When text similarity misses scientific correctness

A local dashboard for 252 structured LLM scientific-reasoning outputs across 32 scenarios and 8 models.

Main finding: text/reasoning metrics are useful diagnostics, but deterministic numeric and symbolic checks remain necessary.

Model leaderboard

Pass rate by model

Fail count by model

Model comparison table

Click table headers to sort.

Mean metric scores by model

Metric comparison

Correlation with deterministic PASS

PASS/FAIL mean gap

Correlation matrix

Metric correlation matrix heatmap

The matrix comes from the findings-report artifact. Higher correlation between text metrics does not imply correctness.

Numeric error distribution

746
Numeric field comparisons
740
Included numeric comparisons
0.0081%
PASS median relative error
65.4%
FAIL median relative error
90.7%
Failed-field median relative error

Relative-error bins

Failed-only relative-error bins

Failed-only relative error bins

Tolerance layers

Tolerance rescue counts

Interpretation

+25% tolerance multiplier rescued 0 rows. +1 percentage point rescued 1 row. 134 original FAIL rows remained hard fail after +1pp.

Most failures are not tolerance-boundary numeric misses.

Large numeric failure gates

98
Fields with >100% relative error
62
Rows affected
42
Fields with >1000% relative error

Large-failure patterns

Gate candidates

Scenario explorer

Lowest pass-rate scenarios

Scenarios with most >100% numeric-error fields

Scenario table

Category table

Model explorer

Key conclusions