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Verification: test what the model can't see before calling it done #93

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Description

@nasser1941

Problem

In a GUI task, the model marked "Test the fix by running the app" as done after only py_compile and an import test. It never ran the app. It changed the window's layout several times without seeing it, and called the work finished each time, while the actual bug went unfound.

The bug could be reproduced in about a minute headless (QT_QPA_PLATFORM=offscreen, fake frames, measuring the window and taking screenshots), as it was later.

Proposal

Teach the model, in its instructions, how to verify work it can't see:

  • What counts as tested: a todo or claim of testing is only true for what was run. Say what was checked and what wasn't.
  • GUI, web and visual changes: reproduce headlessly where possible: Qt's offscreen platform, a headless browser, rendering to an image and looking at it with view_image. Otherwise ask the user to check, and say what to look for.
  • Bug fixes: reproduce the bug first, then show it's gone.

Keep it short, since the instructions go with every request.

Acceptance criteria

  • Instructions for verifying, including visual changes
  • A benchmark task that rewards reproducing before fixing, run before and after
  • Docs

Activity

  1. added a commit that references this issue on Oct 10, 2026
    910e31b
  2. nasser1941 commented on Oct 10, 2026

    @nasser1941
    OwnerAuthor

    Shipped in 0.25.0 (#100), with lcode bench --tasks runtime-bug. The measurements are in #100. In short, for qwen3.6-35b:

    • Pass rate: the guidance doesn't change it on the new task. It's 0/4 and 0/5 both before and after; the task is too hard for this model.
    • Reproducing first: slightly more runs reproduce the bug before editing (4/5 vs 3/5).
    • Default tasks: no regression, 8/8.

    The PyPI build runs as expected. In a manual --no-think run, though, the model still edited before reproducing, so following the guidance depends on the model. The task stays as a harder benchmark for stronger models.

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