What it measures

Vals evaluates agentic legal work using Harvey’s remediated private holdout and an updated harness.

How to read the result

Read strict task pass rate together with individual-criterion pass rate. The task requires producing legal work using source files and document tools.

Limits and comparison traps

This version supersedes the archived Vals v1 board. Vals explicitly states that scores are not comparable with v1 or with Artificial Analysis’s independent Harvey LAB-AA.

Published evaluation

The linked publication documents the task and scoring procedure. Model results retain the source’s evaluation setting, native score units and available uncertainty.

Where this fits in an evaluation suite

Read accuracy together with error and abstention rates. Domain averages can hide language gaps, specialist failures, and poorly calibrated confidence.

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.

PRIMARY EVIDENCE & FURTHER READING

  1. HLAB-Vals-1.1 — evaluation methodology and results ↗

    Vals AI · 2026

Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.