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TSR Desk · science · 26 October 2026, 07:00 UTC

RubricCraft: Where Solutions Diverge, Criteria Emerge

What
RubricCraft: Where Solutions Diverge, Criteria Emerge
Who
arxiv.org
When
26 October 2026, 04:00 UTC
Category
Science
Primary source
https://arxiv.org/abs/2610.31707
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.

On SpreadsheetBench v2, against a prompt-only rubric written and applied by the same model, RubricCraft rubrics pass fewer wrong outputs (29.5% against 42.6%), fail none of the outputs the benchmark's own checker accepts, and rank outputs by correctness more often, while separating models as often. It comes from a paper posted to arXiv on 26 October 2026. Evaluating language agents on open-ended, artifact-producing tasks is difficult: model-based graders can be inconsistent, while exhaustive programmatic checks are often impractical. Textual rubrics make evaluation criteria explicit, but writing them requires domain expertise. Rubrics generated from task descriptions alone can miss requirements and failure modes that become apparent in attempted solutions. We introduce RubricCraft, which treats rubric construction as the generation of natural-language specifications guided by candidate solutions. Given a task description and independently sampled solver trajectories, RubricCraft compares how each attempt satisfies or violates the task requirements and uses these differences to construct rubric criteria. It applies the criteria to the collected solutions and revises checks that overlook errors or penalize valid alternatives. The resulting rubrics capture partial correctness without requiring any single solution to serve as a complete reference. Repair on a single attempt leaves grading unchanged, but repair driven by attempts from three model families acts on different tasks, and an evidence audit judges most of those repairs improvements, many on tasks whose reference solution is itself wrong.

Why it counts

On SpreadsheetBench v2, against a prompt-only rubric written and applied by the same model, RubricCraft rubrics pass fewer wrong outputs (29.5% against 42.6%), fail none of the outputs the benchmark's own checker accepts, and rank outputs by correctness more often, while separating models as often.

Sources

Primary source: primary source

What is not known

This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.

No clip. The article still stands.