{"astropy__astropy-12907__boLMDca":{"task_name":"astropy__astropy-12907","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): separable.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-13033__UbWNasH":{"task_name":"astropy__astropy-13033","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Read"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): core.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom astropy.time import Time\nfrom astropy.timeseries import TimeSeries\n\n# Test 1: single required column case (should still work)\ntime = Time(np.arange(100000, 100003), format='jd')\nts = TimeSeries(time=time, data={'flux': [99.9, 99.8, 99.7]})\ntry:\n    ts.remove_column('time')\nexcept ValueError as e:\n    print('Single required column case:', e)\n\n# Test 2: BinnedTimeSeries\nfrom astropy.timeseries import BinnedTimeSeries\nbts = BinnedTimeSeries(time_bin_start=time, time_bin_size=1*np.ones(3))\nbts._required_columns = ['time_bin_start', 'time_bin_size']\ntry:\n    bts.remove_column('time_bin_size')\nexcept ValueError as e:\n    print('BinnedTimeSeries case:', e)\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-13236__rKT8GAW":{"task_name":"astropy__astropy-13236","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 37 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 21 times. Full breakdown: Bash (21), Read (6), TodoWrite (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): table.py, test_mixin.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest astropy/table/tests/test_mixin.py::test_ndarray_mixin astropy/table/tests/test_mixin.py::test_structured_ndarray_column_no_warning -xvs 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-13398__jexEERs":{"task_name":"astropy__astropy-13398","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 49 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Write"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (8), TodoWrite (4), Edit (2), Agent (1), Write (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): __init__.py, test_intermediate_transformations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.coordinates import ITRS, AltAz, HADec, EarthLocation\nfrom astropy.time import Time\nfrom astropy import units as u\nimport numpy as np\n\n# Basic smoke test\nt = Time('J2010')\nloc = EarthLocation(-1*u.deg, 52*u.deg, height=0.*u.km)\n\n# Test ITRS -> AltAz\nobj = EarthLocation(-1*u.deg, 52*u.deg, height=10.*u.km)\nitrs = obj.get_itrs(t)\naa = itrs.transform_to(AltAz(obstime=t, location=loc))\nprint(f'ITRS -> AltAz: alt={aa.alt:.6f}, az={aa.az:.6f}')\n\n# Test AltAz -> ITRS\nitrs_back = aa.transform_to(ITRS(obstime=t))\nprint(f'AltAz -> ITRS round-trip: x={itrs_back.cartesian.x:.6f}')\n\n# Test ITRS -> HADec\nhd = itrs.transform_to(HADec(obstime=t, location=loc))\nprint(f'ITRS -> HADec: ha={hd.ha:.6f}, dec={hd.dec:.6f}')\n\n# Test HADec -> ITRS\nitrs_back2 = hd.transform_to(ITRS(obstime=t))\nprint(f'HADec -> ITRS round-trip: x={itrs_back2.cartesian.x:.6f}')\n\n# Check straight overhead\nprint(f'Expected alt ~90 deg: {aa.alt}')\nprint(f'Expected ha ~0: {hd.ha}')\nprint(f'Expected dec ~52 deg: {hd.dec}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-13453__QHyUita":{"task_name":"astropy__astropy-13453","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (2), Edit (2), Agent (1), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): html.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.table import Table\nfrom io import StringIO\n\nt = Table([(1.23875234858e-24, 3.2348748432e-15), (2, 4)], names=('a', 'b'))\n\n# Test RST still works\nwith StringIO() as sp:\n    t.write(sp, format='ascii.rst', formats={'a': lambda x: f'{x:.2e}'})\n    print('RST:')\n    print(sp.getvalue())\n\n# Test CSV still works\nwith StringIO() as sp:\n    t.write(sp, format='csv', formats={'a': lambda x: f'{x:.2e}'})\n    print('CSV:')\n    print(sp.getvalue())\n\n# Test HTML without formats (should still show full precision)\nwith StringIO() as sp:\n    t.write(sp, format='html')\n    print('HTML no formats:')\n    print(sp.getvalue())\n\n# Test HTML with string format spec\nwith StringIO() as sp:\n    t.write(sp, format='html', formats={'a': '.2e'})\n    print('HTML string format:')\n    print(sp.getvalue())\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-13579__Ss3784H":{"task_name":"astropy__astropy-13579","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (3), Edit (2), Agent (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sliced_wcs.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport astropy.wcs\nfrom astropy.wcs.wcsapi import SlicedLowLevelWCS, HighLevelWCSWrapper\nimport astropy.units as u\n\nnx = 100\nny = 25\nnz = 2\nwcs_header = {\n    'WCSAXES': 3,\n    'CRPIX1': (nx + 1)/2,\n    'CRPIX2': (ny + 1)/2,\n    'CRPIX3': 1.0,\n    'PC1_1': 0.0,\n    'PC1_2': -1.0,\n    'PC1_3': 0.0,\n    'PC2_1': 1.0,\n    'PC2_2': 0.0,\n    'PC2_3': -1.0,\n    'CDELT1': 5,\n    'CDELT2': 5,\n    'CDELT3': 0.055,\n    'CUNIT1': 'arcsec',\n    'CUNIT2': 'arcsec',\n    'CUNIT3': 'Angstrom',\n    'CTYPE1': 'HPLN-TAN',\n    'CTYPE2': 'HPLT-TAN',\n    'CTYPE3': 'WAVE',\n    'CRVAL1': 0.0,\n    'CRVAL2': 0.0,\n    'CRVAL3': 1.05,\n}\nfits_wcs = astropy.wcs.WCS(header=wcs_header)\n\n# Test at low level to avoid celestial frame issues\nprint('Full WCS world_to_pixel_values:')\n# World order for FITS WCS: world dim 0 = HPLN, dim 1 = HPLT, dim 2 = WAVE \nresult_full = fits_wcs.world_to_pixel_values(0, 0, 1.05e-10)\nprint(result_full)\n\nprint()\nprint('Sliced WCS (slice at array index 0, i.e. first wavelength):')\nll_sliced_wcs = SlicedLowLevelWCS(fits_wcs, 0)\nprint('Kept world dims:', ll_sliced_wcs._world_keep)\nprint('Kept pixel dims:', ll_sliced_wcs._pixel_keep)\nprint('Slices pixel:', ll_sliced_wcs._slices_pixel)\n\nresult_sliced = ll_sliced_wcs.world_to_pixel_values(0, 0)\nprint('Sliced result:', result_sliced)\nprint()\nprint(f'Full: pixel0={result_full[0]}, pixel1={result_full[1]}')\nprint(f'Sliced: pixel0={result_sliced[0]}, pixel1={result_sliced[1]}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-13977__g7QLpn3":{"task_name":"astropy__astropy-13977","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (2), Agent (1), Edit (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): quantity.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport dataclasses\nimport numpy as np\nimport astropy.units as u\n\n\n@dataclasses.dataclass\nclass DuckArray(np.lib.mixins.NDArrayOperatorsMixin):\n    ndarray: u.Quantity\n\n    @property\n    def unit(self):\n        return self.ndarray.unit\n\n    def __array_ufunc__(self, function, method, *inputs, **kwargs):\n        inputs = [inp.ndarray if isinstance(inp, DuckArray) else inp for inp in inputs]\n        for inp in inputs:\n            if isinstance(inp, np.ndarray):\n                result = inp.__array_ufunc__(function, method, *inputs, **kwargs)\n                if result is not NotImplemented:\n                    return DuckArray(result)\n        return NotImplemented\n\n# Test 1: DuckArray + Quantity (same units)\nresult1 = DuckArray(1 * u.mm) + (1 * u.m)\nprint(f'Test 1: DuckArray(1mm) + 1m = {result1}')\n\n# Test 2: Quantity + DuckArray (same units)\nresult2 = (1 * u.mm) + DuckArray(1 * u.mm)\nprint(f'Test 2: 1mm + DuckArray(1mm) = {result2}')\n\n# Test 3: Quantity + DuckArray (different units) - this was failing\nresult3 = (1 * u.m) + DuckArray(1 * u.mm)\nprint(f'Test 3: 1m + DuckArray(1mm) = {result3}')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-14096__nk948YH":{"task_name":"astropy__astropy-14096","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Edit (2), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sky_coordinate.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport astropy.coordinates as coord\n\n# Test 1: Normal SkyCoord attribute access\nc = coord.SkyCoord('00h42m30s', '+41d12m00s', frame='icrs')\nprint('ra:', c.ra)\nprint('dec:', c.dec)\n\n# Test 2: Frame transform \nprint('galactic:', c.galactic)\n\n# Test 3: Non-existent attribute on plain SkyCoord\ntry:\n    c.nonexistent\nexcept AttributeError as e:\n    print('Plain missing attr:', e)\n\n# Test 4: Subclass with working property\nclass custom_coord(coord.SkyCoord):\n    @property\n    def prop(self):\n        return self.ra\n\nc2 = custom_coord('00h42m30s', '+41d12m00s', frame='icrs')\nprint('Subclass prop:', c2.prop)\n\n# Test 5: Subclass with non-existent attribute (not a property)\ntry:\n    c2.totally_fake\nexcept AttributeError as e:\n    print('Subclass missing attr:', e)\n\n# Test 6: The original bug - property that accesses non-existent attr\nclass custom_coord2(coord.SkyCoord):\n    @property\n    def prop(self):\n        return self.random_attr\n\nc3 = custom_coord2('00h42m30s', '+41d12m00s', frame='icrs')\ntry:\n    c3.prop\nexcept AttributeError as e:\n    print('Property internal error:', e)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-14182__bPWw3S9":{"task_name":"astropy__astropy-14182","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): rst.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest /testbed/astropy/io/ascii/tests/test_rst.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-14309__eMmbU2Z":{"task_name":"astropy__astropy-14309","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Read","Bash"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): connect.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.io.registry import identify_format\nfrom astropy.table import Table\nresult = identify_format('write', Table, 'bububu.ecsv', None, [], {})\nprint('Result:', result)\n\n# Also verify FITS files still get identified\nresult2 = identify_format('write', Table, 'test.fits', None, [], {})\nprint('FITS result:', result2)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-14365__wtyLYyA":{"task_name":"astropy__astropy-14365","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Write","Agent","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): qdp.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.io.ascii.qdp import _line_type\n# Test uppercase (original behavior)\nprint(_line_type('READ SERR 3'))\n# Test lowercase\nprint(_line_type('read serr 1 2'))\n# Test mixed case\nprint(_line_type('Read Serr 1 2'))\nprint(_line_type('READ TERR 1'))\nprint(_line_type('read terr 1'))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-14369__Rnkkqre":{"task_name":"astropy__astropy-14369","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): cds.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"rm -f /testbed/astropy/units/format/cds_parsetab.py /testbed/astropy/units/format/cds_lextab.py\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-14508__jNLYcD6":{"task_name":"astropy__astropy-14508","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 52 steps but only 33 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Edit","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 24 times. Full breakdown: Bash (24), Read (5), Agent (1), TaskOutput (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): card.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\n\n# Test various values to understand behavior differences\nvalues = [\n    0.009125,\n    -467374636747637647347374734737437.0,\n    1.2345377437887837487e88,\n    6324767364763746367e-33,\n    1.0,\n    0.0,\n    -0.0,\n    1e-10,\n    1e10,\n    3.141592653589793,\n    float('inf'),\n    float('-inf'),\n    1.0000000000000002,  # value very close to 1\n    np.float64(0.009125),\n    np.float32(0.009125),\n]\n\nfor v in values:\n    s = str(v)\n    g = f'{v:.16G}'\n    print(f'value={v!r:>45s}  str={s:>25s}  .16G={g:>25s}  same={s==g}')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-14539__f36cmdp":{"task_name":"astropy__astropy-14539","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): diff.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-14598__vaZtuCU":{"task_name":"astropy__astropy-14598","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 98 steps but only 58 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 35 times. Full breakdown: Bash (35), Read (11), Edit (6), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): card.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.io import fits\n\nprint('Test 1: value ending with double single-quotes')\nall_pass = True\nfor n in range(60, 70):\n    card1 = fits.Card('CONFIG', 'x' * n + \\\"''\\\")\n    card2 = fits.Card.fromstring(str(card1))\n    ok = card1.value == card2.value\n    if not ok:\n        all_pass = False\n        print(f'  FAIL n={n}: {card1.value!r} != {card2.value!r}')\nprint(f'  All pass: {all_pass}')\n\nprint()\nprint('Test 2: double single-quotes in the middle')\nall_pass = True\nfor n in range(50, 70):\n    card1 = fits.Card('CONFIG', 'x' * n + \\\"''\\\" + 'x' * 10)\n    card2 = fits.Card.fromstring(str(card1))\n    ok = card1.value == card2.value\n    if not ok:\n        all_pass = False\n        print(f'  FAIL n={n}')\nprint(f'  All pass: {all_pass}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-14995__bPANBpc":{"task_name":"astropy__astropy-14995","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): ndarithmetic.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest /testbed/astropy/nddata/mixins/tests/test_ndarithmetic.py -q 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-7166__uHNdasX":{"task_name":"astropy__astropy-7166","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 33 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (3), Read (3), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): misc.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nclass A:\n    @property\n    def foo(self):\n        'A.foo doc'\n        return 1\n\nclass B(A):\n    @property\n    def foo(self):\n        return 2\n\np = B.__dict__['foo']\ntry:\n    p.__doc__ = 'test'\n    print('Direct assignment works:', p.__doc__)\nexcept AttributeError as e:\n    print('Cannot set __doc__ directly:', e)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-7336__gVYMtdW":{"task_name":"astropy__astropy-7336","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Bash","Grep","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): decorators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport astropy.units as u\n\nclass PoC(object):\n    @u.quantity_input\n    def __init__(self, voltage: u.V) -> None:\n        pass\n\npoc = PoC(1.*u.V)\nprint('Success:', poc)\n\n# Also test that normal return annotations still work\n@u.quantity_input\ndef get_speed(distance: u.m, time: u.s) -> u.m / u.s:\n    return distance / time\n\nresult = get_speed(10*u.m, 2*u.s)\nprint('Speed:', result)\n\n# And test no annotation still works\n@u.quantity_input\ndef get_dist(speed: u.m/u.s, time: u.s):\n    return speed * time\n\nresult2 = get_dist(5*u.m/u.s, 3*u.s)\nprint('Distance:', result2)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-7606__r5cutze":{"task_name":"astropy__astropy-7606","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Agent","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Grep (3), Read (3), Edit (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): core.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sys, types\n\n# Minimal stub to allow importing astropy.units.core\nsys.modules['astropy'] = types.ModuleType('astropy')\nsys.modules['astropy.utils'] = types.ModuleType('astropy.utils')\n\n# Need to provide the 'deprecated' decorator  \nimport astropy.utils as au\nau.deprecated = lambda *a, **kw: (lambda f: f)\n\n# Provide minversion\nau.minversion = lambda *a, **kw: True\n\n# Need to handle astropy.extern.configobj etc\nsys.modules['astropy.extern'] = types.ModuleType('astropy.extern')\n\n# Mock what we need\nimport astropy.units\nsys.modules['astropy.units'] = types.ModuleType('astropy.units')\n\n# Direct test of UnrecognizedUnit.__eq__\nexec(open('astropy/units/core.py').read().split('class UnrecognizedUnit')[1].split('class _UnitMetaClass')[0], {'__builtins__': __builtins__})\n\" 2>&1 | head -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-7671__uWNyDSq":{"task_name":"astropy__astropy-7671","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Read","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): introspection.py, test_introspection.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest astropy/utils/tests/test_introspection.py::test_minversion -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"astropy__astropy-8707__imJEdmr":{"task_name":"astropy__astropy-8707","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 65 steps but only 45 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Grep","EnterPlanMode"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Read","Edit"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Grep (6), Edit (6), Read (6), TodoWrite (5), EnterPlanMode (1), Agent (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): groovy-bubbling-pudding.md, card.py, header.py, test_header.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom astropy.io.fits import Header, Card\n\n# Test Card.fromstring with bytes\nc = Card.fromstring(b'SIMPLE  =                    T')\nprint('Card from bytes:', c.keyword, '=', c.value)\n\n# Test Header.fromstring with bytes\nh = Header.fromstring(b'SIMPLE  =                    T' + b' '*50)\nprint('Header from bytes:', h['SIMPLE'])\n\n# Test Header.fromstring with bytes and separator\nhstr = b'SIMPLE  =                    T\\nBITPIX  =                    8\\nEND'\nh = Header.fromstring(hstr, sep='\\n')\nprint('Header with sep:', h['SIMPLE'], h['BITPIX'])\n\nprint('All smoke tests passed!')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"astropy__astropy-8872__37HQuE4":{"task_name":"astropy__astropy-8872","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Bash","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): quantity.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest /testbed/astropy/units/tests/test_quantity.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-10097__7LULjUH":{"task_name":"django__django-10097","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Glob","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): validators.py, invalid_urls.txt, valid_urls.txt."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/validators/tests.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-10554__3pXBQFz":{"task_name":"django__django-10554","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 100 steps but only 64 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Grep","Glob"],"answer":"Bash","reason":"\"Bash\" was called 30 times. Full breakdown: Bash (30), Read (19), Grep (12), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): test_qs_combinators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nimport os\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'\n\n# Check if settings exist\nimport importlib\ntry:\n    mod = importlib.import_module('tests.test_sqlite')\n    print('Settings module exists')\nexcept ImportError:\n    print('No test_sqlite settings')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-10880__dMU8PLK":{"task_name":"django__django-10880","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): aggregates.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}}, INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'])\ndjango.setup()\n\nfrom django.db.models import Count, Case, When, Q\nfrom django.contrib.auth.models import User\n\n# Test: Count with distinct=True and Case expression\nqs = User.objects.values('is_active').annotate(\n    cnt=Count(Case(When(is_staff=True, then=1)), distinct=True)\n)\nprint(str(qs.query))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-10914__mpwHvxu":{"task_name":"django__django-10914","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 28 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): global_settings.py, tests.py, test_storage.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/file_storage/tests.py::FileStoragePermissions -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-10973__Cgsw4uJ":{"task_name":"django__django-10973","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (3), Agent (2), Edit (1), Write (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): client.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/dbshell/test_postgresql.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-10999__RDYupiH":{"task_name":"django__django-10999","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Read","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 2 times. Full breakdown: Bash (2), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dateparse.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.utils.dateparse import parse_duration\n# Test negative durations\nprint('Testing negative durations:')\nprint(repr(parse_duration('-1 day, -1:01:01')))   # was failing\nprint(repr(parse_duration('-1:-1:-1')))            # was failing  \nprint(repr(parse_duration('1:-1:-1')))             # was failing\n# Test that normal durations still work\nprint('Testing normal durations:')\nprint(repr(parse_duration('1 day, 1:01:01')))\nprint(repr(parse_duration('1:01:01')))\nprint(repr(parse_duration('100')))\nprint(repr(parse_duration('1 day')))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11066__uNqTdzc":{"task_name":"django__django-11066","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Grep","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (1), Edit (1), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test contenttypes_tests.test_operations --settings=test_sqlite --verbosity=1 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11087__ATaNE6n":{"task_name":"django__django-11087","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 30 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Bash","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (5), Agent (3), Edit (3), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): deletion.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test delete --settings=tests.test_sqlite --verbosity=2 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11095__P76jN4M":{"task_name":"django__django-11095","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Grep","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/modeladmin/tests.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11099__wFBuHK2":{"task_name":"django__django-11099","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Bash","Grep","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Read"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Bash (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): validators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install pytz -q 2>&1 | tail -1 && python -c \"\nimport django, os\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'\ndjango.setup()\nfrom django.contrib.auth.validators import ASCIIUsernameValidator, UnicodeUsernameValidator\nfrom django.core.exceptions import ValidationError\n\nfor Validator, name in [(ASCIIUsernameValidator, 'ASCII'), (UnicodeUsernameValidator, 'Unicode')]:\n    v = Validator()\n    v('validuser')\n    print(f'{name}: normal username OK')\n    try:\n        v('validuser\\n')\n        print(f'{name}: BUG - trailing newline accepted')\n    except ValidationError:\n        print(f'{name}: trailing newline correctly rejected')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11119__aHBAQaH":{"task_name":"django__django-11119","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Write","Read","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Write","Edit"],"answer":"Grep","reason":"\"Grep\" was called 3 times. Full breakdown: Grep (3), Read (2), Bash (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): engine.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.template import Engine\nengine = Engine(autoescape=False)\nresult = engine.render_to_string('django/test/simple_template.html', {'var': '<b>bold</b>'})\nprint(repr(result))\n\" 2>&1 || python -c \"\nfrom django.template import Engine\n# Use from_string instead to avoid needing template files\ne = Engine(autoescape=False)\nt = e.from_string('{{ var }}')\nfrom django.template import Context\nc = Context({'var': '<b>test</b>'}, autoescape=e.autoescape)\nprint('Direct render:', repr(t.render(c)))\n\n# Test render_to_string by writing a temp template\nimport tempfile, os\nd = tempfile.mkdtemp()\nwith open(os.path.join(d, 'test.html'), 'w') as f:\n    f.write('{{ var }}')\ne2 = Engine(dirs=[d], autoescape=False)\nprint('render_to_string:', repr(e2.render_to_string('test.html', {'var': '<b>test</b>'})))\ne3 = Engine(dirs=[d], autoescape=True)\nprint('render_to_string (autoescape=True):', repr(e3.render_to_string('test.html', {'var': '<b>test</b>'})))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11133__gUpWKTV":{"task_name":"django__django-11133","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Bash","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Edit (2), Agent (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): response.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"DJANGO_SETTINGS_MODULE=tests.test_client.settings pip install pytz -q 2>&1 | tail -1 && python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure()\n\nfrom django.http import HttpResponse\n\n# String content\nresponse = HttpResponse('My Content')\nprint('String:', response.content)\n\n# Bytes content\nresponse = HttpResponse(b'My Content')\nprint('Bytes:', response.content)\n\n# memoryview content\nresponse = HttpResponse(memoryview(b'My Content'))\nprint('Memoryview:', response.content)\n\n# memoryview via iterator\nresponse = HttpResponse(iter([memoryview(b'My Content')]))\nprint('Memoryview iter:', response.content)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11138__64tGf2v":{"task_name":"django__django-11138","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 76 steps but only 49 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Read","Edit"],"answer":"Read","reason":"\"Read\" was called 18 times. Full breakdown: Read (18), Bash (14), Edit (8), TodoWrite (4), Agent (2), Grep (1), WebSearch (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): operations.py, operations.py, base.py, operations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test timezones --settings=test_sqlite 2>&1 | head -50\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11141__AA6poJZ":{"task_name":"django__django-11141","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): loader.py, test_loader.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_loader.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11149__nWApaLX":{"task_name":"django__django-11149","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (3), Grep (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.tests.ReadonlyTest -v2 --settings=django.test.utils 2>&1 | head -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11163__j3b4AsN":{"task_name":"django__django-11163","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 8 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Grep","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Read","Grep"],"answer":"Edit","reason":"\"Edit\" was called 2 times. Full breakdown: Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11179__EmHaq5W":{"task_name":"django__django-11179","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 29 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Bash","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (4), Glob (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): deletion.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n)\ndjango.setup()\n\nfrom django.contrib.auth.models import User\nfrom django.db import connection\n\nwith connection.schema_editor() as editor:\n    editor.create_model(User)\n\nu = User.objects.create(username='testuser')\nprint(f'Before delete: pk={u.pk}')\nu.delete()\nprint(f'After delete: pk={u.pk}')\nassert u.pk is None, f'PK should be None after delete, got {u.pk}'\nprint('Test passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11206__QBtN923":{"task_name":"django__django-11206","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): numberformat.py, test_numberformat.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test utils_tests.test_numberformat --settings=django.conf.global_settings -v 2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11211__Uo3dgjM":{"task_name":"django__django-11211","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 30 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Read","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Agent (3), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): fields.