WideSearch

Reported in recent model launches

These model reports include this benchmark. Their harnesses, task subsets, scoring policies, and resource budgets can differ; follow each report’s evaluation notes before joining the numbers.

What it measures

Broad information gathering asks an agent to identify many entities and their attributes across web sources.

How to read the result

Inspect missing entities, incorrect attributes and incomplete coverage separately. Item F1 and strict whole-table success reward different outcomes.

Limits and comparison traps

Search budget and context management are part of the system. A report using item F1 should not be compared directly with a strict all-cells-correct leaderboard.

Benchmark reference notes

Broad-coverage structured table/entity collection via web search (gathering dozens of verified entities and attributes in parallel).

Where this fits in an evaluation suite

A transcript that says “done” is weaker evidence than the verified final state. Track policy violations, recoveries, and invalid attempts alongside success.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Pin the benchmark release, task split, model configuration, tools, sampling budget, and evaluation harness described by the primary source.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.