidence for every required claim, complementing the DRACO benchmark’s focus on long-form reports. Its tasks model nested research structures such as finding companies, employees, and supporting URLs, with a median requirement of 50 members and 245 source-backed records per task, totaling more than 170,000 records across the release. Rather than relying on fixed answer keys, WANDR grades each submitted record by re-fetching cited sources and evaluating page usability, claim scope, excerpt fidelity, and evidentiary support, then reports soft and hard precision, recall, and F1 scores that distinguish partial from fully completed research paths. In tests of six production systems, Perplexity’s Search as Code achieved the highest main-run results at 0.363 soft F1 and 0.133 hard F1, indicating that comprehensive, evidence-backed research remains difficult even for leading systems. Results showed that systems commonly provide some valid findings but struggle to meet requested coverage, complete deeper hierarchies, and supply excerpts that support all aspects of their claims; discovery shortfalls and evidence construction were particularly significant bottlenecks. The public tasks, verifier, diagnostics, and technical report are intended to support development, evaluation, and potentially reinforcement-learning training for agents that can better plan, verify, and complete broad research assignments.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.