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=[\n        'django.contrib.contenttypes',\n        'django.contrib.auth',\n    ],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nimport uuid\nfrom django.db import models, connection\nfrom django.contrib.contenttypes.fields import GenericForeignKey, GenericRelation\nfrom django.contrib.contenttypes.models import ContentType\n\n# Create models\nclass Foo(models.Model):\n    id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n    name = models.CharField(max_length=100)\n    class Meta:\n        app_label = 'contenttypes'\n\nclass Bar(models.Model):\n    foo_content_type = models.ForeignKey(\n        ContentType, on_delete=models.CASCADE, db_index=True\n    )\n    foo_object_id = models.CharField(max_length=255, db_index=True)\n    foo = GenericForeignKey('foo_content_type', 'foo_object_id')\n    class Meta:\n        app_label = 'contenttypes'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_table(ContentType._meta.db_table and ContentType)\n    schema_editor.create_table(Foo)\n    schema_editor.create_table(Bar)\n\n# Create test data\nfoo = Foo.objects.create(name='test_foo')\nct = ContentType.objects.get_for_model(Foo)\nbar = Bar.objects.create(foo_content_type=ct, foo_object_id=str(foo.pk))\n\n# Test prefetch_related\nbars = Bar.objects.prefetch_related('foo')\nfor b in bars:\n    print(f'bar.foo = {b.foo}')\n    print(f'bar.foo is None: {b.foo is None}')\n    print(f'bar.foo.name = {b.foo.name if b.foo else \\\"N/A\\\"}')\n    print(f'bar.foo.pk = {b.foo.pk if b.foo else \\\"N/A\\\"}')\n    print(f'foo.pk = {foo.pk}')\n    print(f'Match: {b.foo.pk == foo.pk if b.foo else False}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11239__aVmKUAM":{"task_name":"django__django-11239","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Edit (3), Read (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): client.py, test_postgresql.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/dbshell/test_postgresql.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11265__h8AL89W":{"task_name":"django__django-11265","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test filtered_relation.tests.FilteredRelationTests.test_with_join --settings=tests.test_sqlite 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11276__Qiciq3z":{"task_name":"django__django-11276","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Edit (4), Read (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): html.py, test_html.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test utils_tests.test_html --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11292__kD7JRzp":{"task_name":"django__django-11292","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Read","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Edit","Write"],"answer":"Bash","reason":"\"Bash\" was called 15 times. Full breakdown: Bash (15), Read (4), Grep (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test user_commands --settings=tests.test_sqlite 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11299__6r9Y69k":{"task_name":"django__django-11299","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Write"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes'],\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import Q\n\n# Create a test model\nclass TestModel(models.Model):\n    field_1 = models.IntegerField(blank=True, null=True)\n    flag = models.BooleanField()\n    class Meta:\n        app_label = 'test'\n\nconstraint = models.CheckConstraint(\n    check=Q(flag__exact=True, field_1__isnull=False) | Q(flag__exact=False),\n    name='field_1_has_value_if_flag_set',\n)\n\nschema_editor = connection.schema_editor()\nsql = constraint._get_check_sql(TestModel, schema_editor)\nprint('Generated SQL:', sql)\n\n# Verify no table-qualified names appear\nassert 'test_testmodel' not in sql, f'Table name should not appear in constraint SQL: {sql}'\nprint('PASS: No table-qualified column names in constraint SQL')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11333__XiTzG9s":{"task_name":"django__django-11333","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Bash","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Grep"],"answer":"Read","reason":"\"Read\" was called 5 times. Full breakdown: Read (5), Bash (5), Grep (4), Edit (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): base.py, resolvers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test urlpatterns_reverse.tests --settings=urlpatterns_reverse.settings -v 0 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11400__nafERWq":{"task_name":"django__django-11400","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Agent","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): filters.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_filters.tests.ListFiltersTests.test_relatedfieldlistfilter_foreignkey_ordering admin_filters.tests.ListFiltersTests.test_relatedfieldlistfilter_foreignkey_ordering_reverse admin_filters.tests.ListFiltersTests.test_relatedonlyfieldlistfilter_foreignkey admin_filters.tests.ListFiltersTests.test_relatedonlyfieldlistfilter_manytomany admin_filters.tests.ListFiltersTests.test_relatedonlyfieldlistfilter_underscorelookup_foreignkey admin_filters.tests.ListFiltersTests.test_relatedfieldlistfilter_foreignkey --settings=admin_filters.tests 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11433__sxmmnhX":{"task_name":"django__django-11433","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Grep","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (5), Agent (2), Grep (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_forms.tests.ModelFormBaseTest.test_default_populated_on_optional_field model_forms.tests.ModelFormBaseTest.test_default_not_populated_on_optional_checkbox_input model_forms.tests.ModelFormBaseTest.test_default_not_populated_on_checkboxselectmultiple model_forms.tests.ModelFormBaseTest.test_default_not_populated_on_selectmultiple model_forms.tests.ModelFormBaseTest.test_prefixed_form_with_default_field model_forms.tests.ModelFormBaseTest.test_default_splitdatetime_field model_forms.tests.ModelFormBaseTest.test_default_filefield model_forms.tests.ModelFormBaseTest.test_default_selectdatewidget --settings=model_forms.tests 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11451__KMbMZ2T":{"task_name":"django__django-11451","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Edit","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): backends.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test auth_tests.test_auth_backends --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11477__75vNFR6":{"task_name":"django__django-11477","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 33 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Bash","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (5), Edit (4), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): base.py, tests.py, default.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\n\n# Minimal settings for testing\nsettings.configure(\n    ROOT_URLCONF='tests.i18n.patterns.urls.default',\n    USE_I18N=True,\n    LANGUAGE_CODE='en',\n    LANGUAGES=[('en', 'English'), ('nl', 'Dutch')],\n)\ndjango.setup()\n\nfrom django.urls import translate_url\nfrom django.utils import translation\n\n# Test with a simple URL pattern that has optional named groups\n# First, let's reproduce the issue with a custom URL pattern\nfrom django.urls import resolve\n\n# Test the existing tests still pass\nwith translation.override('en'):\n    result = translate_url('/en/users/', 'nl')\n    print(f'translate /en/users/ to nl: {result}')\n    assert result == '/nl/gebruikers/', f'Expected /nl/gebruikers/, got {result}'\n    \n    result = translate_url('/en/nonexistent/', 'nl')\n    print(f'translate /en/nonexistent/ to nl: {result}')\n    \nprint('Basic tests pass!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11490__X5sWnUq":{"task_name":"django__django-11490","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (3), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nimport os\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tests.test_sqlite'\ndjango.setup()\n\nfrom django.test.utils import setup_test_environment\nsetup_test_environment()\n\nfrom django.db import connection\nfrom django.test.utils import setup_databases\nsetup_databases(0, False, aliases=['default'])\n\nfrom reserved_names.models import ReservedName\n\nReservedName.objects.create(name='a', order=2)\nqs1 = ReservedName.objects.all()\n\nprint('Test 1 - values_list with name and order:')\nresult = qs1.union(qs1).values_list('name', 'order').get()\nprint(result)\n\nprint('Test 2 - values_list with just order:')\nresult = qs1.union(qs1).values_list('order').get()\nprint(result)\n\nprint('Test 3 - values_list with just name:')\nresult = qs1.union(qs1).values_list('name').get()\nprint(result)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11532__2hvaFkR":{"task_name":"django__django-11532","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Grep","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Edit (4), Read (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): message.py, utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom unittest.mock import patch\nfrom django.core.mail import EmailMessage\nimport django\nfrom django.conf import settings\nsettings.configure(DEFAULT_FROM_EMAIL='from@example.com', DEFAULT_CHARSET='utf-8', EMAIL_USE_LOCALTIME=False)\n\nwith patch('django.core.mail.message.DNS_NAME', new_callable=lambda: type('MockDNS', (), {'get_fqdn_idna': lambda self: 'xn--p8s937b', 'get_fqdn': lambda self: '\u6f22\u5b57', '__str__': lambda self: '\u6f22\u5b57'})):\n    email = EmailMessage('subject', '', 'from@example.com', ['to@example.com'])\n    email.encoding = 'iso-8859-1'\n    message = email.message()\n    msg_id = message['Message-ID']\n    print('Message-ID:', msg_id)\n    assert 'xn--p8s937b' in msg_id, 'Punycode domain not found in Message-ID'\n    print('SUCCESS: punycode domain found in Message-ID')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11551__e9mkLwH":{"task_name":"django__django-11551","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): checks.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_checks.tests.SystemChecksTestCase -v2 --settings=admin_checks.tests 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11555__tVxQopB":{"task_name":"django__django-11555","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 69 steps but only 43 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (13), Grep (5), Edit (4), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import F\nfrom django.db.models.expressions import OrderBy\n\n# Create models with expression-based ordering on parent\nclass Parent(models.Model):\n    name = models.CharField(max_length=100)\n    class Meta:\n        app_label = 'test'\n        ordering = [F('name').asc()]\n\nclass Child(Parent):\n    extra = models.CharField(max_length=100)\n    class Meta:\n        app_label = 'test'\n\n# Create tables\nwith connection.schema_editor() as editor:\n    editor.create_model(Parent)\n    editor.create_model(Child)\n\n# This should trigger the bug - child inherits parent's expression-based ordering\ntry:\n    print(str(Child.objects.all().query))\n    print('SUCCESS: No crash')\nexcept Exception as e:\n    print(f'ERROR: {e}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11603__GJzv5Vy":{"task_name":"django__django-11603","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Edit (4), Read (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): aggregates.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test aggregation_regress.tests.AggregationTests.test_allow_distinct --settings=tests.test_sqlite 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11728__P2T4urK":{"task_name":"django__django-11728","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (5), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.contrib.admindocs.utils import replace_named_groups\n\n# Bug case: trailing named group without '/'\npattern = r'entries/(?P<pk>[^/.]+)/relationships/(?P<related_field>\\w+)'\nresult = replace_named_groups(pattern)\nprint(f'Input:  {pattern}')\nprint(f'Output: {result}')\nassert '<related_field>' in result, f'FAIL: related_field not replaced'\nassert '<pk>' in result, f'FAIL: pk not replaced'\n\n# Workaround case (should still work)\npattern2 = r'entries/(?P<pk>[^/.]+)/relationships/(?P<related_field>\\w+)/'\nresult2 = replace_named_groups(pattern2)\nprint(f'Input:  {pattern2}')\nprint(f'Output: {result2}')\nassert '<related_field>' in result2\nassert '<pk>' in result2\n\n# Single trailing group\npattern3 = r'(?P<pk>\\w+)'\nresult3 = replace_named_groups(pattern3)\nprint(f'Input:  {pattern3}')\nprint(f'Output: {result3}')\nassert result3 == '<pk>', f'FAIL: got {result3}'\n\n# Existing test cases\npattern4 = r'^(?P<a>\\w+)/b/(\\w+)\\$'\nresult4 = replace_named_groups(pattern4)\nprint(f'Input:  {pattern4}')\nprint(f'Output: {result4}')\n\npattern5 = r'^(?P<a>\\w+)/b/(?P<c>\\w+)/\\$'\nresult5 = replace_named_groups(pattern5)\nprint(f'Input:  {pattern5}')\nprint(f'Output: {result5}')\n\n# Nested parens\npattern6 = r'^(?P<a>(x|y))/b'\nresult6 = replace_named_groups(pattern6)\nprint(f'Input:  {pattern6}')\nprint(f'Output: {result6}')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11734__L3HDT2K":{"task_name":"django__django-11734","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import connection, models\nfrom django.db.models import Exists, OuterRef, Q\n\n# Create minimal models similar to the test case\nclass Number(models.Model):\n    class Meta:\n        app_label = 'test_app'\n\nclass Tag(models.Model):\n    category = models.ForeignKey(Number, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test_app'\n\nclass Item(models.Model):\n    tags = models.ManyToManyField(Tag)\n    class Meta:\n        app_label = 'test_app'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Number)\n    schema_editor.create_model(Tag)\n    schema_editor.create_model(Item)\n    # Create the M2M through table\n    schema_editor.create_model(Item.tags.through)\n\n# Test 1: filter() with OuterRef - should work\nprint('Test 1: filter() with OuterRef')\nqs = Number.objects.annotate(\n    foo=Exists(\n        Item.objects.filter(tags__category_id=OuterRef('pk'))\n    )\n).filter(foo=True)\nprint(qs.query)\nprint('OK')\n\n# Test 2: exclude() with OuterRef - was crashing\nprint('\\nTest 2: exclude() with OuterRef')\nqs = Number.objects.annotate(\n    foo=Exists(\n        Item.objects.exclude(tags__category_id=OuterRef('pk'))\n    )\n).filter(foo=True)\nprint(qs.query)\nprint('OK')\n\n# Test 3: filter(~Q()) with OuterRef - was crashing\nprint('\\nTest 3: filter(~Q()) with OuterRef')\nqs = Number.objects.annotate(\n    foo=Exists(\n        Item.objects.filter(~Q(tags__category_id=OuterRef('pk')))\n    )\n).filter(foo=True)\nprint(qs.query)\nprint('OK')\n\nprint('\\nAll tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11740__EXMSNba":{"task_name":"django__django-11740","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Bash","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): autodetector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nimport uuid\nfrom django.db import models\nfrom django.db.migrations.autodetector import MigrationAutodetector\nfrom django.db.migrations.state import ProjectState, ModelState\nfrom django.db.migrations.questioner import MigrationQuestioner\n\n# Before state: App1 has a UUIDField 'another_app'\nbefore = ProjectState()\nbefore.add_model(ModelState('testapp1', 'App1', [\n    ('id', models.UUIDField(primary_key=True, default=uuid.uuid4)),\n    ('another_app', models.UUIDField(null=True, blank=True)),\n]))\nbefore.add_model(ModelState('testapp2', 'App2', [\n    ('id', models.UUIDField(primary_key=True, default=uuid.uuid4)),\n]))\n\n# After state: App1.another_app changed to ForeignKey to App2\nafter = ProjectState()\nafter.add_model(ModelState('testapp1', 'App1', [\n    ('id', models.UUIDField(primary_key=True, default=uuid.uuid4)),\n    ('another_app', models.ForeignKey('testapp2.App2', models.SET_NULL, null=True, blank=True)),\n]))\nafter.add_model(ModelState('testapp2', 'App2', [\n    ('id', models.UUIDField(primary_key=True, default=uuid.uuid4)),\n]))\n\nautodetector = MigrationAutodetector(before, after, MigrationQuestioner(defaults={'ask_merge': True}))\nchanges = autodetector._detect_changes()\n\nprint('Changes for testapp1:')\nfor migration in changes.get('testapp1', []):\n    print(f'  Migration: {migration.name}')\n    print(f'  Dependencies: {migration.dependencies}')\n    for op in migration.operations:\n        print(f'  Operation: {op}')\n\nhas_dep = any(\n    dep[0] == 'testapp2'\n    for migration in changes.get('testapp1', [])\n    for dep in migration.dependencies\n)\nprint(f'\\nHas dependency on testapp2: {has_dep}')\nassert has_dep, 'BUG: No dependency on testapp2!'\nprint('SUCCESS: Fix works correctly.')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11749__ER7nbkj":{"task_name":"django__django-11749","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Agent","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'])\ndjango.setup()\n\nfrom django.core.management.base import BaseCommand\nfrom django.core.management import call_command\n\nclass Command(BaseCommand):\n    def add_arguments(self, parser):\n        shop = parser.add_mutually_exclusive_group(required=True)\n        shop.add_argument('--shop-id', nargs='?', type=int, default=None, dest='shop_id')\n        shop.add_argument('--shop', nargs='?', type=str, default=None, dest='shop_name')\n    \n    def handle(self, *args, **options):\n        print('shop_id:', options.get('shop_id'))\n        print('shop_name:', options.get('shop_name'))\n\n# Register the command\nfrom django.core.management import get_commands\nimport django.core.management\ncmd = Command()\n\n# Test via call_command with kwargs\ncall_command(cmd, shop_id=1)\nprint('---')\ncall_command(cmd, shop_name='test_shop')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11790__jS8FCQc":{"task_name":"django__django-11790","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Agent (1), Read (1), Edit (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test django.contrib.auth.tests.test_forms -v 2 --settings=django.contrib.auth.tests.settings 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11815__tGM3nof":{"task_name":"django__django-11815","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 42 steps but only 29 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Grep","Write"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Grep (5), Read (5), Edit (5)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): serializer.py, test_writer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_writer.WriterTests.test_serialize_enums --settings=tests.test_sqlite 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11820__fvxkSuA":{"task_name":"django__django-11820","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Grep","Bash"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Write","Glob"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n)\ndjango.setup()\n\nfrom django.db import models\n\n# Create test models\nclass Option(models.Model):\n    class Meta:\n        app_label = 'test_app'\n\nclass SomeModel(models.Model):\n    option = models.ForeignKey(Option, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test_app'\n        ordering = ['option__pk']\n\nerrors = SomeModel.check()\nprint('Errors:', errors)\nassert len(errors) == 0, f'Expected no errors but got: {errors}'\nprint('PASS: option__pk in ordering does not raise E015')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11848__9nfP4mg":{"task_name":"django__django-11848","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Write","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): http.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/utils_tests/test_http.py -x -q --tb=short 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11880__U34YFys":{"task_name":"django__django-11880","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Read","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): fields.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11885__kzbBxuh":{"task_name":"django__django-11885","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 4 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["EnterPlanMode","Grep","Glob","Agent"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 4 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Write"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Edit (7), TodoWrite (5), Agent (4), Bash (4), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): deletion.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test delete --settings=tests.test_sqlite -v2 2>&1 | tail -60\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-11951__55tbjsU":{"task_name":"django__django-11951","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Read","Bash"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11964__L38g3q2":{"task_name":"django__django-11964","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (5), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): enums.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models import TextChoices, IntegerChoices\nfrom django.utils.translation import gettext_lazy as _\n\nclass MyChoice(TextChoices):\n    FIRST_CHOICE = 'first', _('The first choice, it is')\n    SECOND_CHOICE = 'second', _('The second choice, it is')\n\nclass MyIntChoice(IntegerChoices):\n    ONE = 1, 'One'\n    TWO = 2, 'Two'\n\n# Test str() returns the value, not the enum representation\nprint('TextChoices str():', str(MyChoice.FIRST_CHOICE))\nprint('TextChoices repr():', repr(MyChoice.FIRST_CHOICE))\nprint('Expected: first')\nassert str(MyChoice.FIRST_CHOICE) == 'first', f'Got: {str(MyChoice.FIRST_CHOICE)}'\n\nprint('IntegerChoices str():', str(MyIntChoice.ONE))\nprint('Expected: 1')\nassert str(MyIntChoice.ONE) == '1', f'Got: {str(MyIntChoice.ONE)}'\n\n# Test isinstance\nassert isinstance(MyChoice.FIRST_CHOICE, str)\nassert isinstance(MyIntChoice.ONE, int)\n\n# Test equality\nassert MyChoice.FIRST_CHOICE == 'first'\nassert MyIntChoice.ONE == 1\n\nprint('All assertions passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-11999__didnjkf":{"task_name":"django__django-11999","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}}, INSTALLED_APPS=['django.contrib.contenttypes'])\ndjango.setup()\n\nfrom django.db import models\n\nclass FooBar(models.Model):\n    foo_bar = models.CharField('foo', choices=[(1, 'foo'), (2, 'bar')])\n    \n    def get_foo_bar_display(self):\n        return 'something'\n    \n    class Meta:\n        app_label = 'test'\n\nobj = FooBar(foo_bar=1)\nresult = obj.get_foo_bar_display()\nprint(f'Result: {result!r}')\nassert result == 'something', f'Expected \\\"something\\\", got {result!r}'\nprint('OK - override works correctly')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12039__2dcUFU6":{"task_name":"django__django-12039","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Edit","Grep"],"answer":"Grep","reason":"\"Grep\" was called 5 times. Full breakdown: Grep (5), Read (4), Edit (4), Bash (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): ddl_references.py, test_ddl_references.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/backends/test_ddl_references.py -v 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12050__whZwiUU":{"task_name":"django__django-12050","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 15 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Grep","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Grep","Write"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models.sql import Query\nfrom django.db.models import Model\n\n# Create a minimal Query instance\nclass FakeModel(Model):\n    class Meta:\n        app_label = 'test'\n\nq = Query(FakeModel)\n\n# Test that list input stays as list\nlist_val = q.resolve_lookup_value([1, 2, 3], None, True, False)\nprint(f'List input: {type(list_val).__name__} -> {list_val}')\nassert isinstance(list_val, list), f'Expected list, got {type(list_val)}'\n\n# Test that tuple input stays as tuple\ntuple_val = q.resolve_lookup_value((1, 2, 3), None, True, False)\nprint(f'Tuple input: {type(tuple_val).__name__} -> {tuple_val}')\nassert isinstance(tuple_val, tuple), f'Expected tuple, got {type(tuple_val)}'\n\nprint('All assertions passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12125__AMhVZDm":{"task_name":"django__django-12125","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (3), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): serializer.py, test_writer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.migrations.serializer import TypeSerializer\n\n# Simulate an inner class\nclass Outer:\n    class Inner:\n        __module__ = 'test1.models'\n\n# Before fix: would produce 'test1.models.Inner'\n# After fix: should produce 'test1.models.Outer.Inner'\ns = TypeSerializer(Outer.Inner)\nresult, imports = s.serialize()\nprint('Result:', result)\nprint('Imports:', imports)\nassert result == 'test1.models.Outer.Inner', f'Expected test1.models.Outer.Inner, got {result}'\nprint('Fix verified!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12143__UFVQpkg":{"task_name":"django__django-12143","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Write","Read"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12155__RKMGE4k":{"task_name":"django__django-12155","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 27 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Agent","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (3), Edit (3), Read (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_docs.test_utils --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12193__jJdJG99":{"task_name":"django__django-12193","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Agent","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): widgets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.contrib.postgres.forms import SplitArrayField\nfrom django.forms import BooleanField\n\n# Create a SplitArrayField with BooleanField and size 5\nfield = SplitArrayField(BooleanField(required=False), size=5)\nwidget = field.widget\n\n# Simulate rendering with mixed True/False values\n# The bug: after the first True, all subsequent widgets would be checked\nvalue = [True, False, True, False, False]\ncontext = widget.get_context('test', value, {})\n\nfor i, subwidget in enumerate(context['widget']['subwidgets']):\n    checked = subwidget.get('attrs', {}).get('checked', False)\n    print(f'Widget {i}: value={value[i]}, checked={checked}, match={checked == value[i]}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12209__RsyhYYV":{"task_name":"django__django-12209","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 91 steps but only 52 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 19 times. Full breakdown: Bash (19), Read (18), Edit (9), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): base.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test basic.tests --settings=tests.test_sqlite -v 2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12262__cgMgvSy":{"task_name":"django__django-12262","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Agent (1), Read (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): library.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.template import Template, Context, Engine\nfrom django.template.library import Library\n\nregister = Library()\n\n@register.simple_tag\ndef hello(*, greeting='hello'):\n    return f'{greeting} world'\n\nengine = Engine(libraries={'test': 'django.template.library'})\nengine.template_libraries['test'] = register\n\n# Test 1: keyword-only arg with default\nt = Template('{% load test %}{% hello greeting=\\\"hi\\\" %}')\nresult = t.render(Context())\nprint(f'Test 1 (kwarg with default): {result!r}')\n\n# Test 2: keyword-only arg using its default\nt = Template('{% load test %}{% hello %}')\nresult = t.render(Context())\nprint(f'Test 2 (kwarg default used): {result!r}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12273__EWHUj75":{"task_name":"django__django-12273","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create models dynamically\nclass Item(models.Model):\n    uid = models.AutoField(primary_key=True, editable=False)\n    f = models.BooleanField(default=False)\n    class Meta:\n        app_label = 'test'\n    def reset(self):\n        self.uid = None\n        self.f = False\n\nclass Derived(Item):\n    class Meta:\n        app_label = 'test'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Item)\n    schema_editor.create_model(Derived)\n\n# Test\nderived = Derived.objects.create(f=True)\nprint(f'Created derived with pk={derived.pk}, f={derived.f}')\n\nitem = Item.objects.get(pk=derived.pk)\nobj1 = item.derived\nprint(f'Fetched obj1 with pk={obj1.pk}, f={obj1.f}')\n\nobj1.reset()\nprint(f'After reset: pk={obj1.pk}, f={obj1.f}')\n\nobj1.save()\nprint(f'After save: pk={obj1.pk}, f={obj1.f}')\n\n# The original object should still have f=True\nobj = Item.objects.get(pk=derived.pk)\nprint(f'Original object: pk={obj.pk}, f={obj.f}')\nassert obj.f == True, f'Expected f=True but got f={obj.f} -- BUG: original was overwritten!'