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Markdown edition

# SWE-Verifier selectively compares coding-agent actions

> SWE-Verifier selectively compares actions; reported gains carry modeled cost.

By BIGCHANGE.AI Editorial

Published: 2026-10-25T11:28:38.000Z
Updated: 2026-10-25T11:28:38.000Z
Canonical: https://bigchange.ai/blog/swe-verifier-selective-coding-agent-action-checks

![Orange key on keyboard.](https://bigchange.ai/api/media/file/swe-verifier-selective-key-hero-v1.png)
AI-generated illustration by BIG CHANGE.

[SWE-Verifier](https://arxiv.org/html/2610.30284v1), described in an October 21 paper, trains a Qwen3.5-4B model on preferences among replayed coding-agent actions.

## The big change

- **What changed:** It scores a proposed action; a low score triggers alternatives, ranked before execution.
- **Why it matters:** Developers can target costly comparisons to suspect actions.
- **What to watch:** Reported cost excludes local verifier inference.

## Results

On 500 SWE-bench Verified tasks in mini-swe-agent, MiniMax-M2.7 moved from 75.47% to 77.20% resolution at 1.09 times modeled baseline cost. DeepSeek-V4-Flash Preview moved from 72.60% to 74.27% at 1.13 times. The Two-Stage Rubric variant compared alternatives at fewer than 4% of steps.

MiniMax thresholds used 202 tasks. The table leaves variation undefined and does not establish significance. BIG CHANGE did not rerun it. The linked repository returned HTTP 404 on October 25; artifacts and setup remain unverified.

[Earlier coverage](https://bigchange.ai/blog/harness-delta-attribution-agent-benchmark-gains) examines distinct harness attribution.

## Sources

- [SWE-Verifier paper](https://arxiv.org/html/2610.30284v1) — Methods, results, cost assumptions.