[NEW_PATTERN]_{20}_{SAME_PATCH_DIFFERENT_TOOL}.

[buabeng2]

[NEW_PATTERN]{20}{SAME_PATCH_DIFFERENT_TOOL}.

The pattern is when a patch is attempted to be made, and it is unsuccessful. The unsuccessful patch is then followed up by attempting the same thing again but with a different tool. The LLM considers the failure to have to do specifically with the tool, and just retries the same patch. Usuaully this results in an LLM in a stuck state where it continues to attempt and do the same thing to fix issues. This can be found in gpt-5-mini model with the instanceID mwaskom__seaborn-3187. Here is a representation shown here.
{
"label": "str_replace_editor: str_replace",
"args": {
"path": "/testbed/seaborn/_core/scales.py",
"file_text": "",
"view_range": [],
"old_str": " if prop.legend:\n axis.set_view_interval(vmin, vmax)\n locs = axis.major.locator()\n locs = locs[(vmin <= locs) & (locs <= vmax)]\n labels = axis.major.formatter.format_ticks(locs)\n new._legend = list(locs), list(labels)\n\n return new\n",
"new_str": " if prop.legend:\n axis.set_view_interval(vmin, vmax)\n locs = axis.major.locator()\n locs = locs[(vmin <= locs) & (locs <= vmax)]\n formatter = axis.major.formatter\n labels = formatter.format_ticks(locs)\n # Attempt to capture any multiplicative offset used by the formatter\n offset = None\n try:\n # Many formatters (e.g. ScalarFormatter) provide a get_offset() method\n off = formatter.get_offset()\n except Exception:\n off = None\n if off:\n offset = str(off)\n new._legend = list(locs), list(labels) if offset is None else (list(locs), list(labels), offset)\n\n return new\n",
"insert_line": 0,
"edit_status": "failure: unknown"
},
"flags": {},
"phases": [
"patch"
],
"step_indices": [
33
],
"tool": "str_replace_editor",
"command": "",
"subcommand": "str_replace",
"id": "33:str_replace_editor: str_replace"
},
{
"label": "python",
"args": [
"-",
"from pathlib import Path\np=Path('/testbed/seaborn/_core/scales.py')\ns=p.read_text()\nold=''' if prop.legend:\n axis.set_view_interval(vmin, vmax)\n locs = axis.major.locator()\n locs = locs[(vmin <= locs) & (locs <= vmax)]\n labels = axis.major.formatter.format_ticks(locs)\n new._legend = list(locs), list(labels)\n\n return new\n'''\nif old in s:\n new=''' if prop.legend:\n axis.set_view_interval(vmin, vmax)\n locs = axis.major.locator()\n locs = locs[(vmin <= locs) & (locs <= vmax)]\n formatter = axis.major.formatter\n labels = formatter.format_ticks(locs)\n # Attempt to capture any multiplicative offset used by the formatter\n offset = None\n try:\n # Many formatters (e.g. ScalarFormatter) provide a get_offset() method\n off = formatter.get_offset()\n except Exception:\n off = None\n if off:\n offset = str(off)\n new._legend = list(locs), list(labels) if offset is None else (list(locs), list(labels), offset)\n\n return new\n'''\n s=s.replace(old,new)\n p.write_text(s)\n print('patched')\nelse:\n print('pattern not found')"
],
"flags": {
"heredoc": true
},
"phases": [
"validation"
],
"step_indices": [
34
],
"tool": "",
"command": "python",
"subcommand": "",
"id": "34:python"
},
{
"label": "sed",
"args": [
"/testbed/seaborn/_core/scales.py"
],
"flags": {
"n": "1,440p"
},
"phases": [
"patch"
],
"step_indices": [
35
],
"tool": "",
"command": "sed",
"subcommand": "",
"id": "35:sed"
},

Right here the json shows the process of the replacement of the str_replace method with manual patching to make everything work.
In here this is used instead of the Graphtrajectory due to the large size of the image.
gpt-5-mini-mwaskom__seaborn-3187.pdf