\nprint('SUCCESS: Original object was NOT overwritten. A new object was created.')\nprint(f'Total Item count: {Item.objects.count()}')\nprint(f'Total Derived count: {Derived.objects.count()}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12276__6ytjDfe":{"task_name":"django__django-12276","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (4), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): widgets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/forms_tests/widget_tests/test_fileinput.py tests/forms_tests/widget_tests/test_clearablefileinput.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12304__e6Sfvym":{"task_name":"django__django-12304","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Agent","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (3), Read (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): enums.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install pytz 2>/dev/null | tail -1 && python -c \"\nimport sys; sys.path.insert(0, '/testbed')\nfrom django.db.models.enums import TextChoices, IntegerChoices, Choices\n\nclass YearInSchool(TextChoices):\n    FRESHMAN = 'FR', 'Freshman'\n    SOPHOMORE = 'SO', 'Sophomore'\n\nprint('Choices:', Choices.do_not_call_in_templates)\nprint('TextChoices subclass:', YearInSchool.do_not_call_in_templates)\n\nclass Grade(IntegerChoices):\n    A = 1, 'Grade A'\n\nprint('IntegerChoices subclass:', Grade.do_not_call_in_templates)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12308__fa7BpYg":{"task_name":"django__django-12308","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 29 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Write","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Grep (7), Read (5), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): utils.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_utils.tests.UtilsTests.test_json_display_for_field --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-12325__bST3hNs":{"task_name":"django__django-12325","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models\n\n# Test case 1: parent_link declared FIRST (this was the broken case)\nclass Document(models.Model):\n    class Meta:\n        app_label = 'test'\n\nclass Picking(Document):\n    document_ptr = models.OneToOneField(Document, on_delete=models.CASCADE, parent_link=True, related_name='+')\n    origin = models.OneToOneField(Document, related_name='picking', on_delete=models.PROTECT)\n    class Meta:\n        app_label = 'test'\n\nprint('Test 1 passed: parent_link first, non-parent second')\n\n# Test case 2: parent_link declared SECOND (this already worked)\nclass Document2(models.Model):\n    class Meta:\n        app_label = 'test'\n\nclass Picking2(Document2):\n    origin = models.OneToOneField(Document2, related_name='picking2', on_delete=models.PROTECT)\n    document2_ptr = models.OneToOneField(Document2, on_delete=models.CASCADE, parent_link=True, related_name='+')\n    class Meta:\n        app_label = 'test'\n\nprint('Test 2 passed: non-parent first, parent_link second')\n\n# Verify the correct field is used as parent link\nassert Picking._meta.parents[Document] == Picking._meta.get_field('document_ptr')\nassert Picking2._meta.parents[Document2] == Picking2._meta.get_field('document2_ptr')\nprint('All assertions passed: correct parent link fields selected')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-12406__Em9Shim":{"task_name":"django__django-12406","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Edit","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Grep (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.forms import ModelChoiceField, RadioSelect, Select\nfrom django.contrib.auth.models import User\n\n# Test 1: RadioSelect + required=True (default) -> no blank choice\nfield = ModelChoiceField(queryset=User.objects.none(), widget=RadioSelect)\nprint('Test 1 - RadioSelect, required=True:')\nprint(f'  empty_label = {field.empty_label!r}')\nassert field.empty_label is None, 'FAIL: should be None'\n\n# Test 2: Select + required=True -> blank choice should still be there\nfield2 = ModelChoiceField(queryset=User.objects.none(), widget=Select)\nprint('Test 2 - Select, required=True:')\nprint(f'  empty_label = {field2.empty_label!r}')\nassert field2.empty_label == '---------', 'FAIL: should be ---------'\n\n# Test 3: RadioSelect + required=False -> blank choice should be there\nfield3 = ModelChoiceField(queryset=User.objects.none(), widget=RadioSelect, required=False)\nprint('Test 3 - RadioSelect, required=False:')\nprint(f'  empty_label = {field3.empty_label!r}')\nassert field3.empty_label == '---------', 'FAIL: should be ---------'\n\n# Test 4: RadioSelect + required=True + explicit empty_label -> should still suppress\nfield4 = ModelChoiceField(queryset=User.objects.none(), widget=RadioSelect, empty_label='Choose one')\nprint('Test 4 - RadioSelect, required=True, explicit empty_label:')\nprint(f'  empty_label = {field4.empty_label!r}')\nassert field4.empty_label is None, 'FAIL: should be None'\n\n# Test 5: No widget specified (default Select) + required=True -> blank choice\nfield5 = ModelChoiceField(queryset=User.objects.none())\nprint('Test 5 - default widget, required=True:')\nprint(f'  empty_label = {field5.empty_label!r}')\nassert field5.empty_label == '---------', 'FAIL: should be ---------'\n\nprint()\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-12419__hcrsPCG":{"task_name":"django__django-12419","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 33 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (9), Grep (6), Edit (6), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 5 file(s): global_settings.py, base.py, checks.txt, settings.txt, test_security.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/check_framework/test_security.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12663__gg9JXmL":{"task_name":"django__django-12663","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 154 steps but only 104 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 38 times. Full breakdown: Bash (38), Read (33), Grep (21), Edit (10), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): lookups.py, query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.auth', 'django.contrib.contenttypes'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create simple models inline\nfrom django.contrib.auth.models import User\n\nclass A(models.Model):\n    class Meta:\n        app_label = 'test_app'\n\nclass B(models.Model):\n    a = models.ForeignKey(A, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test_app'\n\nclass C(models.Model):\n    owner = models.ForeignKey(User, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test_app'\n\n# Create tables\nwith connection.schema_editor() as editor:\n    editor.create_model(A)\n    editor.create_model(B)\n    editor.create_model(C)\n\nfrom django.db.models import OuterRef, Subquery\nfrom django.utils.functional import SimpleLazyObject\n\n# Test without SimpleLazyObject first\nowner_user = (\n    B.objects.filter(a=OuterRef('pk'))\n    .annotate(owner_user=Subquery(C.objects.values('owner')))\n    .values('owner_user')\n)\n\nuser = User.objects.create_user('testuser')\ntry:\n    qs = A.objects.annotate(owner_user=Subquery(owner_user)).filter(owner_user=user)\n    print('Without SimpleLazyObject: OK')\n    print('SQL:', str(qs.query))\nexcept Exception as e:\n    print(f'Without SimpleLazyObject: FAILED - {type(e).__name__}: {e}')\n\n# Test with SimpleLazyObject\nuser_lazy = SimpleLazyObject(lambda: User.objects.create_user('testuser2'))\ntry:\n    qs = A.objects.annotate(owner_user=Subquery(owner_user)).filter(owner_user=user_lazy)\n    print('With SimpleLazyObject: OK')\nexcept Exception as e:\n    print(f'With SimpleLazyObject: FAILED - {type(e).__name__}: {e}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12708__t5NiuvV":{"task_name":"django__django-12708","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Bash","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Agent"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (6), Grep (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): schema.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test schema.tests.SchemaTests.test_remove_index_together_does_not_remove_unique_together --settings=test_sqlite -v2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12713__R57eKVQ":{"task_name":"django__django-12713","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_widgets.tests --settings=tests.test_sqlite -v 2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12741__Laf8AcB":{"task_name":"django__django-12741","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Grep","Read"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Edit (4), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): flush.py, operations.py, test_operations.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12754__PXSQWfP":{"task_name":"django__django-12754","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 66 steps but only 41 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Read","Edit"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (12), Edit (5), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): autodetector.py, test_autodetector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_autodetector.py::AutodetectorTests::test_field_moved_to_subclass -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12774__Qf5J44n":{"task_name":"django__django-12774","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Read","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (5), Edit (4), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): query.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test lookup.tests.LookupTests.test_in_bulk_non_unique_field lookup.tests.LookupTests.test_in_bulk_with_field lookup.tests.LookupTests.test_in_bulk --settings=test_sqlite -v2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12858__Htufof5":{"task_name":"django__django-12858","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Write","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models\nfrom django.core import checks\n\n# Reproduce the bug scenario: ordering with __isnull on a related field\nclass Product(models.Model):\n    parent = models.ForeignKey('self', models.CASCADE, null=True)\n    class Meta:\n        app_label = 'test'\n\nclass Supply(models.Model):\n    product = models.ForeignKey(Product, models.CASCADE)\n    class Meta:\n        app_label = 'test'\n\nclass Stock(models.Model):\n    supply = models.ForeignKey(Supply, models.CASCADE)\n    class Meta:\n        app_label = 'test'\n        ordering = ['supply__product__parent__isnull']\n\nerrors = Stock.check()\nif errors:\n    print('FAIL: Still getting errors:', errors)\nelse:\n    print('PASS: No errors for supply__product__parent__isnull')\n\n# Also verify that truly invalid lookups still produce E015\nclass BadStock(models.Model):\n    supply = models.ForeignKey(Supply, models.CASCADE)\n    class Meta:\n        app_label = 'test'\n        ordering = ['supply__product__parent__nonexistent']\n\nerrors = BadStock.check()\nif errors:\n    print('PASS: Correctly got error for invalid lookup:', errors[0].id)\nelse:\n    print('FAIL: Should have gotten an error for nonexistent lookup')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-12965__8MvgYCj":{"task_name":"django__django-12965","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.contrib.auth.models import User\nfrom django.db import connection\n\n# Test 1: Model.objects.all().delete() should not use subquery\nqs = User.objects.all()\nquery = qs.query.clone()\nfrom django.db.models.sql import DeleteQuery\nquery.__class__ = DeleteQuery\ncompiler = query.get_compiler('default')\nsql, params = compiler.as_sql()\nprint('all().delete SQL:', sql)\nassert 'IN (SELECT' not in sql, 'FAIL: subquery found in all().delete()'\n\n# Test 2: Filtered delete should still work correctly\nqs2 = User.objects.filter(pk__in=[1, 2, 3])\nquery2 = qs2.query.clone()\nquery2.__class__ = DeleteQuery\ncompiler2 = query2.get_compiler('default')\nsql2, params2 = compiler2.as_sql()\nprint('filter().delete SQL:', sql2)\nassert 'WHERE' in sql2, 'FAIL: WHERE clause missing in filtered delete'\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13012__Y5hg7zC":{"task_name":"django__django-13012","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models import Value, IntegerField, Sum\nfrom django.db.models.expressions import ExpressionWrapper\n\n# Test that ExpressionWrapper delegates get_group_by_cols to its inner expression\nval = Value(3, output_field=IntegerField())\nwrapper = ExpressionWrapper(val, output_field=IntegerField())\n\n# Value should return [] for get_group_by_cols\nprint('Value.get_group_by_cols():', val.get_group_by_cols())\n# ExpressionWrapper should now also return [] when wrapping a Value\nprint('ExpressionWrapper.get_group_by_cols():', wrapper.get_group_by_cols())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13023__xtAPWN5":{"task_name":"django__django-13023","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Edit","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13028__JF3tYVz":{"task_name":"django__django-13028","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Grep (4), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models.sql.query import Query\nfrom django.db.models import Model, CharField, BooleanField\n\n# Simulate the scenario: an object with filterable=False that is NOT an expression\nclass FakeModelInstance:\n    filterable = False\n\nq = Query(None)\n# This should NOT raise NotSupportedError\ntry:\n    q.check_filterable(FakeModelInstance())\n    print('PASS: Non-expression with filterable=False is allowed')\nexcept Exception as e:\n    print(f'FAIL: {e}')\n\n# Test that Window (an actual expression with filterable=False) is still blocked\nfrom django.db.models.expressions import Window\nfrom django.db.models import F\ntry:\n    w = Window(expression=F('price'))\n    q.check_filterable(w)\n    print('FAIL: Window should be disallowed')\nexcept Exception as e:\n    print(f'PASS: Window is correctly disallowed: {e}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13033__n9iidYR":{"task_name":"django__django-13033","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Edit","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create models dynamically\nclass OneModel(models.Model):\n    class Meta:\n        app_label = 'test'\n        ordering = ('-id',)\n    root = models.ForeignKey('self', on_delete=models.CASCADE, null=True)\n    oneval = models.BigIntegerField(null=True)\n\nclass TwoModel(models.Model):\n    class Meta:\n        app_label = 'test'\n    record = models.ForeignKey(OneModel, on_delete=models.CASCADE)\n    twoval = models.BigIntegerField(null=True)\n\n# Test the queryset SQL\nqs = TwoModel.objects.filter(record__oneval__in=[1,2,3])\nqs = qs.order_by('record__root_id')\nsql = str(qs.query)\nprint('order_by(\\\"record__root_id\\\"):')\nprint(sql)\nprint()\n\n# Check for the bug symptoms\nassert 'ASC' in sql, 'FAIL: Should have ASC ordering'\nassert 'DESC' not in sql, 'FAIL: Should NOT have DESC ordering'\nassert 'LEFT OUTER JOIN' not in sql and 'T3' not in sql, 'FAIL: Should NOT have extra JOIN'\nprint('PASS: Correct ASC ordering with single JOIN')\nprint()\n\n# Also test descending\nqs2 = TwoModel.objects.filter(record__oneval__in=[1,2,3])\nqs2 = qs2.order_by('-record__root_id')\nsql2 = str(qs2.query)\nprint('order_by(\\\"-record__root_id\\\"):')\nprint(sql2)\nprint()\nassert 'DESC' in sql2, 'FAIL: Should have DESC ordering'\nassert 'ASC' not in sql2, 'FAIL: Should NOT have ASC ordering'\nassert 'LEFT OUTER JOIN' not in sql2 and 'T3' not in sql2, 'FAIL: Should NOT have extra JOIN'\nprint('PASS: Correct DESC ordering with single JOIN')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13089__s8XjXwK":{"task_name":"django__django-13089","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Edit","Bash"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Grep","Read"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): db.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13109__RBKBTxs":{"task_name":"django__django-13109","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Grep","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Edit (5), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): related.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test validation.tests.BaseModelValidationTests.test_FK_validates_using_base_manager --settings=validation.settings 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13112__JjtM7bW":{"task_name":"django__django-13112","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): related.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    INSTALLED_APPS=[\n        'django.contrib.contenttypes',\n        'django.contrib.auth',\n    ],\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n)\ndjango.setup()\n\nfrom django.db.models.fields.related import ForeignObject, ForeignKey\nfrom django.db import models\n\n# Test that deconstruct preserves app_label case for string references\nclass MockRemoteField:\n    model = 'DJ_RegLogin.Category'\n    on_delete = models.CASCADE\n    parent_link = False\n\nclass MockField(ForeignObject):\n    pass\n\n# Simulate what deconstruct does with the string\nmodel_str = 'DJ_RegLogin.Category'\n\n# Old behavior\nprint('Old (buggy):', model_str.lower())\n\n# New behavior\nif '.' in model_str:\n    app_label, model_name = model_str.split('.', 1)\n    result = '%s.%s' % (app_label, model_name.lower())\nelse:\n    result = model_str.lower()\nprint('New (fixed):', result)\n\n# Also test without app label\nmodel_str2 = 'Category'\nif '.' in model_str2:\n    app_label, model_name = model_str2.split('.', 1)\n    result2 = '%s.%s' % (app_label, model_name.lower())\nelse:\n    result2 = model_str2.lower()\nprint('No app label:', result2)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13121__LVkgqDM":{"task_name":"django__django-13121","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (4), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/expressions/tests.py -x -q -k \"duration\" 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13128__hB7F8GF":{"task_name":"django__django-13128","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nimport datetime\nfrom django.db.models import F, Value, DurationField\nfrom django.db.models.expressions import CombinedExpression\n\n# Simulate the reported scenario: F('end') - F('start') where both are DateTimeField\nfrom django.db.models.fields import DateTimeField\n\nlhs = Value(datetime.datetime.now(), output_field=DateTimeField())\nrhs = Value(datetime.datetime.now(), output_field=DateTimeField())\n\nexpr = lhs - rhs\nprint('Type of expr:', type(expr))\nprint('Output field:', expr.output_field)\nprint('Output field type:', type(expr.output_field))\n\n# Test datetime - datetime + timedelta\nexpr2 = lhs - rhs + Value(datetime.timedelta(), output_field=DurationField())\nprint('Combined output field:', expr2.output_field)\nprint('Combined output field type:', type(expr2.output_field))\n\n# Test datetime + duration\nexpr3 = lhs + Value(datetime.timedelta(days=1), output_field=DurationField())\nprint('DateTime + Duration output field:', expr3.output_field)\nprint('DateTime + Duration output field type:', type(expr3.output_field))\n\n# Test duration + datetime\nexpr4 = Value(datetime.timedelta(days=1), output_field=DurationField()) + lhs\nprint('Duration + DateTime output field:', expr4.output_field)\nprint('Duration + DateTime output field type:', type(expr4.output_field))\n\n# Test datetime - duration\nexpr5 = lhs - Value(datetime.timedelta(days=1), output_field=DurationField())\nprint('DateTime - Duration output field:', expr5.output_field)\nprint('DateTime - Duration output field type:', type(expr5.output_field))\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13158__YigkhLb":{"task_name":"django__django-13158","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Edit","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.contrib.auth.models import User\nfrom django.db import connection\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    from django.contrib.auth.models import User, Group, Permission\n    from django.contrib.contenttypes.models import ContentType\n    for model in [ContentType, Permission, Group, User]:\n        try:\n            schema_editor.create_model(model)\n        except:\n            pass\n\n# Create some test data\nUser.objects.create_user('user1', 'u1@test.com', 'pass')\nUser.objects.create_user('user2', 'u2@test.com', 'pass')\nUser.objects.create_user('user3', 'u3@test.com', 'pass')\n\n# Test: .none() on a union query should return empty\nqs1 = User.objects.filter(username='user1')\nqs2 = User.objects.filter(username='user2')\ncombined = qs1.union(qs2)\nprint('Combined count:', combined.count())\nprint('Combined.none() count:', combined.none().count())\nprint('Is EmptyQuerySet:', isinstance(combined.none(), type(User.objects.none())))\n\n# Test: .none() on intersection\nqs_all = User.objects.all()\nintersected = qs1.intersection(qs_all)\nprint('Intersection.none() count:', intersected.none().count())\n\n# Test regular .none() still works\nprint('Regular .none() count:', User.objects.none().count())\nprint('Regular count:', User.objects.all().count())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13195__CPMyFSc":{"task_name":"django__django-13195","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 47 steps but only 29 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (8), Edit (8), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 6 file(s): cookie.py, middleware.py, response.py, request-response.txt, test_cookie.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/responses/test_cookie.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13212__fdGXNgx":{"task_name":"django__django-13212","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Edit (7), Read (1), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): validators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/validators/ -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13279__a8AKHTn":{"task_name":"django__django-13279","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 31 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (6), Grep (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/sessions_tests/tests.py -x -q 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13297__iUmFwbu":{"task_name":"django__django-13297","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/generic_views/ -x -q 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13315__RJB8qio":{"task_name":"django__django-13315","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Read","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (6), Edit (6), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): models.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_forms.tests.LimitChoicesToDuplicateTests --settings=test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13343__u4kPW8D":{"task_name":"django__django-13343","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Edit (2), Agent (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): files.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models.fields.files import FileField\nfrom django.core.files.storage import FileSystemStorage, default_storage\n\n# Test 1: callable storage should deconstruct to the callable\ndef my_storage():\n    return FileSystemStorage()\n\nf = FileField(storage=my_storage)\nname, path, args, kwargs = f.deconstruct()\nassert kwargs['storage'] is my_storage, 'callable storage should deconstruct to the callable itself'\nprint('Test 1 passed: callable storage deconstructs to the callable')\n\n# Test 2: non-callable storage should work as before\nfs = FileSystemStorage()\nf2 = FileField(storage=fs)\nname, path, args, kwargs = f2.deconstruct()\nassert kwargs['storage'] is fs, 'non-callable storage should deconstruct to the instance'\nprint('Test 2 passed: non-callable storage deconstructs to the instance')\n\n# Test 3: default storage should not appear in kwargs\nf3 = FileField()\nname, path, args, kwargs = f3.deconstruct()\nassert 'storage' not in kwargs, 'default storage should not appear in kwargs'\nprint('Test 3 passed: default storage not in kwargs')\n\n# Test 4: lambda callable\nf4 = FileField(storage=lambda: FileSystemStorage())\nname, path, args, kwargs = f4.deconstruct()\nassert callable(kwargs['storage']), 'lambda storage should deconstruct to the callable'\nprint('Test 4 passed: lambda callable storage deconstructs correctly')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13344__ENTa6Az":{"task_name":"django__django-13344","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 52 steps but only 33 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Grep","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Read (9), Edit (4), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): base.py, middleware.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test middleware_exceptions.tests.MiddlewareSyncAsyncTests.test_mixin_subclass_without_async_check --settings=middleware_exceptions.tests --no-input -v2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13346__8FTzBZA":{"task_name":"django__django-13346","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Glob","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (5), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): json.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_fields.test_jsonfield --settings=tests.test_sqlite -v 2 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13363__PBL9rew":{"task_name":"django__django-13363","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Bash","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): datetime.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test db_functions.datetime.test_extract_trunc --settings=tests.test_sqlite -v 0 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13401__38DYMgJ":{"task_name":"django__django-13401","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Grep","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}}, INSTALLED_APPS=['django.contrib.contenttypes'])\ndjango.setup()\n\nfrom django.db import models\n\nclass A(models.Model):\n    class Meta:\n        abstract = True\n    myfield = models.IntegerField()\n\nclass B(A):\n    class Meta:\n        app_label = 'test'\n\nclass C(A):\n    class Meta:\n        app_label = 'test'\n\nb_field = B._meta.get_field('myfield')\nc_field = C._meta.get_field('myfield')\n\nprint('B field == C field:', b_field == c_field)\nprint('Set length:', len({b_field, c_field}))\nprint('B field < C field:', b_field < c_field)\nprint('C field < B field:', c_field < b_field)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13406__MQKHg2a":{"task_name":"django__django-13406","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Grep","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import connection, models\n\n# Create a model dynamically\nclass Toy(models.Model):\n    name = models.CharField(max_length=16)\n    material = models.CharField(max_length=16)\n    price = models.PositiveIntegerField()\n    class Meta:\n        app_label = 'test'\n\nwith connection.schema_editor() as editor:\n    editor.create_model(Toy)\n\nToy.objects.create(name='foo', price=10, material='wood')\nToy.objects.create(name='bar', price=20, material='plastic')\nToy.objects.create(name='baz', price=100, material='wood')\n\nimport pickle\nfrom django.db.models import Sum\n\nprices = Toy.objects.values('material').annotate(total_price=Sum('price'))\nprint('Original:', prices)\nprint('Type of first element:', type(prices[0]))\n\nprices2 = Toy.objects.all()\nprices2.query = pickle.loads(pickle.dumps(prices.query))\nprint('Type of first element after pickle:', type(prices2[0]))\nprint('After pickle:', prices2)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13410__ejwTggK":{"task_name":"django__django-13410","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Agent","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Grep","Bash"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): locks.