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gehuageyan  updated a dataset about 10 hours ago
Lexmount/LexBench-Headless-Browser
gehuageyan  published a dataset about 10 hours ago
Lexmount/LexBench-Headless-Browser
gehuageyan  updated a dataset about 20 hours ago
Lexmount/LexBench-WebArena-Replay
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Organization Card

Lexmount

Lexmount builds browsers and web-access tools for AI agents. Our platform provides Web Search, Web Fetch and managed cloud browsers, with Moli as our browser engine.

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Browser Eval and LexBench

Browser Eval compares browsers and web tools through direct tool calls and fixed browser trajectory replay. It favors programmatic scoring where tasks have definite answers, retains evidence for each result, and turns saved results into HTML reports readers can inspect, rebuild and share. Optional verification provides a separate check of the results.

Our versioned benchmark datasets are available here:

Dataset What it evaluates
LexBench-Web-Fetch Page content, documents, structured records, resource discovery and file delivery
LexBench-Web-Search Search relevance, ranking and explicit source requirements
LexBench-WebMainBench Main-content extraction from frozen HTML, using a reference-corrected upstream edition
LexBench-SimpleQA-Verified Reference-page retrieval and answer-bearing results for unchanged publisher questions selected by length
LexBench-WCXB Source-qualified frozen web pages and extraction references
LexBench-Headless-Browser Raw CDP, browser-driver compatibility and web-platform behavior on deterministic fixtures
LexBench-Trajectory-Replay Curated fixed-action browser tasks and replay trajectories
LexBench-WebArena-Replay Separate human-recording, corrected-task and full official-task configurations

Each dataset card describes its task scope, use, sources and evaluation limits. Derived datasets retain upstream attribution and document their changes.

Evaluation examples

The Web Fetch comparison shows how task requirements distinguish successful requests from complete content delivery. The Web Search comparison explains relevance grading, ranking quality, latency and cost with query-level examples. Both articles link the measured populations and evaluation conditions.