Perplexity AI Releases WANDR: An Open Benchmark Evaluating Research Agents That Must Search Wide And Deep

AI Releases WANDR: An Open Benchmark Evaluating Research Agents That Must Search Wide And Deep">

Research agents already handle real knowledge work today. Teams delegate competitive mapping, due diligence, and literature review to them. However, most benchmarks test a single answer, not large evidence-backed collections. Perplexity targets that gap with a new open benchmark.

Perplexity released WANDR (Wide ANd Deep Research) . It is an open benchmark and evaluation harness. It is built around 500 realistic, challenging data-collection tasks for knowledge work. WANDR is the wide sibling of Perplexity’s DRACO benchmark for deep research. DRACO asks whether an agent produces an accurate, complete, objective long-form report. WANDR instead asks whether it can build a large collection with evidence.

At its core, WANDR tests two demands together. Wide means discovering a large, often open-ended set of qualifying entities. Deep means investigating every entity enough to support each claim with evidence. Combining both changes the problem for agents. A few compelling examples are not enough here. A polished narrative built on incomplete research also falls short.

To capture this, WANDR uses a composable qualification key hierarchy . One task might request company(n) -> employee(m) -> url(k) . This means n qualifying companies, m employees each, and k supporting pages each. Every complete path through the tree gets validated independently. The same structure can represent a flat list, nested search, or matrix.

To ground that hierarchy, consider the released ceo_cfo_appointments task. It asks for at least 70 US-based companies. Each must have a CEO or CFO appointment first announced between March 1 and April 30, 2026. For each, the agent supplies one authoritative appointment page. A subtask adds a listing-authority page per company. Together, the task requires 140 source-backed records.

Concretely, the two hierarchies and one submitted record look like this:

Beyond single examples, WANDR builds its tasks from real usage. It starts from de-identified patterns seen in production, not synthetic prompts. A semi-automated pipeline then turns those patterns into tasks. The pipeline runs four stages: seeding, authoring, admission, and curation. It uses an interleaved author-critic loop with mechanical linting.

As a result, the median task asks for 50 members and 245 records overall. Across all 500 tasks, WANDR calls for 170,495 source-backed records. Tasks split into 167 lower, 166 middle, and 167 higher difficulty. Difficulty depends on per-record work, not scale alone.