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13417__jsrAGJw":{"task_name":"django__django-13417","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (3), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import Count\n\n# Create a test model with Meta.ordering\nclass Foo(models.Model):\n    name = models.CharField(max_length=100)\n    class Meta:\n        app_label = 'test'\n        ordering = ['name']\n\n# Create the table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Foo)\n\nqs = Foo.objects.all()\nprint('qs.ordered:', qs.ordered)\nprint('qs SQL:', str(qs.query))\nprint()\n\nqs2 = Foo.objects.annotate(Count('pk')).all()\nprint('qs2.ordered:', qs2.ordered)\nprint('qs2 SQL:', str(qs2.query))\nprint()\n\n# Explicit order_by should still work with annotate\nqs3 = Foo.objects.annotate(Count('pk')).order_by('name')\nprint('qs3.ordered:', qs3.ordered)\nprint('qs3 SQL:', str(qs3.query))\nprint()\n\n# Clear ordering explicitly\nqs4 = Foo.objects.order_by()\nprint('qs4.ordered:', qs4.ordered)\nprint('qs4 SQL:', str(qs4.query))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13449__oZfRPSB":{"task_name":"django__django-13449","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (4), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import F\nfrom django.db.models.functions import Lag\nfrom django.db.models import Window\n\n# Create a test model dynamically\nfrom django.db import models\n\nclass LagTest(models.Model):\n    modified = models.DateField()\n    data = models.FloatField()\n    amount = models.DecimalField(decimal_places=4, max_digits=7)\n    class Meta:\n        app_label = 'test'\n\n# Create the table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(LagTest)\n\n# Test the query\nw = Window(expression=Lag('amount', 7), partition_by=[F('modified')], order_by=F('modified').asc())\nq = LagTest.objects.all().annotate(w=w)\nsql = str(q.query)\nprint('Generated SQL:')\nprint(sql)\n\n# Actually execute it to make sure it doesn't crash\nprint()\nprint('Executing query...')\nresult = list(q)\nprint('Success! No OperationalError.')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13512__jSSJ2XZ":{"task_name":"django__django-13512","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): utils.py, fields.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.forms.fields import JSONField\nf = JSONField()\n# Test Chinese characters\nresult = f.prepare_value({'name': '\u4e2d\u56fd'})\nprint('prepare_value:', repr(result))\nassert '\u4e2d\u56fd' in result, 'Chinese chars should be displayed directly'\n\n# Test that invalid JSON input is still handled\nfrom django.forms.fields import InvalidJSONInput\nresult2 = f.prepare_value(InvalidJSONInput('{bad}'))\nprint('invalid input:', repr(result2))\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13513__PAorFX9":{"task_name":"django__django-13513","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (3), Grep (3), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): debug.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/view_tests/tests/test_debug.py::ExceptionReporterTests::test_suppressed_context -xvs 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13516__QniCsjy":{"task_name":"django__django-13516","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Grep (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.core.management.base import OutputWrapper\nimport io\n\n# Test with a StringIO that has flush\nbuf = io.StringIO()\nwrapper = OutputWrapper(buf)\nwrapper.write('test', ending='')\nwrapper.flush()  # Should not raise\nprint('flush() on StringIO works:', repr(buf.getvalue()))\n\n# Test with an object that doesn't have flush\nclass NoFlush:\n    def write(self, msg): pass\n    def isatty(self): return False\n\nwrapper2 = OutputWrapper(NoFlush())\nwrapper2.flush()  # Should not raise\nprint('flush() on object without flush works')\n\n# Test with sys.stdout\nimport sys\nwrapper3 = OutputWrapper(sys.stdout)\nwrapper3.write('hello from wrapper', ending='\\n')\nwrapper3.flush()\nprint('flush() on sys.stdout works')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13551__A5UmHy5":{"task_name":"django__django-13551","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Write"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Bash (3), Grep (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): tokens.py, test_tokens.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/auth_tests/test_tokens.py -x -q 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13568__4XJnb58":{"task_name":"django__django-13568","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Glob","Edit"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (5), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): checks.py, test_checks.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/auth_tests/test_checks.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13569__EwKTHwE":{"task_name":"django__django-13569","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): math.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import connection, models\nfrom django.db.models import Count\n\n# Create models dynamically\nclass Thing(models.Model):\n    class Meta:\n        app_label = 'test'\n\nclass Related(models.Model):\n    thing = models.ForeignKey(Thing, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test'\n\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Thing)\n    schema_editor.create_model(Related)\n\nt = Thing.objects.create()\nRelated.objects.create(thing=t)\nRelated.objects.create(thing=t)\n\n# Test the problematic query\nqs = Thing.objects.annotate(rc=Count('related')).order_by('?').values('id', 'rc')\nprint('Query:', qs.query)\nprint('Result:', list(qs))\n\n# Verify expected behavior: should have one row with rc=2\nresult = list(qs)\nassert len(result) == 1, f'Expected 1 row, got {len(result)}'\nassert result[0]['rc'] == 2, f'Expected rc=2, got {result[0][\\\"rc\\\"]}'\nprint('PASS: aggregation works correctly with order_by(\\\"?\\\")')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13590__XNQnj3U":{"task_name":"django__django-13590","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Bash","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Glob","Grep"],"answer":"Grep","reason":"\"Grep\" was called 1 times. Full breakdown: Grep (1), Read (1), Edit (1), Bash (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13658__5eGC3Zg":{"task_name":"django__django-13658","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 7 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Bash","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Grep","Bash"],"answer":"Edit","reason":"\"Edit\" was called 2 times. Full breakdown: Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13670__FjMKLvS":{"task_name":"django__django-13670","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Bash","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dateformat.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13741__SrgzFeS":{"task_name":"django__django-13741","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Write","Agent"],"answer":"Read","reason":"\"Read\" was called 5 times. Full breakdown: Read (5), Bash (3), Grep (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/auth_tests/test_forms.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13786__zXmxcvD":{"task_name":"django__django-13786","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (3), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_optimizer.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13794__RH8ayMM":{"task_name":"django__django-13794","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 42 steps but only 28 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Glob","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Edit (6), Read (4), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): defaultfilters.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.template.defaultfilters import add\nfrom django.utils.translation import gettext_lazy\n\n# Test: str + lazy string\nlazy = gettext_lazy('world')\nresult = add('hello ', lazy)\nprint(f'str + lazy: \\\"{result}\\\"')\n\n# Test: lazy string + str\nresult2 = add(lazy, ' hello')\nprint(f'lazy + str: \\\"{result2}\\\"')\n\n# Test: lazy + lazy\nlazy2 = gettext_lazy(' hello')\nresult3 = add(lazy, lazy2)\nprint(f'lazy + lazy: \\\"{result3}\\\"')\n\n# Test: int addition still works\nresult4 = add(1, 2)\nprint(f'int + int: {result4}')\n\n# Test: str + str still works\nresult5 = add('hello', ' world')\nprint(f'str + str: \\\"{result5}\\\"')\n\n# Test: numeric strings still add as ints\nresult6 = add('1', '2')\nprint(f'numeric str + numeric str: {result6}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-13807__PpGyyhP":{"task_name":"django__django-13807","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Write","Bash"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13809__ySth8dD":{"task_name":"django__django-13809","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): runserver.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_scripts.tests.ManageRunserver --settings=test_sqlite -v 0 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13810__8kh9FSj":{"task_name":"django__django-13810","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Bash","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (5), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): base.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test middleware_exceptions --settings=test_sqlite -v2 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13820__oKiXXyg":{"task_name":"django__django-13820","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Read","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (4), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): loader.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_loader --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13821__YLrKHJL":{"task_name":"django__django-13821","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Read","Agent"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Bash (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): base.py, databases.txt, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/backends/sqlite/tests.py::Tests::test_check_sqlite_version -x 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13837__RbKWkYB":{"task_name":"django__django-13837","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (5), Edit (4), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): autoreload.py, test_autoreload.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/utils_tests/test_autoreload.py::TestChildArguments -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13925__PUKN65c":{"task_name":"django__django-13925","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 28 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (8), Grep (3), Agent (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): base.py, test_model_checks.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/check_framework/test_model_checks.py::ModelDefaultAutoFieldTests -x -v 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13933__jEok4Ti":{"task_name":"django__django-13933","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 35 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Read","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Read"],"answer":"Read","reason":"\"Read\" was called 11 times. Full breakdown: Read (11), Edit (11), Bash (8), Grep (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 5 file(s): models.py, tests.py, test_modelchoicefield.py, tests.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_forms.test_modelchoicefield --settings=tests.test_sqlite --no-input 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-13964__AXT8eq4":{"task_name":"django__django-13964","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Grep (6), Read (5), Agent (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): related_descriptors.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create test models\nclass Product(models.Model):\n    sku = models.CharField(primary_key=True, max_length=50)\n    class Meta:\n        app_label = 'test'\n\nclass Order(models.Model):\n    product = models.ForeignKey(Product, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Product)\n    schema_editor.create_model(Order)\n\n# Test 1: The bug scenario - setting PK after FK assignment\norder = Order()\norder.product = Product()\norder.product.sku = 'foo'\norder.product.save()\norder.save()\n\nassert order.product_id == 'foo', f'Expected product_id=foo, got {order.product_id!r}'\nassert Order.objects.filter(product_id='foo').exists(), 'Order with product_id=foo should exist'\nassert not Order.objects.filter(product_id='').exists(), 'Order with product_id=\\\"\\\" should NOT exist'\nprint('Test 1 PASSED: Setting PK after FK assignment works correctly')\n\n# Test 2: Setting PK before FK assignment (should still work)\norder2 = Order()\norder2.product = Product(sku='bar')\norder2.product.save()\norder2.save()\nassert order2.product_id == 'bar', f'Expected product_id=bar, got {order2.product_id!r}'\nprint('Test 2 PASSED: Setting PK before FK assignment works correctly')\n\n# Test 3: Auto PK (regression check)\nclass Item(models.Model):\n    class Meta:\n        app_label = 'test'\n\nclass ItemOrder(models.Model):\n    item = models.ForeignKey(Item, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test'\n\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Item)\n    schema_editor.create_model(ItemOrder)\n\nitem = Item()\nitem.save()\nio = ItemOrder()\nio.item = item\nio.save()\nassert io.item_id == item.pk, f'Expected item_id={item.pk}, got {io.item_id!r}'\nprint('Test 3 PASSED: Auto PK FK assignment works correctly')\n\n# Test 4: Setting FK to None\norder3 = Order()\norder3.product = Product(sku='baz')\norder3.product.save()\norder3.save()\norder3.product = None  # This should not error  \n# Can't save with null FK since it's not nullable, but the assignment itself should work\nprint('Test 4 PASSED: Setting FK to None works correctly')\n\nprint()\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14007__8Pk4evu":{"task_name":"django__django-14007","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (8), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\nclass MyIntWrapper(int):\n    def __repr__(self):\n        return '<MyIntWrapper: %s>' % super().__repr__()\n\nclass MyAutoField(models.BigAutoField):\n    def from_db_value(self, value, expression, connection):\n        if value is None:\n            return None\n        return MyIntWrapper(value)\n    def get_prep_value(self, value):\n        if value is None:\n            return None\n        return int(value)\n\nclass AutoModel(models.Model):\n    id = MyAutoField(primary_key=True)\n    class Meta:\n        app_label = 'test'\n\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(AutoModel)\n\n# Test create\nam = AutoModel.objects.create()\nprint('create() id:', repr(am.id))\nassert isinstance(am.id, MyIntWrapper), f'Expected MyIntWrapper, got {type(am.id)}'\n\n# Test query\nam_q = AutoModel.objects.first()\nprint('query id:', repr(am_q.id))\nassert isinstance(am_q.id, MyIntWrapper), f'Expected MyIntWrapper, got {type(am_q.id)}'\n\n# Test bulk_create\nams = [AutoModel()]\nAutoModel.objects.bulk_create(ams)\nprint('bulk_create id:', repr(ams[0].id))\nassert isinstance(ams[0].id, MyIntWrapper), f'Expected MyIntWrapper, got {type(ams[0].id)}'\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14011__jy7hnJE":{"task_name":"django__django-14011","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Read","Agent"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (5), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): basehttp.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test servers.tests --settings=django.conf.global_settings 2>&1 | head -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14017__HjafbRV":{"task_name":"django__django-14017","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 67 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Read","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Read","Edit"],"answer":"Edit","reason":"\"Edit\" was called 14 times. Full breakdown: Edit (14), Bash (12), Read (7), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query_utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models import Q, Exists\nfrom django.db.models.expressions import RawSQL\n\n# Test Q() & Exists(...)\nprint('Q() & Exists(RawSQL(\\\"1\\\", [])):', Q() & Exists(RawSQL('1', [])))\nprint('Exists(RawSQL(\\\"1\\\", [])) & Q():', Exists(RawSQL('1', [])) & Q())\nprint('Q() | Exists(RawSQL(\\\"1\\\", [])):', Q() | Exists(RawSQL('1', [])))\nprint('Exists(RawSQL(\\\"1\\\", [])) | Q():', Exists(RawSQL('1', [])) | Q())\n\n# Test with non-empty Q\nprint('Q(x=1) & Exists(RawSQL(\\\"1\\\", [])):', Q(x=1) & Exists(RawSQL('1', [])))\nprint('Q(x=1) | Exists(RawSQL(\\\"1\\\", [])):', Q(x=1) | Exists(RawSQL('1', [])))\n\n# Test that TypeError is still raised for non-conditional objects\ntry:\n    Q() & 'invalid'\n    print('ERROR: should have raised TypeError')\nexcept TypeError:\n    print('TypeError correctly raised for non-conditional object')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14034__s62UydY":{"task_name":"django__django-14034","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 73 steps but only 42 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 26 times. Full breakdown: Bash (26), Read (7), Edit (6), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): fields.py, test_forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test tests.forms_tests.field_tests.test_multivaluefield --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14053__DE9DF6j":{"task_name":"django__django-14053","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (6), Agent (2), Grep (2), Edit (2), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): storage.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/staticfiles_tests/test_storage.py -x -q 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14089__XTyqLU9":{"task_name":"django__django-14089","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Edit","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Read","Bash"],"answer":"Grep","reason":"\"Grep\" was called 3 times. Full breakdown: Grep (3), Edit (2), Read (1), Bash (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): datastructures.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14122__piK8AvM":{"task_name":"django__django-14122","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 32 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Glob","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (4), Agent (2), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import Count\n\n# Create a model with Meta.ordering\nclass TestModel(models.Model):\n    name = models.CharField(max_length=100)\n    category = models.CharField(max_length=100)\n    \n    class Meta:\n        app_label = 'test'\n        ordering = ['name']\n\n# Create the table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(TestModel)\n\n# Test: values().annotate() should NOT include 'name' in GROUP BY\nqs = TestModel.objects.values('category').annotate(count=Count('id'))\nsql = str(qs.query)\nprint('SQL:', sql)\nprint()\n\n# Check if 'name' appears in GROUP BY\ngroup_by_idx = sql.upper().index('GROUP BY')\ngroup_by_clause = sql[group_by_idx:]\nprint('GROUP BY clause:', group_by_clause)\nprint()\n\nif 'name' in group_by_clause.lower():\n    print('BUG: Meta.ordering field \\\"name\\\" is in GROUP BY!')\nelse:\n    print('OK: Meta.ordering field \\\"name\\\" is NOT in GROUP BY')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14140__VnpELWo":{"task_name":"django__django-14140","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Edit","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query_utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14155__LHEbJYH":{"task_name":"django__django-14155","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Grep","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (3), Grep (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): resolvers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport functools\nfrom django.urls.resolvers import ResolverMatch\n\ndef my_view(request, arg1, arg2):\n    pass\n\n# Test with a regular function\nmatch = ResolverMatch(my_view, ('a',), {'arg2': 'b'})\nprint('Regular:', repr(match))\n\n# Test with a partial function\npartial_view = functools.partial(my_view, 'fixed_arg1')\nmatch = ResolverMatch(partial_view, ('a',), {'arg2': 'b'})\nprint('Partial:', repr(match))\nprint('func:', match.func)\nprint('args:', match.args)\nprint('kwargs:', match.kwargs)\n\n# Test with partial with keyword args\npartial_view2 = functools.partial(my_view, arg2='default')\nmatch2 = ResolverMatch(partial_view2, ('a',), {})\nprint('Partial kw:', repr(match2))\nprint('args:', match2.args)\nprint('kwargs:', match2.kwargs)\n\n# Test kwargs override (url kwargs should override partial keywords)\nmatch3 = ResolverMatch(partial_view2, ('a',), {'arg2': 'override'})\nprint('Partial kw override:', repr(match3))\nprint('kwargs:', match3.kwargs)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14170__NY2ruDd":{"task_name":"django__django-14170","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 43 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (7), Edit (7), Agent (1), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): lookups.py, test_extract_trunc.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test db_functions.datetime.test_extract_trunc.DateFunctionTests.test_extract_iso_year_func_boundaries --settings=tests.test_sqlite -v 2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14238__eyupNsY":{"task_name":"django__django-14238","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Grep","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Grep (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14311__NHjsWBA":{"task_name":"django__django-14311","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Edit","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Write","Read"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (7), Edit (3), Grep (2), Agent (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): autoreload.py, test_autoreload.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom tests.utils_tests.test_module import __main__ as test_main\ns = test_main.__spec__\nprint('name:', s.name)\nprint('parent:', s.parent)\n\" 2>/dev/null || python -c \"\nimport sys; sys.path.insert(0, '/testbed/tests')\nfrom utils_tests.test_module import __main__ as test_main\ns = test_main.__spec__\nprint('name:', s.name)\nprint('parent:', s.parent)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14315__mwX8BKp":{"task_name":"django__django-14315","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (3), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): client.py, test_postgresql.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/dbshell/ -x -q 2>&1 | head -50\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14349__g26sW3q":{"task_name":"django__django-14349","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Glob","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): validators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/validators/tests.py -x -q --tb=short -k \"URLValidator\" 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14351__6teZaJf":{"task_name":"django__django-14351","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 67 steps but only 46 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Write","Grep"],"answer":"Read","reason":"\"Read\" was called 19 times. Full breakdown: Read (19), Grep (13), Bash (8), Edit (3), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): query.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/queries/tests.py::Queries1Tests::test_subquery_in_or_with_annotation -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14373__EwwUout":{"task_name":"django__django-14373","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dateformat.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14376__NJRvFFq":{"task_name":"django__django-14376","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Read","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Glob","Bash"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14404__krwoSSz":{"task_name":"django__django-14404","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Bash","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sites.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.tests.AdminSiteFinalCatchAllPatternTests -v 2 --settings=test_sqlite 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14434__t6Af48j":{"task_name":"django__django-14434","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (8), Read (5), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): schema.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test backends.test_ddl_references --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14493__msKK3CA":{"task_name":"django__django-14493","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 7 steps but only 4 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Bash","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Bash","Grep"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): storage.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14500__GghpA67":{"task_name":"django__django-14500","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (4), Agent (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): executor.py, test_executor.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_executor.ExecutorTests.test_run_with_squashed migrations.test_executor.ExecutorTests.test_apply_all_replaced_marks_replacement_as_applied migrations.test_executor.ExecutorTests.test_migrate_marks_replacement_applied_even_if_it_did_nothing --settings=test_sqlite --verbosity=2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14534__vyss2jP":{"task_name":"django__django-14534","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Glob","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Grep (6), Read (5), Edit (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): boundfield.py, test_forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/forms_tests/tests/test_forms.py -x -q --tb=short 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14539__YdEMu35":{"task_name":"django__django-14539","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 21 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): html.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.utils.html import urlize\n\n# Bug report case\nresult = urlize('Search for google.com/?q=1&lt! and see.')\nprint('Result:', repr(result))\n\nexpected = 'Search for <a href=\\\"http://google.com/?q=1%3C\\\">google.com/?q=1&lt</a>! and see.'\nprint('Expected:', repr(expected))\nprint('Match:', result == expected)\n\n# Test without entities (regression check)\nresult2 = urlize('Search for google.com! and see.')\nprint()\nprint('No entity:', repr(result2))\n\n# Test with multiple trailing punctuation\nresult3 = urlize('Search for google.com/?q=1&lt!. and see.')\nprint('Multi trail:', repr(result3))\n\n# Test with entity but no trailing punct\nresult4 = urlize('Search for google.com/?q=1&lt and see.')\nprint('No trail:', repr(result4))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14559__dSP8Utd":{"task_name":"django__django-14559","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (2), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test queries.test_bulk_update --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14580__8up3iYM":{"task_name":"django__django-14580","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): serializer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.migrations.serializer import TypeSerializer\nfrom django.db import models\n\n# Test that models.Model now returns the correct import\nserializer = TypeSerializer(models.Model)\nstring, imports = serializer.serialize()\nprint('String:', string)\nprint('Imports:', imports)\nassert string == 'models.Model'\nassert 'from django.db import models' in imports, f'Missing import! Got: {imports}'\nprint('TypeSerializer fix verified.')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14608__qxfX2WB":{"task_name":"django__django-14608","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (4), Agent (1), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): formsets.py, tests.py, test_formsets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/forms_tests/tests/test_formsets.py::FormsFormsetTestCase::test_non_form_errors -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14631__9zcWAsj":{"task_name":"django__django-14631","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 52 steps but only 35 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","EnterPlanMode","Grep"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), TodoWrite (7), Edit (6), Read (5), Agent (3), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): boundfield.py, forms.py, test_forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test forms_tests.tests.test_forms --settings=tests.test_sqlite -v 0 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14672__XMUiC6H":{"task_name":"django__django-14672","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Agent","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Write","Agent"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): reverse_related.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14725__JRVMPf4":{"task_name":"django__django-14725","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 33 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Grep","Edit"],"answer":"Edit","reason":"\"Edit\" was called 9 times. Full breakdown: Edit (9), Bash (9), Read (8), TodoWrite (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): formsets.py, models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test forms_tests.tests.test_formsets model_formsets --settings=tests.test_sqlite --parallel -v 0 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14752__xHhoKbH":{"task_name":"django__django-14752","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Glob","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): autocomplete.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.test_autocomplete_view --settings=tests.test_sqlite 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14765__HktAsho":{"task_name":"django__django-14765","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 8 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Agent","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Read","Edit"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Edit (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): state.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14771__6ybtiNg":{"task_name":"django__django-14771","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): autoreload.py, test_autoreload.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/utils_tests/test_autoreload.py::TestChildArguments -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14787__ftgyZa4":{"task_name":"django__django-14787","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Edit","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): decorators.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom functools import wraps\nfrom django.utils.decorators import method_decorator\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef log_decorator(func):\n    @wraps(func)\n    def inner(*args, **kwargs):\n        try:\n            result = func(*args, **kwargs)\n        except Exception as e:\n            result = str(e)\n        finally:\n            logger.debug(f'{func.__name__} called with args: {args} and kwargs: {kwargs} resulting: {result}')\n        return result\n    return inner\n\nclass Test:\n    @method_decorator(log_decorator)\n    def hello_world(self):\n        return 'hello'\n\nprint(Test().hello_world())\nprint('Success!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14792__rNPAZaz":{"task_name":"django__django-14792","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Edit","Write"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (5), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): operations.py, operations.py, operations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.backends.postgresql.operations import DatabaseOperations\n\n# Create an instance (pass None for connection, we only test _prepare_tzname_delta)\nclass FakeConn:\n    pass\nops = DatabaseOperations.__new__(DatabaseOperations)\n\n# Test PostgreSQL _prepare_tzname_delta\ncases = [\n    # (input, expected_output)\n    ('+10', '-10'),           # raw positive offset -> reversed\n    ('-05:00', '+05:00'),     # raw negative offset -> reversed\n    ('UTC+05:00', 'UTC-05:00'),  # UTC+ offset -> reversed\n    ('UTC-10:00', 'UTC+10:00'),  # UTC- offset -> reversed\n    ('UTC', 'UTC'),           # plain UTC -> unchanged\n    ('Etc/GMT-10', 'Etc/GMT-10'),  # IANA name -> unchanged (THE BUG FIX)\n    ('Etc/GMT+5', 'Etc/GMT+5'),    # IANA name -> unchanged\n    ('US/Eastern', 'US/Eastern'),  # IANA name -> unchanged\n    ('Europe/London', 'Europe/London'),  # IANA name -> unchanged\n    ('America/Argentina/Buenos_Aires', 'America/Argentina/Buenos_Aires'),  # IANA name -> unchanged\n]\n\nall_pass = True\nfor inp, expected in cases:\n    result = ops._prepare_tzname_delta(inp)\n    status = 'PASS' if result == expected else 'FAIL'\n    if status == 'FAIL':\n        all_pass = False\n    print(f'{status}: _prepare_tzname_delta({inp!r}) = {result!r} (expected {expected!r})')\n\nprint()\nprint('All tests passed!' if all_pass else 'SOME TESTS FAILED!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-14855__EHHHEai":{"task_name":"django__django-14855","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 28 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Grep (7), Read (7), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): helpers.py, admin.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.tests.ReadonlyTest.test_readonly_foreignkey_links admin_views.tests.ReadonlyTest.test_readonly_foreignkey_links_custom_admin_site --settings=admin_views.settings -v2 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14915__pjJbLyQ":{"task_name":"django__django-14915","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 2 times. Full breakdown: Bash (2), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.forms.models import ModelChoiceIteratorValue\n\n# Test basic hashability\nv = ModelChoiceIteratorValue(1, None)\nprint('hash:', hash(v))\n\n# Test 'in' with dict (the reported failing case)\nshow_fields = {1: ['first_name', 'last_name']}\nprint('v in dict:', v in show_fields)  # Should be True\n\n# Test 'in' with list\nallowed = [1, 2]\nprint('v in list:', v in allowed)  # Should be True\n\n# Test use in set\ns = {v}\nprint('in set:', ModelChoiceIteratorValue(1, None) in s)  # Should be True\n\n# Test hash consistency with eq\nv2 = ModelChoiceIteratorValue(1, None)\nprint('eq:', v == v2, 'same hash:', hash(v) == hash(v2))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-14999__V8bUeon":{"task_name":"django__django-14999","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Grep","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (5), Agent (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, test_operations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_operations.py -k \"rename_model\" -x --no-header -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15022__kkaCAzT":{"task_name":"django__django-15022","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 15 times. Full breakdown: Bash (15), Read (5), Agent (2), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/admin_changelist/tests.py -x -q --tb=short 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15037__TBmyCk7":{"task_name":"django__django-15037","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (5), TodoWrite (5), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): inspectdb.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test inspectdb.tests --settings=inspectdb.tests_settings 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15098__UmDcoHo":{"task_name":"django__django-15098","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 34 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Bash","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (10), Read (7), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): trans_real.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport re\n# Original regex\nold_re = re.compile(r'^/(\\w+([@-]\\w+)?)(/|$)')\n# Fixed regex\nnew_re = re.compile(r'^/(\\w+([@-]\\w+)*)(/|$)')\n\ntest_paths = ['/en-us/', '/en-latn-us/', '/en-Latn-US/', '/en/']\nfor path in test_paths:\n    old_match = old_re.match(path)\n    new_match = new_re.match(path)\n    print(f'{path}:')\n    print(f'  old: {old_match[1] if old_match else None}')\n    print(f'  new: {new_match[1] if new_match else None}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15103__GLKiwwT":{"task_name":"django__django-15103","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Read","Grep"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Edit (4), Bash (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): defaultfilters.py, html.py, test_json_script.py, test_html.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/utils_tests/test_html.py::TestUtilsHtml::test_json_script tests/utils_tests/test_html.py::TestUtilsHtml::test_json_script_without_id tests/template_tests/filter_tests/test_json_script.py -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15104__rVzC7Au":{"task_name":"django__django-15104","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Bash","Write","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): autodetector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db import models\nfrom django.db.migrations.autodetector import MigrationAutodetector\nfrom django.db.migrations.state import ModelState, ProjectState\nimport django\nfrom django.conf import settings\nsettings.configure(INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'], DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}})\ndjango.setup()\n\nclass CustomFKField(models.ForeignKey):\n    def __init__(self, *args, **kwargs):\n        kwargs['to'] = 'testapp.HardcodedModel'\n        super().__init__(*args, **kwargs)\n    def deconstruct(self):\n        name, path, args, kwargs = super().deconstruct()\n        del kwargs['to']\n        return name, path, args, kwargs\n\nbefore = ProjectState()\nbefore.add_model(ModelState('testapp', 'HardcodedModel', []))\nafter = ProjectState()\nafter.add_model(ModelState('testapp', 'HardcodedModel', []))\nafter.add_model(ModelState('testapp', 'TestModel', [('custom', CustomFKField(on_delete=models.CASCADE))]))\nchanges = MigrationAutodetector(before, after)._detect_changes()\nassert len(changes['testapp']) == 1\nprint('OK - fix works')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15127__EHJV4Zv":{"task_name":"django__django-15127","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Grep","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Edit (4), Read (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): signals.py, base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test messages_tests --settings=messages_tests.settings -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15128__mbn3z9L":{"task_name":"django__django-15128","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 64 steps but only 40 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (13), Edit (5), Agent (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): query.py, models.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create models dynamically\nclass Qux(models.Model):\n    class Meta:\n        app_label = 'test_app'\n\nclass Baz(models.Model):\n    class Meta:\n        app_label = 'test_app'\n\nclass Foo(models.Model):\n    qux = models.ForeignKey(Qux, on_delete=models.CASCADE, related_name='foos')\n    class Meta:\n        app_label = 'test_app'\n\nclass Bar(models.Model):\n    foo = models.ForeignKey(Foo, on_delete=models.CASCADE, related_name='bars')\n    another_foo = models.ForeignKey(Foo, on_delete=models.CASCADE, related_name='other_bars')\n    baz = models.ForeignKey(Baz, on_delete=models.CASCADE, related_name='bars')\n    class Meta:\n        app_label = 'test_app'\n\n# Add ManyToMany\nQux.bazes = models.ManyToManyField(Baz, related_name='quxes')\nQux.bazes.contribute_to_class(Qux, 'bazes')\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Qux)\n    schema_editor.create_model(Baz)\n    schema_editor.create_model(Foo)\n    schema_editor.create_model(Bar)\n    # Create the M2M through table\n    schema_editor.create_model(Qux.bazes.through)\n\nfrom django.db.models import Q\n\nqux = Qux.objects.create()\nqs1 = qux.foos.all()\nqs2 = Foo.objects.filter(\n    Q(bars__baz__in=qux.bazes.all()) | Q(other_bars__baz__in=qux.bazes.all())\n)\n\n# This should work (qs2 | qs1 works, qs1 | qs2 was failing)\nresult = qs1 | qs2\nprint('qs1 | qs2 succeeded!')\nprint('SQL:', str(result.query))\n\n# Also verify qs2 | qs1 still works\nresult2 = qs2 | qs1\nprint('qs2 | qs1 succeeded!')\nprint('SQL:', str(result2.query))\n\n# Verify both produce results without error\nlist(result)\nlist(result2)\nprint('Both queries executed successfully!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15161__eAQLmCs":{"task_name":"django__django-15161","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 102 steps but only 80 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Bash","Write"],"answer":"Edit","reason":"\"Edit\" was called 23 times. Full breakdown: Edit (23), Bash (21), Read (20), TodoWrite (5), Grep (4), Agent (3), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): aggregates.py, expressions.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test expressions.tests.FTests.test_deconstruct expressions.tests.ValueTests.test_deconstruct expressions.tests.ValueTests.test_deconstruct_output_field --settings=tests.test_sqlite --verbosity=2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15252__he88hX9":{"task_name":"django__django-15252","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): recorder.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_loader.RecorderTests --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15268__EJdks2z":{"task_name":"django__django-15268","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.migrations.optimizer import MigrationOptimizer\nfrom django.db import migrations\n\noptimizer = MigrationOptimizer()\n\n# The scenario from the issue\noperations = [\n    migrations.AlterUniqueTogether(name='mymodel', unique_together=set()),\n    migrations.AlterIndexTogether(name='mymodel', index_together=set()),\n    migrations.AlterUniqueTogether(name='mymodel', unique_together={('col',)}),\n    migrations.AlterIndexTogether(name='mymodel', index_together={('col',)}),\n]\n\nresult = optimizer.optimize(operations, 'testapp')\nprint(f'Input: {len(operations)} operations')\nprint(f'Output: {len(result)} operations')\nfor op in result:\n    print(f'  {op.__class__.__name__}({op.option_name}={op.option_value})')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15277__BYKY5gN":{"task_name":"django__django-15277","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Edit (2), Agent (1), Read (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models import Value\nx = Value('test')\ny = x._resolve_output_field()\nprint('validators:', y.validators)\nprint('max_length:', y.max_length)\n\n# Also verify CharField with max_length still gets the validator\nfrom django.db.models.fields import CharField\nf = CharField(max_length=255)\nprint('CharField(max_length=255) validators:', f.validators)\n\n# Verify clean works on the no-max-length field doesn't crash\nprint('No crash on resolve_output_field - success')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15278__Mf6jUaL":{"task_name":"django__django-15278","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): schema.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import connection, models\nfrom django.db.backends.sqlite3.schema import DatabaseSchemaEditor\n\n# Create a base table\nclass TestModel(models.Model):\n    name = models.CharField(max_length=100)\n    class Meta:\n        app_label = 'test'\n\nclass RelatedModel(models.Model):\n    class Meta:\n        app_label = 'test'\n\nwith connection.schema_editor() as editor:\n    editor.create_model(TestModel)\n    editor.create_model(RelatedModel)\n\n# Now try adding a nullable OneToOneField\nfield = models.OneToOneField(RelatedModel, null=True, blank=True, on_delete=models.SET_NULL)\nfield.set_attributes_from_name('related')\nfield.contribute_to_class(TestModel, 'related')\n\nwith connection.schema_editor() as editor:\n    editor.add_field(TestModel, field)\n\nprint('SUCCESS: nullable OneToOneField added without error')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15280__AwtPhjz":{"task_name":"django__django-15280","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 68 steps but only 43 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Edit","Read","Bash"],"answer":"Read","reason":"\"Read\" was called 17 times. Full breakdown: Read (17), Bash (10), Grep (8), Edit (5), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): related_descriptors.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/prefetch_related/tests.py::CustomPrefetchTests::test_nested_prefetch_with_deferred_fields -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15315__KgEv5Qe":{"task_name":"django__django-15315","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): __init__.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}}, INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'])\ndjango.setup()\n\nfrom django.db import models\n\nf = models.CharField(max_length=200)\nd = {f: 1}\n\nclass Book(models.Model):\n    title = f\n    class Meta:\n        app_label = 'test'\n\nassert f in d, 'Field should still be found in dict after being assigned to a model'\nprint('SUCCESS: Field.__hash__() is now immutable')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15368__Sxd4QUL":{"task_name":"django__django-15368","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (1), Edit (1), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test tests.queries.test_bulk_update --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15375__QL7Q7VS":{"task_name":"django__django-15375","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Bash","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): aggregates.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import connection\nfrom django.db.models import Sum, F\nfrom django.contrib.auth.models import User\n\n# Create the tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(User)\n\n# Insert some test data\nUser.objects.create_user('a', password='x')\nUser.objects.create_user('b', password='x')\n\n# This was the failing case: annotate() then aggregate() with default\nresult = User.objects.annotate(idx=F('id')).aggregate(Sum('id', default=0))\nprint('Result:', result)\nprint('SUCCESS')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15380__nZruJJh":{"task_name":"django__django-15380","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Read","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): autodetector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.migrations.autodetector import MigrationAutodetector\nfrom django.db.migrations.state import ProjectState, ModelState\nfrom django.db.migrations.questioner import MigrationQuestioner\nfrom django.db import models\n\n# 'from' state: app 'testapp' has model 'MyModel' with fields 'id' and 'field_old'\nfrom_state = ProjectState()\nfrom_state.add_model(ModelState('testapp', 'MyModel', [\n    ('id', models.AutoField(primary_key=True)),\n    ('field_old', models.CharField(max_length=100)),\n]))\n\n# 'to' state: model renamed to 'MyModel2', field renamed to 'field_new'\nto_state = ProjectState()\nto_state.add_model(ModelState('testapp', 'MyModel2', [\n    ('id', models.AutoField(primary_key=True)),\n    ('field_new', models.CharField(max_length=100)),\n]))\n\nclass YesQuestioner(MigrationQuestioner):\n    def ask_rename(self, model_name, old_name, new_name, field_instance):\n        return True\n    def ask_rename_model(self, old_model_state, new_model_state):\n        return True\n\nautodetector = MigrationAutodetector(from_state, to_state, YesQuestioner())\nchanges = autodetector.changes(graph=None)\nprint('Success! Changes:', changes)\nfor app, migrations in changes.items():\n    for m in migrations:\n        for op in m.operations:\n            print(f'  {op}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15382__dccPdpY":{"task_name":"django__django-15382","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.contrib.auth.models import User\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(User)\n\n# Create test data\nUser.objects.create_user('testuser', 'test@test.com', 'password', first_name='test')\n\n# Test case from the bug report\nqs = User.objects.filter(~models.Exists(User.objects.none()), first_name='test')\nprint('Query:', qs.query)\nprint('Count:', qs.count())\nprint('Results:', list(qs.values_list('username', flat=True)))\n\n# Also test non-negated Exists with empty queryset (should return nothing)\nqs2 = User.objects.filter(models.Exists(User.objects.none()), first_name='test')\nprint()\nprint('Non-negated empty Exists:')\nprint('Count:', qs2.count())\n\n# Test negated Exists with non-empty queryset (normal case)\nqs3 = User.objects.filter(~models.Exists(User.objects.all()), first_name='test')\nprint()\nprint('Negated non-empty Exists:')\nprint('Count:', qs3.count())\n\n# Test with OR\nqs4 = User.objects.filter(models.Q(first_name='nonexistent') | ~models.Exists(User.objects.none()))\nprint()\nprint('OR with negated empty Exists (should match everything):')\nprint('Count:', qs4.count())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15467__g4bHWeq":{"task_name":"django__django-15467","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Glob","Grep"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Edit (2), Read (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15499__DYzX9uD":{"task_name":"django__django-15499","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (3), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, test_optimizer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_optimizer.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15503__k4N58Yq":{"task_name":"django__django-15503","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 70 steps but only 42 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (11), Edit (8), Agent (2), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): json.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_fields.test_jsonfield.TestQuerying.test_has_key model_fields.test_jsonfield.TestQuerying.test_has_key_null_value model_fields.test_jsonfield.TestQuerying.test_has_key_deep model_fields.test_jsonfield.TestQuerying.test_has_key_list model_fields.test_jsonfield.TestQuerying.test_has_keys model_fields.test_jsonfield.TestQuerying.test_has_any_keys --settings=tests.test_sqlite --no-input -v 2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15525__iwFJ96A":{"task_name":"django__django-15525","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 56 steps but only 34 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Agent","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Write","Grep"],"answer":"Read","reason":"\"Read\" was called 14 times. Full breakdown: Read (14), Bash (8), Edit (6), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): base.py, natural.py, test_natural.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\n# Check if test settings have multiple databases\nimport tests.test_sqlite as ts\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15554__rAmvk8A":{"task_name":"django__django-15554","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 92 steps but only 60 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Bash","Read"],"answer":"Read","reason":"\"Read\" was called 23 times. Full breakdown: Read (23), Bash (14), Edit (13), Grep (6), Agent (1), TaskOutput (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): datastructures.py, query.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test filtered_relation.tests.FilteredRelationTests.test_multiple_filtered_relations_for_same_relation --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15561__o4K5ctA":{"task_name":"django__django-15561","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): schema.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"DJANGO_SETTINGS_MODULE=tests.test_sqlite python -c \"\nimport django\ndjango.setup()\nfrom django.db import models, connection\nfrom django.db.backends.base.schema import BaseDatabaseSchemaEditor\n\n# Test 1: Adding choices should NOT trigger alter\nold_field = models.IntegerField(name='status', column='status')\nnew_field = models.IntegerField(name='status', column='status', choices=[(1, 'Draft'), (2, 'Published')])\neditor = BaseDatabaseSchemaEditor(connection)\nresult = editor._field_should_be_altered(old_field, new_field)\nprint(f'Should alter when only adding choices: {result}')  # Should be False\n\n# Test 2: Changing choices should NOT trigger alter\nold_field2 = models.IntegerField(name='status', column='status', choices=[(1, 'Draft')])\nnew_field2 = models.IntegerField(name='status', column='status', choices=[(1, 'Draft'), (2, 'Published')])\nresult2 = editor._field_should_be_altered(old_field2, new_field2)\nprint(f'Should alter when only changing choices: {result2}')  # Should be False\n\n# Test 3: Real schema changes should still trigger alteration\nold_field3 = models.IntegerField(name='status', column='status')\nnew_field3 = models.CharField(name='status', column='status', max_length=10)\nresult3 = editor._field_should_be_altered(old_field3, new_field3)\nprint(f'Should alter when changing field type: {result3}')  # Should be True\n\n# Test 4: Adding null should trigger alteration\nold_field4 = models.IntegerField(name='status', column='status')\nnew_field4 = models.IntegerField(name='status', column='status', null=True)\nresult4 = editor._field_should_be_altered(old_field4, new_field4)\nprint(f'Should alter when adding null: {result4}')  # Should be True\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15563__xgfia8S":{"task_name":"django__django-15563","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Edit","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): compiler.py, subqueries.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create models dynamically\nclass Base(models.Model):\n    base_id = models.AutoField(primary_key=True)\n    field_base = models.IntegerField()\n    class Meta:\n        app_label = 'test'\n\nclass OtherBase(models.Model):\n    otherbase_id = models.AutoField(primary_key=True)\n    field_otherbase = models.IntegerField()\n    class Meta:\n        app_label = 'test'\n\nclass Child(Base, OtherBase):\n    class Meta:\n        app_label = 'test'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(Base)\n    schema_editor.create_model(OtherBase)\n    schema_editor.create_model(Child)\n\n# Reproduce the bug scenario\nOtherBase.objects.create(field_otherbase=100)\nOtherBase.objects.create(field_otherbase=101)\nChild.objects.create(field_base=0, field_otherbase=0)\nChild.objects.create(field_base=1, field_otherbase=1)\n\nprint('Before update:')\nprint('Child field_otherbase:', list(Child.objects.values('field_otherbase')))\nprint('OtherBase (all):', list(OtherBase.objects.values('otherbase_id', 'field_otherbase')))\n\nChild.objects.update(field_otherbase=55)\n\nprint()\nprint('After update:')\nprint('Child field_otherbase:', list(Child.objects.values('field_otherbase')))\nprint('OtherBase (all):', list(OtherBase.objects.values('otherbase_id', 'field_otherbase')))\n\n# Verify: Child rows should now be 55, OtherBase-only rows should be unchanged\nchild_values = list(Child.objects.values_list('field_otherbase', flat=True))\notherbase_only = list(OtherBase.objects.filter(otherbase_id__in=[1,2]).values_list('field_otherbase', flat=True))\n\nassert child_values == [55, 55], f'Child values wrong: {child_values}'\nassert otherbase_only == [100, 101], f'OtherBase-only values should be unchanged: {otherbase_only}'\n\nprint()\nprint('SUCCESS: Bug is fixed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15569__tNwSQiV":{"task_name":"django__django-15569","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Write","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Edit","Read"],"answer":"Read","reason":"\"Read\" was called 3 times. Full breakdown: Read (3), Edit (2), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): query_utils.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15572__q3D5jpb":{"task_name":"django__django-15572","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 25 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Glob","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (4), Edit (2), Grep (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): autoreload.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/template_tests/test_autoreload.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15629__jDfJoe3":{"task_name":"django__django-15629","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 163 steps but only 95 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Grep","Bash"],"answer":"Read","reason":"\"Read\" was called 36 times. Full breakdown: Read (36), Bash (27), Edit (17), Grep (12), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): schema.py, schema.py, related.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create test models\nclass Account(models.Model):\n    id = models.CharField(primary_key=True, db_collation='utf8_bin', max_length=22)\n    class Meta:\n        app_label = 'test'\n\nclass Profile(models.Model):\n    account = models.ForeignKey(Account, on_delete=models.CASCADE)\n    class Meta:\n        app_label = 'test'\n\n# Check that the FK field propagates db_collation\nfk_field = Profile._meta.get_field('account')\nparams = fk_field.db_parameters(connection)\nprint('FK db_parameters:', params)\nassert params.get('collation') == 'utf8_bin', f'Expected utf8_bin, got {params.get(\\\"collation\\\")}'\nprint('SUCCESS: db_collation is propagated to FK field')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15695__uxBWMZq":{"task_name":"django__django-15695","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Grep","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, test_operations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_operations.py::OperationTests::test_rename_index_unnamed_index -x 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15731__ry7beRT":{"task_name":"django__django-15731","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Edit (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): manager.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15732__gP4kLdM":{"task_name":"django__django-15732","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): schema.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test schema --settings=tests.test_sqlite -v 2 -k unique_together 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15741__gpkeBnf":{"task_name":"django__django-15741","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Read","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Agent","Write"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): formats.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(USE_L10N=True)\ndjango.setup()\n\nfrom django.utils.translation import gettext_lazy as _\nfrom django.utils.formats import get_format\n\n# Test with a lazy string - this was the failing case\nlazy_format = _('DATE_FORMAT')\nresult = get_format(lazy_format)\nprint(f'Lazy string result: {result}')\n\n# Test with a regular string - should still work\nresult2 = get_format('DATE_FORMAT')\nprint(f'Regular string result: {result2}')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15814__YQUPSyP":{"task_name":"django__django-15814","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Read","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (5), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): query.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test proxy_models.tests.ProxyModelTests.test_select_related_only_on_proxy_model_fk --settings=test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15851__pprKi6J":{"task_name":"django__django-15851","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (3), Read (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): client.py, test_postgresql.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/dbshell/test_postgresql.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15863__mDU2GEn":{"task_name":"django__django-15863","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 21 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Grep","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Agent (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): defaultfilters.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test template_tests.filter_tests.test_floatformat --settings=test_sqlite 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15916__TmNdTCk":{"task_name":"django__django-15916","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django import forms\nfrom django.db import models\nimport django\nfrom django.conf import settings\nsettings.configure(DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}}, DEFAULT_AUTO_FIELD='django.db.models.AutoField', INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'])\ndjango.setup()\n\nclass MyModel(models.Model):\n    active = models.BooleanField()\n    name = models.CharField(max_length=64, blank=True, null=True)\n    class Meta:\n        app_label = 'test'\n\ndef all_required(field, **kwargs):\n    formfield = field.formfield(**kwargs)\n    formfield.required = True\n    return formfield\n\nclass MyForm(forms.ModelForm):\n    formfield_callback = all_required\n    class Meta:\n        model = MyModel\n        formfield_callback = all_required\n        fields = ['active', 'name']\n\nFactoryForm = forms.modelform_factory(MyModel, form=MyForm)\n\n# Check that both forms have required fields\nmy_form = MyForm()\nfactory_form = FactoryForm()\n\nprint('MyForm fields required:')\nfor name, field in my_form.fields.items():\n    print(f'  {name}: required={field.required}')\n\nprint('FactoryForm fields required:')\nfor name, field in factory_form.fields.items():\n    print(f'  {name}: required={field.required}')\n\n# Verify they match\nassert all(f.required for f in factory_form.fields.values()), 'FactoryForm fields should all be required!'\nprint('SUCCESS: FactoryForm correctly inherited formfield_callback from MyForm')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-15930__3vw2QJZ":{"task_name":"django__django-15930","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Edit","Write"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.contrib.auth.models import User\nfrom django.db import connection\nfrom django.db.models import Q, Value, Case, When, BooleanField\n\n# Create the table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(User)\n\n# Test the reported case\nqs = User.objects.annotate(\n    _a=Case(\n        When(~Q(pk__in=[]), then=Value(True)),\n        default=Value(False),\n        output_field=BooleanField(),\n    )\n).order_by('-_a').values('pk')\n\nprint('SQL:', qs.query)\nprint('Result:', list(qs))\nprint('Success!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15957__EzSE3pZ":{"task_name":"django__django-15957","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 49 steps but only 35 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Edit","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (12), Agent (2), Edit (2), Grep (1), TaskOutput (1), TaskStop (1), Write (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nimport os\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tests.test_sqlite')\ndjango.setup()\n\nfrom django.test.utils import setup_test_environment\nsetup_test_environment()\n\n# Use the test models\nfrom prefetch_related.models import House, Room, Person\nfrom django.db.models import Prefetch\n\n# Create test data\nfrom django.test.utils import setup_databases\nfrom django.test.runner import DiscoverRunner\n\nrunner = DiscoverRunner(verbosity=0)\nold_config = runner.setup_databases()\n\n# Create some test data\np1 = Person.objects.create(name='Person 1')\np2 = Person.objects.create(name='Person 2')\n\nh1 = House.objects.create(name='House 1', address='Addr 1', owner=p1)\nh2 = House.objects.create(name='House 2', address='Addr 2', owner=p1)\nh3 = House.objects.create(name='House 3', address='Addr 3', owner=p1)\nh4 = House.objects.create(name='House 4', address='Addr 4', owner=p2)\nh5 = House.objects.create(name='House 5', address='Addr 5', owner=p2)\n\nr1 = Room.objects.create(name='Room 1', house=h1)\nr2 = Room.objects.create(name='Room 2', house=h1)\nr3 = Room.objects.create(name='Room 3', house=h1)\nr4 = Room.objects.create(name='Room 4', house=h2)\nr5 = Room.objects.create(name='Room 5', house=h2)\n\n# Set main rooms\nh1.main_room = r1\nh1.save()\nh2.main_room = r4\nh2.save()\n\n# Add houses to persons via M2M\np1.houses.add(h1, h2, h3)\np2.houses.add(h4, h5)\n\n# Test: Prefetch with sliced queryset and to_attr\nprint('Test 1: Prefetch with sliced queryset and to_attr')\ntry:\n    persons = list(Person.objects.prefetch_related(\n        Prefetch('houses', queryset=House.objects.order_by('name')[:2], to_attr='first_two_houses')\n    ))\n    for p in persons:\n        print(f'  {p.name}: {[h.name for h in p.first_two_houses]}')\n    print('  PASSED')\nexcept Exception as e:\n    print(f'  FAILED: {e}')\n\n# Test: Prefetch with sliced queryset without to_attr  \nprint('Test 2: Prefetch with sliced queryset without to_attr')\ntry:\n    persons = list(Person.objects.prefetch_related(\n        Prefetch('houses', queryset=House.objects.order_by('name')[:2])\n    ))\n    for p in persons:\n        print(f'  {p.name}: {[h.name for h in p.houses.all()]}')\n    print('  PASSED')\nexcept Exception as e:\n    print(f'  FAILED: {e}')\n\n# Test: Prefetch with offset and limit\nprint('Test 3: Prefetch with offset and limit [1:3]')\ntry:\n    persons = list(Person.objects.prefetch_related(\n        Prefetch('houses', queryset=House.objects.order_by('name')[1:3], to_attr='houses_slice')\n    ))\n    for p in persons:\n        print(f'  {p.name}: {[h.name for h in p.houses_slice]}')\n    print('  PASSED')\nexcept Exception as e:\n    print(f'  FAILED: {e}')\n\n# Test: Reverse FK with slice\nprint('Test 4: Reverse FK with slice')\ntry:\n    houses = list(House.objects.filter(pk__in=[h1.pk, h2.pk]).prefetch_related(\n        Prefetch('rooms', queryset=Room.objects.order_by('name')[:2], to_attr='first_two_rooms')\n    ))\n    for h in houses:\n        print(f'  {h.name}: {[r.name for r in h.first_two_rooms]}')\n    print('  PASSED')\nexcept Exception as e:\n    print(f'  FAILED: {e}')\n\nrunner.teardown_databases(old_config)\nprint('\\\\nAll tests completed!')\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15973__je7NVJ2":{"task_name":"django__django-15973","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Edit (4), Agent (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): autodetector.py, test_autodetector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_autodetector.py::AutodetectorTests::test_create_with_through_model_separate_app -xvs 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-15987__ux896Gh":{"task_name":"django__django-15987","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (4), Grep (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): loaddata.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test fixtures.tests.FixtureLoadingTests.test_fixture_dirs_with_duplicates --settings=test_sqlite 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16032__ybYVTa5":{"task_name":"django__django-16032","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 55 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Read","Bash"],"answer":"Read","reason":"\"Read\" was called 13 times. Full breakdown: Read (13), Bash (12), Edit (5), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): query.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test annotations.tests.NonAggregateAnnotationTestCase --settings=tests.test_sqlite -v 0 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16082__J42fAm7":{"task_name":"django__django-16082","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expressions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.db.models import DecimalField, IntegerField, FloatField, Value\nfrom django.db.models.expressions import CombinedExpression, Combinable\n\n# Test MOD with mixed types\nlhs = Value(10, output_field=IntegerField())\nrhs = Value(3, output_field=DecimalField())\n\n# This should resolve to DecimalField, not raise FieldError\nexpr = lhs % rhs\nprint('MOD Integer %% Decimal:', type(expr.output_field).__name__)\n\nexpr2 = rhs % lhs\nprint('MOD Decimal %% Integer:', type(expr2.output_field).__name__)\n\n# Test with Float\nlhs_float = Value(10, output_field=FloatField())\nexpr3 = lhs % lhs_float\nprint('MOD Integer %% Float:', type(expr3.output_field).__name__)\n\n# Test POW with mixed types\nexpr4 = lhs ** rhs\nprint('POW Integer ** Decimal:', type(expr4.output_field).__name__)\n\nprint('All OK!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16100__vi7SstV":{"task_name":"django__django-16100","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Edit","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (4), Read (2), Edit (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.tests.ListEditable --settings=tests.test_sqlite -v 0 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16116__g83fbiP":{"task_name":"django__django-16116","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Agent","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (3), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): makemigrations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_commands.py -x -q -k \"check\" 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16136__X3mLLch":{"task_name":"django__django-16136","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport asyncio\nimport django\nfrom django.conf import settings\n\nsettings.configure(\n    ROOT_URLCONF='__main__',\n    SECRET_KEY='test',\n)\ndjango.setup()\n\nfrom django.views import View\nfrom django.http import HttpResponse\nfrom django.test import RequestFactory\n\nclass AsyncPostView(View):\n    async def post(self, request):\n        return HttpResponse('ok')\n\nfactory = RequestFactory()\n\n# Test: GET request to an async view that only supports POST\nrequest = factory.get('/demo')\nview = AsyncPostView.as_view()\n\n# The view function returns a coroutine since the view is async\nresult = view(request)\nprint(f'Result type: {type(result)}')\n\n# Await it like the ASGI handler would\nresponse = asyncio.run(result)\nprint(f'Response status: {response.status_code}')\nprint(f'Response type: {type(response).__name__}')\nprint('Success! No TypeError.')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16139__2JGUucM":{"task_name":"django__django-16139","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (5), Grep (4), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test auth_tests.test_forms.UserChangeFormTest --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16145__FRyxLfH":{"task_name":"django__django-16145","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Glob","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Glob"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (4), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): runserver.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport re\nfrom django.utils.regex_helper import _lazy_re_compile\n\nnaiveip_re = _lazy_re_compile(\n    r'^(?:(?P<addr>(?P<ipv4>\\d{1,3}(?:\\.\\d{1,3}){3})|(?P<ipv6>\\[[a-fA-F0-9:]+\\])|(?P<fqdn>[a-zA-Z0-9-]+(?:\\.[a-zA-Z0-9-]+)*))):)?(?P<port>\\d+)$',\n    re.X,\n)\n\n# Test '0:8000'\nm = re.match(naiveip_re, '0:8000')\nprint('Groups for 0:8000:', m.groups())\n\n# Test '0.0.0.0:8000' \nm = re.match(naiveip_re, '0.0.0.0:8000')\nprint('Groups for 0.0.0.0:8000:', m.groups())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16255__LHMxJYZ":{"task_name":"django__django-16255","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 5 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Agent","Glob","Read"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16256__DDJRr7r":{"task_name":"django__django-16256","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Read","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Edit (3), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): related_descriptors.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n)\ndjango.setup()\n\nfrom django.contrib.auth.models import User, Group\n# Check that the async methods exist on the related manager\nu = type('FakeUser', (), {'pk': 1, '_prefetched_objects_cache': {}, '_state': type('S', (), {'db': 'default', 'adding': False})()})()\n\n# Test reverse FK manager\nfrom django.db.models.fields.related_descriptors import create_reverse_many_to_one_manager\nprint('Import successful')\n\n# Verify the methods exist on the managers\nfrom django.db.models import Manager\nprint('acreate' in dir(Manager))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16263__6AEEeZG":{"task_name":"django__django-16263","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 32 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 4 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","EnterPlanMode","Edit","Glob"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 4 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Read","Grep"],"answer":"Read","reason":"\"Read\" was called 14 times. Full breakdown: Read (14), Grep (10), Agent (4), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16315__LYBfYsq":{"task_name":"django__django-16315","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compiler.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\n\n# Create a model with db_column\nclass TestModel(models.Model):\n    class Meta:\n        app_label = 'test'\n        db_table = 'test_table'\n    myid = models.IntegerField(primary_key=True, db_column='MyID')\n    myfield = models.IntegerField(null=True, db_column='MyField')\n\n# Create table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(TestModel)\n\n# Test bulk_create with update_conflicts\nobjs = [TestModel(myid=1, myfield=10), TestModel(myid=2, myfield=20)]\nresult = TestModel.objects.bulk_create(\n    objs,\n    update_conflicts=True,\n    update_fields=['myfield'],\n    unique_fields=['myid'],\n)\nprint('SUCCESS: bulk_create with db_column worked')\n\n# Verify by reading back\nitems = TestModel.objects.all().order_by('myid')\nfor item in items:\n    print(f'  myid={item.myid}, myfield={item.myfield}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16333__uBGRTWs":{"task_name":"django__django-16333","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Agent","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Write","Read"],"answer":"Read","reason":"\"Read\" was called 8 times. Full breakdown: Read (8), Bash (6), Edit (3), Grep (1), Agent (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): forms.py, test_forms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test auth_tests.test_forms.UserCreationFormTest.test_custom_form_saves_m2m --settings=auth_tests.settings --verbosity=2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16429__HQRFZmT":{"task_name":"django__django-16429","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Edit","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Agent","Glob"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): timesince.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test utils_tests.test_timesince --settings=test_sqlite 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16454__jNw6jRr":{"task_name":"django__django-16454","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 30 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Agent"],"answer":"Bash","reason":"\"Bash\" was called 15 times. Full breakdown: Bash (15), Grep (3), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom django.core.management.base import CommandParser\n\n# Simulate what happens when called from the command line\nparser = CommandParser(called_from_command_line=True, prog='manage.py cheeses')\nsubparsers = parser.add_subparsers(required=True)\ncreate = subparsers.add_parser('create')\ncreate.add_argument('name')\n\n# Test that the subparser has called_from_command_line set\nprint('create.called_from_command_line:', create.called_from_command_line)\n\n# Test that missing args in subparser gives a nice error, not a stack trace\ntry:\n    parser.parse_args(['create'])\nexcept SystemExit as e:\n    print('Got SystemExit (nice CLI error) as expected')\nexcept Exception as e:\n    print(f'Got unexpected exception: {type(e).__name__}: {e}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16485__JMrxQpx":{"task_name":"django__django-16485","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 21 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Grep (2), Read (1), Edit (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): defaultfilters.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test template_tests.filter_tests.test_floatformat --settings=test_sqlite 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16493__yipYNku":{"task_name":"django__django-16493","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Grep","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): files.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.core.files.storage import default_storage, FileSystemStorage, Storage\nfrom django.db.models import FileField\n\n# Case 1: callable that returns default_storage\ndef get_default():\n    return default_storage\n\nfield = FileField(storage=get_default)\nname, path, args, kwargs = field.deconstruct()\nassert 'storage' in kwargs, 'FAIL: storage kwarg missing when callable returns default_storage'\nassert kwargs['storage'] is get_default, 'FAIL: storage kwarg should be the callable'\nprint('PASS: callable returning default_storage is preserved')\n\n# Case 2: no storage argument (should NOT include storage in kwargs)\nfield2 = FileField()\nname, path, args, kwargs2 = field2.deconstruct()\nassert 'storage' not in kwargs2, 'FAIL: storage kwarg should not be present when using default'\nprint('PASS: default storage omitted correctly')\n\n# Case 3: callable that returns non-default storage\nother = FileSystemStorage(location='/tmp/other')\ndef get_other():\n    return other\n\nfield3 = FileField(storage=get_other)\nname, path, args, kwargs3 = field3.deconstruct()\nassert 'storage' in kwargs3, 'FAIL: storage kwarg missing for callable returning other storage'\nassert kwargs3['storage'] is get_other, 'FAIL: storage kwarg should be the callable'\nprint('PASS: callable returning other storage works')\n\n# Case 4: direct non-default storage\nfield4 = FileField(storage=other)\nname, path, args, kwargs4 = field4.deconstruct()\nassert 'storage' in kwargs4, 'FAIL: storage kwarg missing for direct other storage'\nassert kwargs4['storage'] is other, 'FAIL: storage kwarg should be the storage instance'\nprint('PASS: direct non-default storage works')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16502__5Hw45fi":{"task_name":"django__django-16502","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (6), Grep (2), Agent (1), Glob (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): basehttp.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test servers.test_basehttp --settings=servers.tests -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16527__t6967xw":{"task_name":"django__django-16527","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Grep","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Read","Grep"],"answer":"Read","reason":"\"Read\" was called 5 times. Full breakdown: Read (5), Bash (4), Grep (2), Edit (1), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): admin_modify.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_views.test_templatetags admin_views.tests.SaveAsTests admin_views.tests.AdminViewPermissionsTest.test_change_view_save_as_new --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16560__9Ru4h2b":{"task_name":"django__django-16560","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 112 steps but only 65 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Glob"],"answer":"Bash","reason":"\"Bash\" was called 23 times. Full breakdown: Bash (23), Edit (19), Read (15), TodoWrite (4), Agent (1), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): constraints.py, constraints.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test constraints.tests.BaseConstraintTests constraints.tests.CheckConstraintTests.test_eq constraints.tests.CheckConstraintTests.test_validate constraints.tests.CheckConstraintTests.test_validate_custom_error_code constraints.tests.UniqueConstraintTests.test_eq constraints.tests.UniqueConstraintTests.test_validate_custom_error_code_expression constraints.tests.UniqueConstraintTests.test_validate_custom_error_code_condition --settings=tests.test_sqlite --verbosity=2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16569__odGFHRW":{"task_name":"django__django-16569","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Read","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Agent","Glob"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): formsets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport os, django\nfrom django import forms\n\nclass MyForm(forms.Form):\n    my_field = forms.CharField()\n\ndjango.conf.settings.configure(DEBUG=True, MIDDLEWARE_CLASSES=[], ROOT_URLCONF='test')\ndjango.setup()\n\nMyFormSet = forms.formset_factory(form=MyForm, can_delete=True, can_delete_extra=False)\nmy_formset = MyFormSet(initial=None)\nprint(my_formset.empty_form)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16595__igziSZv":{"task_name":"django__django-16595","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): fields.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_optimizer --settings=tests.test_sqlite -v2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16612__aEqG2LD":{"task_name":"django__django-16612","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Bash","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (2), Edit (1), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sites.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_tests.test_adminsite -v2 --settings=django.test.utils 2>&1 | head -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16631__FyMqZoM":{"task_name":"django__django-16631","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (6), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): __init__.py, base_user.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test django.contrib.auth.tests.test_auth_backends django.contrib.auth.tests.test_basic -v2 --settings=tests.test_sqlite 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16642__Xe3Mu4N":{"task_name":"django__django-16642","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Write","Grep"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): response.py, test_fileresponse.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"import mimetypes; print(mimetypes.guess_type('test.html.br')); print(mimetypes.guess_type('test.html.Z'))\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16661__sxRz285":{"task_name":"django__django-16661","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Bash","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (3), Edit (3), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): options.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test tests.modeladmin.tests.ModelAdminTests.test_lookup_allowed_foreign_primary tests.modeladmin.tests.ModelAdminTests.test_lookup_allowed_onetoone tests.modeladmin.tests.ModelAdminTests.test_lookup_allowed_allows_nonexistent_lookup --settings=tests.test_sqlite --verbosity=2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16662__n893Krm":{"task_name":"django__django-16662","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (3), Edit (3), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): writer.py, test_writer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"DJANGO_SETTINGS_MODULE=tests.test_sqlite python -c \"\nimport django\ndjango.setup()\nfrom django.db.migrations import writer, operations\nfrom django.db import migrations, models\nimport uuid\n\nmigration = type(\n    'Migration',\n    (migrations.Migration,),\n    {\n        'operations': [\n            migrations.AddField(\n                'mymodel',\n                'myfield',\n                models.UUIDField(default=uuid.uuid4),\n            ),\n        ]\n    },\n)\nw = writer.MigrationWriter(migration)\noutput = w.as_string()\nfor line in output.split('\\n'):\n    if line.startswith(('import ', 'from ')):\n        print(line)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16667__sRNGBKU":{"task_name":"django__django-16667","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Read","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Glob","Edit"],"answer":"Edit","reason":"\"Edit\" was called 3 times. Full breakdown: Edit (3), Bash (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): widgets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom django.forms.widgets import SelectDateWidget\nfrom django.test import override_settings\nimport django\nfrom django.conf import settings\nsettings.configure(USE_L10N=False)\ndjango.setup()\n\nw = SelectDateWidget()\ndata = {'my_date_day': '1', 'my_date_month': '1', 'my_date_year': '1234567821345678'}\nresult = w.value_from_datadict(data, {}, 'my_date')\nprint('Result:', repr(result))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"django__django-16801__HgpYK6T":{"task_name":"django__django-16801","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Write","Glob","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Write","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): files.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_fields.test_imagefield --settings=tests.test_sqlite -v 2 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16819__z2tgU5n":{"task_name":"django__django-16819","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Bash","Edit"],"answer":"Read","reason":"\"Read\" was called 9 times. Full breakdown: Read (9), Bash (8), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, test_optimizer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/migrations/test_optimizer.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16877__tQv5uvj":{"task_name":"django__django-16877","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Glob","Agent"],"answer":"Read","reason":"\"Read\" was called 5 times. Full breakdown: Read (5), Bash (5), TodoWrite (4), Grep (3), Edit (3), Agent (1), Write (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): defaultfilters.py, builtins.txt, 5.0.txt."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test tests.template_tests.filter_tests.test_escapeseq --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16899__EddYFXw":{"task_name":"django__django-16899","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Grep","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (4), Read (4), Edit (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): checks.py, checks.txt, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test admin_checks.tests.ReadonlyFieldsCheckTests -v2 --settings=admin_checks.tests 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16901__KW7vwLR":{"task_name":"django__django-16901","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (5), Edit (3), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): where.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test xor_lookups --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16938__Agwb6M8":{"task_name":"django__django-16938","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 55 steps but only 38 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Bash","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Edit (9), Read (7), Grep (6), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): python.py, xml_serializer.py, base.py, tests.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.BigAutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import Manager\n\n# Define models\nclass TestTagManager(Manager):\n    def get_queryset(self):\n        qs = super().get_queryset()\n        qs = qs.select_related('master')\n        return qs\n\nclass TestTagMaster(models.Model):\n    name = models.CharField(max_length=120)\n    class Meta:\n        app_label = 'test_app'\n\nclass TestTag(models.Model):\n    objects = TestTagManager()\n    name = models.CharField(max_length=120)\n    master = models.ForeignKey(TestTagMaster, on_delete=models.SET_NULL, null=True)\n    class Meta:\n        app_label = 'test_app'\n\nclass Test(models.Model):\n    name = models.CharField(max_length=120)\n    tags = models.ManyToManyField(TestTag, blank=True)\n    class Meta:\n        app_label = 'test_app'\n\n# Create tables\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(TestTagMaster)\n    schema_editor.create_model(TestTag)\n    schema_editor.create_model(Test)\n    # Create the M2M through table\n    for field in Test._meta.local_many_to_many:\n        with connection.schema_editor() as schema_editor2:\n            schema_editor2.create_model(field.remote_field.through)\n\n# Create test data\ntag_master = TestTagMaster.objects.create(name='master')\ntag = TestTag.objects.create(name='tag', master=tag_master)\ntest = Test.objects.create(name='test')\ntest.tags.add(tag)\n\n# Serialize\nfrom django.core import serializers\nresult = serializers.serialize('json', [test])\nprint('JSON serialization succeeded:', result)\n\nresult = serializers.serialize('python', [test])\nprint('Python serialization succeeded:', result)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-16950__dBnFwaF":{"task_name":"django__django-16950","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Grep","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (5), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): models.py, test_uuid.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test model_formsets.test_uuid --settings=tests.test_sqlite -v2 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-17029__E9EkrUQ":{"task_name":"django__django-17029","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Grep","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Agent"],"answer":"Grep","reason":"\"Grep\" was called 4 times. Full breakdown: Grep (4), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): registry.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-17084__dCuQWpK":{"task_name":"django__django-17084","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (4), Agent (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): query.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport django\nfrom django.conf import settings\nsettings.configure(\n    DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},\n    INSTALLED_APPS=['django.contrib.contenttypes', 'django.contrib.auth'],\n    DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n)\ndjango.setup()\n\nfrom django.db import models, connection\nfrom django.db.models import Sum, F, Window\nfrom django.db.models.functions import Coalesce\n\n# Create a simple model\nclass TestModel(models.Model):\n    value = models.IntegerField()\n    date = models.DateField()\n    class Meta:\n        app_label = 'test'\n\n# Create the table\nwith connection.schema_editor() as schema_editor:\n    schema_editor.create_model(TestModel)\n\n# Insert some data\nimport datetime\nTestModel.objects.create(value=10, date=datetime.date(2023, 1, 1))\nTestModel.objects.create(value=20, date=datetime.date(2023, 1, 2))\nTestModel.objects.create(value=30, date=datetime.date(2023, 1, 3))\n\n# Test: annotate with window function, then aggregate\nqs = TestModel.objects.annotate(\n    cumul_value=Window(Sum('value'), order_by=F('date').asc())\n)\n\n# This should use a subquery now instead of inlining the window function\nresult = qs.aggregate(\n    value_total=Sum('value'),\n    cumul_value_total=Sum('cumul_value'),\n)\nprint('Result:', result)\nprint('Expected value_total: 60')\nprint('Expected cumul_value_total: 10 + 30 + 60 = 100')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-17087__pcxRPsZ":{"task_name":"django__django-17087","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): serializer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_writer --settings=tests.test_sqlite -v 0 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-7530__JyQiBcK":{"task_name":"django__django-7530","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 18 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (2), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): makemigrations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m django test migrations.test_commands --settings=tests.test_sqlite -v 2 -k \"consistency\" 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"django__django-9296__Q8Ukcsg":{"task_name":"django__django-9296","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 4 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Edit","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Grep"],"answer":"Grep","reason":"\"Grep\" was called 1 times. Full breakdown: Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): paginator.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-13989__Z9TqonH":{"task_name":"matplotlib__matplotlib-13989","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _axes.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-14623__Q5eRKyd":{"task_name":"matplotlib__matplotlib-14623","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Edit","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Grep","Glob"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): ticker.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n# Test x-axis inversion with log scale\nfig, ax = plt.subplots()\nax.plot([1, 10, 100], [1, 2, 3])\nax.set_xscale('log')\nax.set_xlim(100, 1)\nprint(f'xaxis log: xlim={ax.get_xlim()}, inverted={ax.xaxis_inverted()}')\n\n# Test symlog\nfig, ax = plt.subplots()\nax.plot([1, 10, 100], [1, 2, 3])\nax.set_yscale('symlog')\nax.set_ylim(100, 1)\nprint(f'yaxis symlog: ylim={ax.get_ylim()}, inverted={ax.yaxis_inverted()}')\n\n# Test that normal (non-inverted) log still works\nfig, ax = plt.subplots()\nax.plot([1, 10, 100], [1, 2, 3])\nax.set_yscale('log')\nax.set_ylim(1, 100)\nprint(f'yaxis log (normal): ylim={ax.get_ylim()}, inverted={ax.yaxis_inverted()}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-20488__DVxgXyu":{"task_name":"matplotlib__matplotlib-20488","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (4), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): image.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest lib/matplotlib/tests/test_image.py::test_huge_range_log -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-20676__oA5ExeS":{"task_name":"matplotlib__matplotlib-20676","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 69 steps but only 43 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Agent","Bash"],"answer":"Read","reason":"\"Read\" was called 14 times. Full breakdown: Read (14), Grep (11), Bash (10), Edit (6), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): widgets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nfrom matplotlib.widgets import SpanSelector\n\n# Test 1: horizontal interactive SpanSelector\nfig, ax = plt.subplots()\nax.plot([10, 20], [10, 20])\nxlim_before = ax.get_xlim()\nss = SpanSelector(ax, print, 'horizontal', interactive=True)\nfig.canvas.draw()\nxlim_after = ax.get_xlim()\nprint(f'Test 1 (horizontal interactive): xlim before={xlim_before}, after={xlim_after}')\nassert xlim_before == xlim_after, f'FAIL: xlim changed from {xlim_before} to {xlim_after}'\nprint('PASS')\n\n# Test 2: vertical interactive SpanSelector\nfig2, ax2 = plt.subplots()\nax2.plot([10, 20], [10, 20])\nylim_before = ax2.get_ylim()\nss2 = SpanSelector(ax2, print, 'vertical', interactive=True)\nfig2.canvas.draw()\nylim_after = ax2.get_ylim()\nprint(f'Test 2 (vertical interactive): ylim before={ylim_before}, after={ylim_after}')\nassert ylim_before == ylim_after, f'FAIL: ylim changed from {ylim_before} to {ylim_after}'\nprint('PASS')\n\n# Test 3: non-interactive should also be fine\nfig3, ax3 = plt.subplots()\nax3.plot([10, 20], [10, 20])\nxlim_before = ax3.get_xlim()\nss3 = SpanSelector(ax3, print, 'horizontal', interactive=False)\nfig3.canvas.draw()\nxlim_after = ax3.get_xlim()\nprint(f'Test 3 (non-interactive): xlim before={xlim_before}, after={xlim_after}')\nassert xlim_before == xlim_after, f'FAIL: xlim changed from {xlim_before} to {xlim_after}'\nprint('PASS')\n\n# Test 4: SpanSelector created before any data\nfig4, ax4 = plt.subplots()\nss4 = SpanSelector(ax4, print, 'horizontal', interactive=True)\nax4.plot([10, 20], [10, 20])\nfig4.canvas.draw()\nxlim = ax4.get_xlim()\nprint(f'Test 4 (selector before data): xlim={xlim}')\nassert xlim[0] > 0, f'FAIL: xlim includes 0: {xlim}'\nprint('PASS')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-20826__6PJkwg6":{"task_name":"matplotlib__matplotlib-20826","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 32 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (6), Edit (5), Grep (2), Agent (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n# Test 1: Non-shared subplots should keep all labels visible\nfig, axes = plt.subplots(2, 2)\nx = np.arange(0.0, 2*np.pi, 0.01)\ny = np.sin(x)\n\nfor ax in axes.flatten():\n    ax.clear()\n    ax.plot(x, y)\n\nfor i, ax in enumerate(axes.flatten()):\n    row, col = divmod(i, 2)\n    xmaj = ax.xaxis._major_tick_kw\n    ymaj = ax.yaxis._major_tick_kw\n    label_bottom = xmaj.get('label1On', True)\n    label_left = ymaj.get('label1On', True)\n    assert label_bottom == True, f'Non-shared ax[{row},{col}] labelbottom should be True'\n    assert label_left == True, f'Non-shared ax[{row},{col}] labelleft should be True'\n\nprint('Test 1 passed: non-shared subplots keep all labels')\n\n# Test 2: Shared subplots without clear() should work as before\nfig2, axes2 = plt.subplots(2, 2, sharex=True, sharey=True)\nfor ax in axes2.flatten():\n    ax.plot(x, y)\n\nfor i, ax in enumerate(axes2.flatten()):\n    row, col = divmod(i, 2)\n    xmaj = ax.xaxis._major_tick_kw\n    ymaj = ax.yaxis._major_tick_kw\n    is_last_row = (row == 1)\n    is_first_col = (col == 0)\n    label_bottom = xmaj.get('label1On', True)\n    label_left = ymaj.get('label1On', True)\n    assert label_bottom == is_last_row, f'No-clear ax[{row},{col}] labelbottom={label_bottom}, expected {is_last_row}'\n    assert label_left == is_first_col, f'No-clear ax[{row},{col}] labelleft={label_left}, expected {is_first_col}'\n\nprint('Test 2 passed: shared subplots without clear work correctly')\n\n# Test 3: sharex only (column sharing)\nfig3, axes3 = plt.subplots(2, 2, sharex=True)\nfor ax in axes3.flatten():\n    ax.clear()\n    ax.plot(x, y)\n\nfor i, ax in enumerate(axes3.flatten()):\n    row, col = divmod(i, 2)\n    xmaj = ax.xaxis._major_tick_kw\n    is_last_row = (row == 1)\n    label_bottom = xmaj.get('label1On', True)\n    assert label_bottom == is_last_row, f'sharex-only ax[{row},{col}] labelbottom={label_bottom}, expected {is_last_row}'\n    # y labels should all be visible\n    ymaj = ax.yaxis._major_tick_kw\n    label_left = ymaj.get('label1On', True)\n    assert label_left == True, f'sharex-only ax[{row},{col}] labelleft should be True'\n\nprint('Test 3 passed: sharex-only works correctly')\n\nprint('All edge case tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-20859__3cMpUr4":{"task_name":"matplotlib__matplotlib-20859","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Grep","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Write"],"answer":"Grep","reason":"\"Grep\" was called 3 times. Full breakdown: Grep (3), Bash (3), Edit (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): legend.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nsubfig = plt.figure().subfigures()\nax = subfig.subplots()\nax.plot([0, 1, 2], [0, 1, 2], label='test')\nsubfig.legend()\nprint('Success: legend on SubFigure works')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-21568__kxrxo7q":{"task_name":"matplotlib__matplotlib-21568","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (4), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): dates.py, test_dates.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom matplotlib.dates import _wrap_in_tex\n\n# Test time strings\nprint('04:00  ->', repr(_wrap_in_tex('04:00')))\nprint('00:02  ->', repr(_wrap_in_tex('00:02')))\nprint('14:30  ->', repr(_wrap_in_tex('14:30')))\n\n# Test date strings with dashes\nprint('2020-01 ->', repr(_wrap_in_tex('2020-01')))\n\n# Test mixed\nprint('Jan 01 ->', repr(_wrap_in_tex('Jan 01')))\nprint('00:02:30 ->', repr(_wrap_in_tex('00:02:30')))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-22719__YCEmY2B":{"task_name":"matplotlib__matplotlib-22719","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Glob","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Write","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (3), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): category.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -W error -c \"\nimport matplotlib.pyplot as plt\nf, ax = plt.subplots()\nax.xaxis.update_units(['a', 'b'])\nax.plot([], [])\nprint('Test 1 passed: ax.plot([], []) with category units works')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-22865__dn9VsJa":{"task_name":"matplotlib__matplotlib-22865","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Write","Glob"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): colorbar.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib as mpl\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import from_levels_and_colors\n\nmpl.use('Agg')\n\nmy_cmap = mpl.cm.viridis\nbounds = np.arange(10)\nnb_colors = len(bounds) + 1\ncolors = my_cmap(np.linspace(100, 255, nb_colors).astype(int))\nmy_cmap, my_norm = from_levels_and_colors(bounds, colors, extend='both')\n\nplt.figure(figsize=(5, 1))\nax = plt.subplot(111)\ncbar = mpl.colorbar.ColorbarBase(ax, cmap=my_cmap, norm=my_norm, orientation='horizontal', drawedges=True)\nplt.subplots_adjust(left=0.05, bottom=0.4, right=0.95, top=0.9)\nplt.savefig('/testbed/test_drawedges.png')\nprint('Success - saved test_drawedges.png')\n\n# Also test all extend modes\nfor ext in ['neither', 'min', 'max', 'both']:\n    fig, ax = plt.subplots(figsize=(5, 1))\n    my_cmap2, my_norm2 = from_levels_and_colors(bounds, colors, extend=ext)\n    cbar = mpl.colorbar.ColorbarBase(ax, cmap=my_cmap2, norm=my_norm2, orientation='horizontal', drawedges=True)\n    plt.savefig(f'/testbed/test_drawedges_{ext}.png')\n    print(f'  extend={ext!r} OK')\n\n# Test without drawedges (should still work)\nfig, ax = plt.subplots(figsize=(5, 1))\nmy_cmap3, my_norm3 = from_levels_and_colors(bounds, colors, extend='both')\ncbar = mpl.colorbar.ColorbarBase(ax, cmap=my_cmap3, norm=my_norm3, orientation='horizontal', drawedges=False)\nplt.savefig('/testbed/test_no_drawedges.png')\nprint('  drawedges=False OK')\n\n# Test vertical orientation too\nfig, ax = plt.subplots(figsize=(1, 5))\nmy_cmap4, my_norm4 = from_levels_and_colors(bounds, colors, extend='both')\ncbar = mpl.colorbar.ColorbarBase(ax, cmap=my_cmap4, norm=my_norm4, orientation='vertical', drawedges=True)\nplt.savefig('/testbed/test_drawedges_vertical.png')\nprint('  vertical + drawedges=True OK')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-22871__4i3UXRh":{"task_name":"matplotlib__matplotlib-22871","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Bash","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dates.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nfrom datetime import datetime, timedelta\n\n# Reproduce the bug: <12 months, no January\ninitial = datetime(2021, 2, 14, 0, 0, 0)\ntime_array = [initial + timedelta(days=x) for x in range(1, 200)]\ndata = [-x**2/20000 for x in range(1, 200)]\n\nfig, ax = plt.subplots()\nax.plot(time_array, data)\n\nlocator = mdates.AutoDateLocator()\nformatter = mdates.ConciseDateFormatter(locator)\nax.xaxis.set_major_locator(locator)\nax.xaxis.set_major_formatter(formatter)\n\nfig.canvas.draw()\n\nprint('Offset string:', repr(formatter.get_offset()))\nprint('Tick labels:', [t.get_text() for t in ax.xaxis.get_ticklabels()])\n\n# Also test level==0 (years vary) still suppresses offset\ninitial2 = datetime(2019, 1, 1)\ntime_array2 = [initial2 + timedelta(days=x*120) for x in range(1, 20)]\ndata2 = list(range(1, 20))\n\nfig2, ax2 = plt.subplots()\nax2.plot(time_array2, data2)\nlocator2 = mdates.AutoDateLocator()\nformatter2 = mdates.ConciseDateFormatter(locator2)\nax2.xaxis.set_major_locator(locator2)\nax2.xaxis.set_major_formatter(formatter2)\nfig2.canvas.draw()\nprint()\nprint('Multi-year offset string:', repr(formatter2.get_offset()))\nprint('Multi-year tick labels:', [t.get_text() for t in ax2.xaxis.get_ticklabels()])\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-23299__mWZ9MLJ":{"task_name":"matplotlib__matplotlib-23299","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib import get_backend, rc_context\n\n# Test 1: Original bug reproduction\nwith rc_context():\n    fig2 = plt.figure()\nbefore = f'{id(plt._pylab_helpers.Gcf)} {plt._pylab_helpers.Gcf.figs!r}'\nget_backend()\nafter = f'{id(plt._pylab_helpers.Gcf)} {plt._pylab_helpers.Gcf.figs!r}'\nassert before == after, '\\n' + before + '\\n' + after\nprint('PASS: Test 1 - figures preserved after get_backend()')\n\n# Test 2: rc_context that explicitly changes backend should still work\nplt.close('all')\nwith rc_context({'backend': 'agg'}):\n    fig3 = plt.figure()\nprint(f'PASS: Test 2 - explicit backend in rc_context works, backend={get_backend()}')\n\n# Test 3: Figure created before rc_context should survive\nplt.close('all')\nfig1 = plt.figure()\nwith rc_context():\n    fig2 = plt.figure()\nget_backend()\nassert len(plt._pylab_helpers.Gcf.figs) == 2, f'Expected 2 figs, got {len(plt._pylab_helpers.Gcf.figs)}'\nprint('PASS: Test 3 - both figures preserved')\n\n# Test 4: close still works after the fix\nplt.close(fig2)\nassert len(plt._pylab_helpers.Gcf.figs) == 1, f'Expected 1 fig, got {len(plt._pylab_helpers.Gcf.figs)}'\nprint('PASS: Test 4 - plt.close(fig) works correctly')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-23314__Z6jQATD":{"task_name":"matplotlib__matplotlib-23314","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Edit","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Agent (1), Edit (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): axes3d.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nfig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw={'projection': '3d'})\nax1.scatter(1,1,1)\nax2.scatter(1,1,1, c='r')\nax1.set_visible(False)\n\nfig.savefig('/tmp/test_3d_visible.png')\nprint('Saved successfully')\n\n# Verify ax1 is not visible and ax2 is\nprint('ax1 visible:', ax1.get_visible())\nprint('ax2 visible:', ax2.get_visible())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-23412__fFPaWYh":{"task_name":"matplotlib__matplotlib-23412","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): patches.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nfig, ax = plt.subplots(figsize=(10,10))\n# Red with offset 0, blue with offset 10 - they should NOT overlap\nax.add_patch(mpl.patches.Rectangle((0.5,0.5),1,1, alpha=0.5, edgecolor='r', linewidth=4, ls=(0,(10,10))))\nax.add_patch(mpl.patches.Rectangle((0.5,0.5),1,1, alpha=0.5, edgecolor='b', linewidth=4, ls=(10,(10,10))))\nax.set_ylim([0,2])\nax.set_xlim([0,2])\nfig.savefig('/testbed/test_offset.png')\nprint('Saved test_offset.png successfully')\n\n# Verify the dash patterns are stored correctly with offsets\np1 = mpl.patches.Rectangle((0,0),1,1, ls=(0,(10,10)))\np2 = mpl.patches.Rectangle((0,0),1,1, ls=(10,(10,10)))\nprint(f'Patch 1 dash pattern: {p1._dash_pattern}')\nprint(f'Patch 2 dash pattern: {p2._dash_pattern}')\nassert p1._dash_pattern[0] != p2._dash_pattern[0], 'Offsets should differ'\nprint('Offsets are correctly different')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-23476__9cZs8DX":{"task_name":"matplotlib__matplotlib-23476","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): figure.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest lib/matplotlib/tests/test_pickle.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24026__Jwy8CRN":{"task_name":"matplotlib__matplotlib-24026","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Bash","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Edit (3), Read (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): stackplot.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport numpy\n\n# Test 1: CN color aliases (the reported bug)\nmy_data = numpy.array([[1, 1, 1], [1, 2, 3], [4, 3, 2]])\nfig, ax = plt.subplots()\nax.plot([1, 3], [1, 3], color='C0')\nax.add_patch(Rectangle(xy=(1.5, 1.5), width=0.5, height=0.5, facecolor='C1'))\nax.stackplot([1, 2, 3], my_data, colors=['C2', 'C3', 'C4'])\nprint('Test 1 (CN aliases): PASSED')\n\n# Test 2: stackplot should not change Axes cycler\nfig2, ax2 = plt.subplots()\nax2.plot([1, 2], [1, 2])  # uses C0\nax2.stackplot([1, 2, 3], my_data, colors=['red', 'green', 'blue'])\nax2.plot([1, 2], [3, 3])  # should use C1, not be affected by stackplot\nline = ax2.get_lines()[-1]\nprint(f'Test 2 (cycler not changed): color after stackplot = {line.get_color()}')\n\n# Test 3: default colors (no colors arg) still work\nfig3, ax3 = plt.subplots()\nresult = ax3.stackplot([1, 2, 3], my_data)\nprint(f'Test 3 (default colors): {len(result)} polycollections created')\n\n# Test 4: color cycling when fewer colors than data series\nfig4, ax4 = plt.subplots()\nresult = ax4.stackplot([1, 2, 3], my_data, colors=['red', 'blue'])\nfacecolors = [c.get_facecolor()[0].tolist() for c in result]\nprint(f'Test 4 (color cycling): 3 series with 2 colors = {len(result)} polycollections')\n\n# Test 5: named colors\nfig5, ax5 = plt.subplots()\nresult = ax5.stackplot([1, 2, 3], my_data, colors=['red', 'green', 'blue'])\nprint(f'Test 5 (named colors): PASSED')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24149__GXzQ9aA":{"task_name":"matplotlib__matplotlib-24149","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Read","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _axes.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Test case from the bug report\nf, ax = plt.subplots()\nresult = ax.bar([np.nan], [np.nan])\nprint('bar([np.nan], [np.nan]):', result)\nprint('x:', result[0].get_x())\nprint('height:', result[0].get_height())\n\n# Additional test cases\nf2, ax2 = plt.subplots()\nresult2 = ax2.bar([np.nan], [0])\nprint('bar([np.nan], [0]):', result2)\n\n# Normal case still works\nf3, ax3 = plt.subplots()\nresult3 = ax3.bar([1, 2], [3, 4])\nprint('bar([1,2], [3,4]):', result3)\n\n# Mixed nan case\nf4, ax4 = plt.subplots()\nresult4 = ax4.bar([np.nan, 1], [2, 3])\nprint('bar([np.nan,1], [2,3]):', result4)\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24177__pBe6oxL":{"task_name":"matplotlib__matplotlib-24177","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 65 steps but only 42 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 21 times. Full breakdown: Bash (21), Read (10), Grep (7), Agent (1), TaskOutput (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nnp.random.seed(42)\nscale = 1.2\ntest_random = np.random.randn(100000) * scale\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 10))\nhist_bar = ax[0].hist(test_random, bins=100, density=True, histtype='bar')\nhist_step = ax[1].hist(test_random, bins=100, density=True, histtype='step')\n\nprint('Bar ylim:', ax[0].get_ylim())\nprint('Step ylim:', ax[1].get_ylim())\nprint('Bar max density:', max(hist_bar[0]))\nprint('Step max density:', max(hist_step[0]))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24570__ZxGyM57":{"task_name":"matplotlib__matplotlib-24570","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Edit","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Grep (3), Read (3), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): offsetbox.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\nfrom matplotlib.offsetbox import DrawingArea, HPacker, VPacker, AnchoredOffsetbox, TextArea\nfrom matplotlib.patches import Rectangle\n\nda1 = DrawingArea(10, 20)\nrect1 = Rectangle((0, 0), 10, 20)\nda1.add_artist(rect1)\n\nda2 = DrawingArea(10, 30)\nrect2 = Rectangle((0, 0), 10, 30)\nda2.add_artist(rect2)\n\n# Test bottom alignment - short box should be at bottom\nalign = 'bottom'\npack = HPacker(children=[da1, da2], pad=10, sep=10, align=align)\nrenderer = plt.gcf().canvas.get_renderer()\nw, h, xd, yd, offsets = pack.get_extent_offsets(renderer)\nprint(f'align={align}: offsets={offsets}')\n# bottom align: both boxes should have y-offset based on descent (0), so short box at bottom\n# da1 (h=20, d=0) offset should be d=0\n# da2 (h=30, d=0) offset should be d=0\n\n# Test top alignment - short box should be at top  \nalign = 'top'\npack = HPacker(children=[da1, da2], pad=10, sep=10, align=align)\nw, h, xd, yd, offsets = pack.get_extent_offsets(renderer)\nprint(f'align={align}: offsets={offsets}')\n# top align: boxes should be pushed to top\n# da1 (h=20, d=0) offset should be height-h+d = 30-20+0 = 10\n# da2 (h=30, d=0) offset should be height-h+d = 30-30+0 = 0\n\nprint('Looks correct!')\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24627__Wig3PsN":{"task_name":"matplotlib__matplotlib-24627","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 93 steps but only 62 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["EnterPlanMode","Read","Write","Edit"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 21 times. Full breakdown: Bash (21), Read (18), Grep (8), TodoWrite (5), Agent (3), Edit (3), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): _base.py, figure.py, test_artist.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\n\n# Test 1: cla() should unset .axes\nf, a = plt.subplots(); l, = a.plot([1, 2]); a.cla(); print('After cla(), l.axes =', l.axes)\n\n# Test 2: remove() should unset .axes (existing behavior)\nf, a = plt.subplots(); l, = a.plot([1, 2]); l.remove(); print('After remove(), l.axes =', l.axes)\n\n# Test 3: clf() should unset .figure\nf, a = plt.subplots(); l, = a.plot([1, 2]); f.clf(); print('After clf(), l.figure =', l.figure)\nprint('After clf(), l.axes =', l.axes)\nprint('After clf(), a.figure =', a.figure)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24637__qPjJ7ZX":{"task_name":"matplotlib__matplotlib-24637","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): offsetbox.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\nfrom matplotlib.offsetbox import AnnotationBbox, TextArea\nimport io\n\nfig, ax = plt.subplots()\nab = AnnotationBbox(TextArea('test'), (0.5, 0.5),\n                    xycoords='data',\n                    boxcoords='offset points')\nab.set_gid('my_test_gid')\nax.add_artist(ab)\n\nbuf = io.BytesIO()\nfig.savefig(buf, format='svg')\nsvg = buf.getvalue().decode('utf-8')\n\nif 'my_test_gid' in svg:\n    print('SUCCESS: gid found in SVG output')\nelse:\n    print('FAIL: gid not found in SVG output')\nplt.close()\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-24870__wdtdBxe":{"task_name":"matplotlib__matplotlib-24870","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): contour.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.contour import QuadContourSet\n\n# Test boolean array auto-detection\nii, jj = np.ogrid[:100, :100]\nim = (ii + jj) % 20 < 10  # boolean array\n\nfig, ax = plt.subplots()\ncs = ax.contour(im)\nprint('levels for bool input:', cs.levels)\nassert list(cs.levels) == [0.5], f'Expected [0.5], got {cs.levels}'\n\n# Test that explicit levels still work with bool input\ncs2 = ax.contour(im, levels=[0.25, 0.75])\nprint('levels with explicit levels:', cs2.levels)\nassert list(cs2.levels) == [0.25, 0.75]\n\n# Test that explicit N levels still work with bool input\ncs3 = ax.contour(im, 3)\nprint('levels with N=3:', cs3.levels)\n\n# Test that non-bool input is unchanged\nim_float = im.astype(float)\ncs4 = ax.contour(im_float)\nprint('levels for float input:', cs4.levels)\nassert len(cs4.levels) > 1, 'Float input should have default multi-level behavior'\n\n# Test contourf with bool input\ncs5 = ax.contourf(im)\nprint('contourf levels for bool input:', cs5.levels)\nassert list(cs5.levels) == [0.5], f'Expected [0.5], got {cs5.levels}'\n\nprint()\nprint('All tests passed!')\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-24970__8JFqvDa":{"task_name":"matplotlib__matplotlib-24970","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Glob","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (3), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): colors.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -W error -c \"\nimport matplotlib.pyplot as plt\nimport numpy as np\nplt.get_cmap()(np.empty((0, ), dtype=np.uint8))\nprint('Empty array: OK')\n\n# Also test with actual uint8 data\nresult = plt.get_cmap()(np.array([0, 128, 255], dtype=np.uint8))\nprint('uint8 data:', result.shape, 'OK')\n\n# Test that float path still works\nresult = plt.get_cmap()(np.array([0.0, 0.5, 1.0]))\nprint('float data:', result.shape, 'OK')\n\n# Test with int (non-uint8) data\nresult = plt.get_cmap()(np.array([0, 128, 255], dtype=np.int32))\nprint('int32 data:', result.shape, 'OK')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-25122__4A4ZVSh":{"task_name":"matplotlib__matplotlib-25122","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Grep","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): mlab.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\n\n# Simulate what flattop window looks like (has negative values)\n# Using a simple window with negative values to test\nwindow = np.array([0.0, -0.05, 0.5, 1.0, 0.5, -0.05, 0.0])\n\n# The bug: np.abs changes the result for windows with negative values\nprint('With np.abs (old, buggy):')\nprint('  density:  ', (np.abs(window)**2).sum())\nprint('  spectrum: ', np.abs(window).sum()**2)\n\nprint('Without np.abs (new, correct):')\nprint('  density:  ', (window**2).sum())\nprint('  spectrum: ', window.sum()**2)\n\nprint()\nprint('Difference in density:', (np.abs(window)**2).sum() - (window**2).sum())\nprint('Difference in spectrum:', np.abs(window).sum()**2 - window.sum()**2)\nprint('Both differences should be 0 for windows without negative values')\nprint('For flattop (which has negatives), np.abs gives wrong results')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-25287__7G783iq":{"task_name":"matplotlib__matplotlib-25287","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Edit","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (2), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): axis.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# Test 1: labelcolor set explicitly\nplt.rcParams.update({'ytick.labelcolor': 'red'})\nfig = plt.figure()\nax = fig.add_subplot(1,1,1)\nax.plot([1.01e9, 1.02e9, 1.03e9])\nfig.canvas.draw()\ncolor = ax.yaxis.offsetText.get_color()\nprint(f'Test 1 (ytick.labelcolor=red): offset text color = {color}')\nassert color == 'red', f'Expected red, got {color}'\nplt.close()\n\n# Test 2: labelcolor='inherit' (default) should use tick color\nplt.rcParams.update({'ytick.labelcolor': 'inherit', 'ytick.color': 'blue'})\nfig = plt.figure()\nax = fig.add_subplot(1,1,1)\nax.plot([1.01e9, 1.02e9, 1.03e9])\nfig.canvas.draw()\ncolor = ax.yaxis.offsetText.get_color()\nprint(f'Test 2 (ytick.labelcolor=inherit, ytick.color=blue): offset text color = {color}')\nassert color == 'blue', f'Expected blue, got {color}'\nplt.close()\n\n# Test 3: xtick labelcolor\nplt.rcParams.update({'xtick.labelcolor': 'green'})\nfig = plt.figure()\nax = fig.add_subplot(1,1,1)\nax.plot([1.01e9, 1.02e9, 1.03e9])\nfig.canvas.draw()\ncolor = ax.xaxis.offsetText.get_color()\nprint(f'Test 3 (xtick.labelcolor=green): offset text color = {color}')\nassert color == 'green', f'Expected green, got {color}'\nplt.close()\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-25311__cDRT5UV":{"task_name":"matplotlib__matplotlib-25311","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 72 steps but only 42 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (11), Edit (8), Grep (7), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): figure.py, legend.py, offsetbox.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"sed -n '3179,3210p' /testbed/lib/matplotlib/figure.py\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-25332__cPDYeCU":{"task_name":"matplotlib__matplotlib-25332","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 34 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): cbook.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\nimport pickle\n\nfig = plt.figure()\nax1 = fig.add_subplot(211)\nax2 = fig.add_subplot(212)\ntime=[0,1,2,3,4]\nspeed=[40000,4300,4500,4700,4800]\nacc=[10,11,12,13,14]\nax1.plot(time,speed)\nax1.set_ylabel('speed')\nax2.plot(time,acc)\nax2.set_ylabel('acc')\n\nfig.align_labels()\n\n# Test pickle round-trip\nfig2 = pickle.loads(pickle.dumps(fig))\nprint('Pickle round-trip succeeded')\n\n# Verify the align groups survived\ngrouper_x = fig2._align_label_groups['x']\ngrouper_y = fig2._align_label_groups['y']\nprint(f'x groups: {list(grouper_x)}')\nprint(f'y groups: {list(grouper_y)}')\n\n# Verify the axes are still grouped\naxes = fig2.get_axes()\nprint(f'y-label siblings of ax1: {len(grouper_y.get_siblings(axes[0]))}')\nprint(f'Axes joined: {grouper_y.joined(axes[0], axes[1])}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-25479__yAnyeaC":{"task_name":"matplotlib__matplotlib-25479","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 41 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Edit (3), Read (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): cm.py, test_colors.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom matplotlib import cm\nfrom matplotlib.colors import LinearSegmentedColormap\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nmy_cmap_data = [[1.5e-03, 4.7e-04, 1.4e-02],\n                [2.3e-03, 1.3e-03, 1.8e-02],\n                [3.3e-03, 2.3e-03, 2.4e-02]]\nmy_cmap = LinearSegmentedColormap.from_list('some_cmap_name', my_cmap_data)\ncm.register_cmap(name='my_cmap_name', cmap=my_cmap)\n\n# Verify the registered cmap's .name matches the registered name\nregistered = cm.get_cmap('my_cmap_name')\nprint(f'registered cmap .name: {registered.name}')\nassert registered.name == 'my_cmap_name', f'Expected my_cmap_name, got {registered.name}'\n\n# Verify original cmap is not mutated\nprint(f'original cmap .name: {my_cmap.name}')\nassert my_cmap.name == 'some_cmap_name', f'Original should be unchanged'\n\n# Test the full workflow from the bug report\nplt.set_cmap('my_cmap_name')\nplt.imshow([[1, 1], [2, 2]])\nprint('Success! plt.imshow worked after set_cmap with mismatched names.')\nplt.close()\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-25775__fppRDfh":{"task_name":"matplotlib__matplotlib-25775","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 85 steps but only 53 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Edit","Agent"],"answer":"Read","reason":"\"Read\" was called 15 times. Full breakdown: Read (15), Bash (13), Edit (12), Grep (6), TodoWrite (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 5 file(s): _mathtext.py, backend_agg.py, backend_cairo.py, mathtext.py, text.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest lib/matplotlib/tests/test_text.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-25960__AnC9ywR":{"task_name":"matplotlib__matplotlib-25960","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 83 steps but only 52 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Grep"],"answer":"Read","reason":"\"Read\" was called 19 times. Full breakdown: Read (19), Bash (15), Grep (13), Edit (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): figure.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Test 1: basic wspace/hspace=0 vs large values\nfig1 = plt.figure()\nsfs = fig1.subfigures(2, 2, wspace=0, hspace=0)\npositions_no_space = []\nfor sf in sfs.flat:\n    sf._redo_transform_rel_fig()\n    positions_no_space.append(sf.bbox_relative.extents)\nprint('No space:', positions_no_space)\n\nfig2 = plt.figure()\nsfs2 = fig2.subfigures(2, 2, wspace=1, hspace=1)\npositions_with_space = []\nfor sf in sfs2.flat:\n    sf._redo_transform_rel_fig()\n    positions_with_space.append(sf.bbox_relative.extents)\nprint('With space:', positions_with_space)\n\n# They should be different\nassert positions_no_space != positions_with_space, 'FAIL: positions should differ!'\nprint('PASS: wspace/hspace affect subfigure positions')\n\n# Test 2: wspace=0, hspace=0 should tile the full figure\nfor ext in positions_no_space:\n    pass  # Just checking they're valid\nprint('No-space subfigs cover: x0_min={}, y0_min={}, x1_max={}, y1_max={}'.format(\n    min(e[0] for e in positions_no_space),\n    min(e[1] for e in positions_no_space),\n    max(e[2] for e in positions_no_space),\n    max(e[3] for e in positions_no_space),\n))\n\n# Test 3: width_ratios still work\nfig3 = plt.figure()\nsfs3 = fig3.subfigures(1, 2, wspace=0, width_ratios=[2, 1])\npositions_ratios = []\nfor sf in sfs3.flat:\n    sf._redo_transform_rel_fig()\n    positions_ratios.append(sf.bbox_relative.extents)\nprint('Ratios [2,1]:', positions_ratios)\n# First should be 2x wider than second\nw0 = positions_ratios[0][2] - positions_ratios[0][0]\nw1 = positions_ratios[1][2] - positions_ratios[1][0]\nassert abs(w0 / w1 - 2.0) < 1e-10, f'FAIL: ratio should be 2:1, got {w0}:{w1}'\nprint('PASS: width_ratios still work correctly')\n\n# Test 4: single subfigure should always fill the space\nfig4 = plt.figure()\nsf4 = fig4.subfigures(1, 1, wspace=0.5, hspace=0.5)\nsf4._redo_transform_rel_fig()\next = sf4.bbox_relative.extents\nprint('Single subfig extents:', ext)\nassert abs(ext[0]) < 1e-10 and abs(ext[1]) < 1e-10, f'FAIL: expected (0,0), got ({ext[0]},{ext[1]})'\nassert abs(ext[2] - 1.0) < 1e-10 and abs(ext[3] - 1.0) < 1e-10, f'FAIL: expected (1,1), got ({ext[2]},{ext[3]})'\nprint('PASS: single subfigure fills entire space')\n\nprint()\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-26113__yxjcosR":{"task_name":"matplotlib__matplotlib-26113","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 15 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Grep","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Grep (2), Read (1), Edit (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _axes.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-26208__7ZgYAY2":{"task_name":"matplotlib__matplotlib-26208","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Bash","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Agent"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (2), Agent (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest lib/matplotlib/tests/test_axes.py -x -q -k \"relim or twinx or twin or stackplot\" 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"matplotlib__matplotlib-26291__e9vsbCy":{"task_name":"matplotlib__matplotlib-26291","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Agent (1), Edit (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): inset_locator.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\n\nfig, (ax, ax2) = plt.subplots(1, 2, figsize=[5.5, 2.8])\naxins = inset_axes(ax, width=1.3, height=0.9)\nfig.savefig('/tmp/test_inset.png', bbox_inches='tight')\nprint('Success!')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-26342__ZqBdh2s":{"task_name":"matplotlib__matplotlib-26342","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (4), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): contour.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Create a contour set\nx = np.linspace(-3, 3, 50)\ny = np.linspace(-3, 3, 50)\nX, Y = np.meshgrid(x, y)\nZ = np.sin(X) * np.cos(Y)\n\nfig, ax = plt.subplots()\ncs = ax.contour(X, Y, Z)\n\n# Test get_paths\npaths = cs.get_paths()\nprint(f'Number of paths: {len(paths)}')\nprint(f'Path type: {type(paths[0])}')\n\n# Test set_paths\ncs.set_paths(paths)\nprint(f'set_paths works: {cs.get_paths() is paths}')\nprint(f'stale after set_paths: {cs.stale}')\n\n# Test with transformed paths (simulating Cartopy's use case)\nnew_paths = list(paths)  # copy the list\ncs.set_paths(new_paths)\nprint(f'set_paths with new list works: {cs.get_paths() is new_paths}')\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"matplotlib__matplotlib-26466__hM9scqQ":{"task_name":"matplotlib__matplotlib-26466","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Edit","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Grep (6), Read (4), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): text.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e /testbed 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"mwaskom__seaborn-3069__npFH6Ea":{"task_name":"mwaskom__seaborn-3069","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 32 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 5 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["EnterPlanMode","Edit","Read","Bash"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 5 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Edit","Bash"],"answer":"Read","reason":"\"Read\" was called 11 times. Full breakdown: Read (11), Agent (5), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"mwaskom__seaborn-3187__qYS2Ata":{"task_name":"mwaskom__seaborn-3187","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 56 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Bash","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (12), Grep (4), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): plot.py, scales.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport seaborn as sns\nimport seaborn.objects as so\nimport matplotlib\nmatplotlib.use('Agg')\n\npenguins = sns.load_dataset('penguins')\npenguins['body_mass_mg'] = penguins['body_mass_g'] * 1000\n\n# Test with objects API\nfrom seaborn._core.scales import Continuous\nfrom seaborn._core.properties import Pointsize\nimport numpy as np\n\n# Quick test via the scale setup\nprop = Pointsize()\ns = Continuous()\ndata = penguins['body_mass_mg'].dropna()\ns = s._setup(data, prop)\nprint('Legend values:', s._legend[0][:3])\nprint('Legend labels:', s._legend[1][:3])\nprint('Legend title:', repr(s._legend_title))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pallets__flask-5014__FJ2F4wg":{"task_name":"pallets__flask-5014","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Glob","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): blueprints.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"from flask import Blueprint; bp = Blueprint('test', __name__); print(f'Created blueprint: {bp.name}')\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"psf__requests-1142__38hVPZP":{"task_name":"psf__requests-1142","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Edit","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Glob"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport requests\n\n# Test GET request - should NOT have Content-Length\nreq = requests.Request('GET', 'http://example.com')\nprepared = req.prepare()\nprint('GET headers:', dict(prepared.headers))\nassert 'Content-Length' not in prepared.headers, 'GET should not have Content-Length'\n\n# Test POST with no body - should NOT have Content-Length  \nreq = requests.Request('POST', 'http://example.com')\nprepared = req.prepare()\nprint('POST (no body) headers:', dict(prepared.headers))\nassert 'Content-Length' not in prepared.headers, 'POST with no body should not have Content-Length'\n\n# Test POST with body - SHOULD have Content-Length\nreq = requests.Request('POST', 'http://example.com', data='hello')\nprepared = req.prepare()\nprint('POST (with body) headers:', dict(prepared.headers))\nassert prepared.headers['Content-Length'] == '5', 'POST with body should have Content-Length=5'\n\n# Test POST with empty body - SHOULD have Content-Length=0\nreq = requests.Request('POST', 'http://example.com', data='')\nprepared = req.prepare()\nprint('POST (empty body) headers:', dict(prepared.headers))\nassert prepared.headers['Content-Length'] == '0', 'POST with empty string body should have Content-Length=0'\n\nprint('\\nAll tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"psf__requests-1724__SLyqqSq":{"task_name":"psf__requests-1724","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Read","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Write","Bash"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Grep (2), Edit (1), Bash (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-1766__5c9GNvj":{"task_name":"psf__requests-1766","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 7 steps but only 4 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Agent","Read"],"answer":"Grep","reason":"\"Grep\" was called 1 times. Full breakdown: Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): auth.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-1921__Efezoh8":{"task_name":"psf__requests-1921","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 18 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sessions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\n# Test merge_setting directly without importing the full requests package\nimport sys\nsys.path.insert(0, '/testbed')\n\nfrom collections import OrderedDict\n\n# Inline the function to test\nclass CaseInsensitiveDict(dict):\n    def __setitem__(self, key, value):\n        super().__setitem__(key.lower(), value)\n    def __getitem__(self, key):\n        return super().__getitem__(key.lower())\n    def __delitem__(self, key):\n        super().__delitem__(key.lower())\n    def __contains__(self, key):\n        return super().__contains__(key.lower())\n\ndef to_key_val_list(value):\n    if isinstance(value, dict):\n        return list(value.items())\n    return value\n\ndef merge_setting(request_setting, session_setting, dict_class=OrderedDict):\n    if session_setting is None:\n        return request_setting\n    if request_setting is None:\n        return session_setting\n    if not (isinstance(session_setting, dict) and isinstance(request_setting, dict)):\n        return request_setting\n    merged_setting = dict_class(to_key_val_list(session_setting))\n    merged_setting.update(to_key_val_list(request_setting))\n    # Remove keys that are set to None.\n    for (k, v) in list(merged_setting.items()):\n        if v is None:\n            del merged_setting[k]\n    return merged_setting\n\n# Test 1: session header set to None\nsession_headers = {'User-Agent': 'python-requests', 'Accept-Encoding': None, 'Accept': '*/*'}\nmerged = merge_setting({}, session_headers, dict_class=CaseInsensitiveDict)\nprint('Test 1 - session None:', dict(merged))\nassert 'accept-encoding' not in merged\n\n# Test 2: request header set to None (existing behavior) \nsession_headers2 = {'User-Agent': 'python-requests', 'Accept-Encoding': 'gzip', 'Accept': '*/*'}\nmerged2 = merge_setting({'Accept-Encoding': None}, session_headers2, dict_class=CaseInsensitiveDict)\nprint('Test 2 - request None:', dict(merged2))\nassert 'accept-encoding' not in merged2\n\n# Test 3: both set, request wins\nmerged3 = merge_setting({'Accept-Encoding': 'br'}, session_headers2, dict_class=CaseInsensitiveDict)\nprint('Test 3 - request override:', dict(merged3))\nassert merged3['accept-encoding'] == 'br'\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-2317__EjakCwQ":{"task_name":"psf__requests-2317","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Agent","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Agent","Read"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Edit (2), Bash (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sessions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom requests import Session\ns = Session()\n# Verify that bytes method gets decoded properly\nreq = s.prepare_request(s.request.__func__.__code__ and type('R', (), {'method': b'GET', 'url': 'http://example.com', 'headers': {}, 'files': None, 'data': None, 'json': None, 'params': {}, 'auth': None, 'cookies': None, 'hooks': None})())\nprint('Testing method handling...')\n\n# Direct test: simulate what the session.request method does\nmethod = b'GET'\nif isinstance(method, bytes):\n    method = method.decode('utf-8')\nprint(f'bytes b\\\"GET\\\" -> {repr(method)}')\n\nmethod = 'GET'\nfrom requests.compat import builtin_str\nif isinstance(method, bytes):\n    method = method.decode('utf-8')\nelse:\n    method = builtin_str(method)\nprint(f'str \\\"GET\\\" -> {repr(method)}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-2931__Uo9v7Td":{"task_name":"psf__requests-2931","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Read","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (3), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom requests.models import PreparedRequest\n# Test that binary payload with non-ASCII bytes doesn't crash\ndata = u'\u00f6\u00f6\u00f6'.encode('utf-8')\nresult = PreparedRequest._encode_params(data)\nassert result == data, f'Expected {data!r}, got {result!r}'\nprint('Binary payload: OK')\n\n# Test that str payload still works\nresult2 = PreparedRequest._encode_params('hello')\nassert result2 == 'hello', f'Expected \\\"hello\\\", got {result2!r}'\nprint('String payload: OK')\n\n# Test that dict/list params still work\nresult3 = PreparedRequest._encode_params({'key': 'value'})\nassert result3 == 'key=value', f'Expected \\\"key=value\\\", got {result3!r}'\nprint('Dict payload: OK')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-5414__EJ467NG":{"task_name":"psf__requests-5414","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Glob","Bash"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Edit","Glob"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Bash (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): models.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"psf__requests-6028__6sx8EyD":{"task_name":"psf__requests-6028","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 68 steps but only 43 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Glob"],"answer":"Bash","reason":"\"Bash\" was called 20 times. Full breakdown: Bash (20), Read (13), Grep (6), Agent (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): utils.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom requests.utils import prepend_scheme_if_needed\n\n# Test various proxy URL formats\nurls = [\n    'http://user:pass@proxy:8080',\n    'http://user:pass@proxy',\n    'user:pass@proxy:8080',  # no scheme\n    'proxy:8080',  # no scheme, no auth\n    'http://proxy:8080',  # no auth\n    'http://user:p%40ss@proxy:8080',  # encoded password\n]\n\nfor url in urls:\n    result = prepend_scheme_if_needed(url, 'http')\n    print(f'{url!r:45s} -> {result!r}')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-2905__2y9dpH2":{"task_name":"pydata__xarray-2905","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 65 steps but only 41 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Read","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 26 times. Full breakdown: Bash (26), Grep (6), Read (6), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): variable.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\nimport numpy as np\n\n# Test that as_compatible_data preserves objects with .values attribute\nfrom xarray.core.variable import as_compatible_data\n\nclass HasValues(object):\n    values = 5\n\nobj = HasValues()\nresult = as_compatible_data(obj)\nprint('result:', result)\nprint('type:', type(result))\nprint('Is HasValues?', isinstance(result, HasValues))\n\n# Test that pd.Series still works\nimport pandas as pd\ns = pd.Series([1, 2, 3])\nresult2 = as_compatible_data(s)\nprint('pd.Series result type:', type(result2))\nprint('pd.Series values:', result2)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-3095__NxpyQAj":{"task_name":"pydata__xarray-3095","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Grep","Write"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): variable.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-3151__ErJWBY6":{"task_name":"pydata__xarray-3151","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): combine.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_combine.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-3305__Bf84LuW":{"task_name":"pydata__xarray-3305","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (5), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dataset.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\nda = xr.DataArray([0, 0], dims='x', attrs={'units':'K'})\nout = da.quantile(.9, dim='x', keep_attrs=True)\nprint('DataArray attrs:', out.attrs)\nassert out.attrs == {'units': 'K'}, f'Expected units=K, got {out.attrs}'\n\n# Also test keep_attrs=False (default)\nout2 = da.quantile(.9, dim='x')\nprint('DataArray attrs (default):', out2.attrs)\nassert out2.attrs == {}, f'Expected empty, got {out2.attrs}'\n\n# Test Dataset too\nds = xr.Dataset({'a': da})\nout3 = ds.quantile(.9, dim='x', keep_attrs=True)\nprint('Dataset attrs:', out3.attrs)\nprint('Dataset var attrs:', out3['a'].attrs)\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-3677__qp97LoQ":{"task_name":"pydata__xarray-3677","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Read","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (3), Grep (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): merge.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\n\n# Test with overwrite_vars\nds = xr.Dataset({'a': 0, 'b': 99})\nda = xr.DataArray(1, name='b')\nresult = ds.merge(da, overwrite_vars='b')\nprint('overwrite_vars test:')\nprint(result)\nprint()\n\n# Test with unnamed DataArray (should fail with clear error)\nda_unnamed = xr.DataArray(1)\ntry:\n    ds.merge(da_unnamed)\nexcept Exception as e:\n    print(f'Unnamed DataArray error: {type(e).__name__}: {e}')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-3993__4uivhjM":{"task_name":"pydata__xarray-3993","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Bash","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Write","Read"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (7), TodoWrite (5), Edit (4), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 4 file(s): whats-new.rst, dataarray.py, test_dataset.py, test_units.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_dataset.py::test_integrate -xvs 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4075__4dAtX7L":{"task_name":"pydata__xarray-4075","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Read","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Agent","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): weighted.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport xarray as xr\n\ndta = xr.DataArray([1., 1., 1.])\nwgt = xr.DataArray(np.array([1, 1, 0], dtype=np.bool_))\n\nresult = dta.weighted(wgt).mean()\nprint('Result:', result.values)\nassert result.values == 1.0, f'Expected 1.0, got {result.values}'\n\n# Also verify sum_of_weights is correct\nsow = dta.weighted(wgt).sum_of_weights()\nprint('Sum of weights:', sow.values)\nassert sow.values == 2, f'Expected 2, got {sow.values}'\n\n# Verify non-boolean weights still work\nwgt2 = xr.DataArray([2.0, 3.0, 0.0])\nresult2 = dta.weighted(wgt2).mean()\nprint('Non-bool result:', result2.values)\nassert result2.values == 1.0\n\nprint('All tests PASS')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4094__EEBGxh5":{"task_name":"pydata__xarray-4094","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dataarray.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport xarray as xr\n\narr = xr.DataArray(np.arange(3), coords=[('x', [0, 1, 2])])\ndata = xr.Dataset({'a': arr, 'b': arr})\nstacked = data.to_stacked_array('y', sample_dims=['x'])\n\n# Test fix: drop the stacked dim coordinate\ndata_dict = {}\nidx = stacked.indexes['y']\nlevel_number = idx._get_level_number(0)\nvariables = idx.levels[level_number]\nvariable_dim = idx.names[level_number]\n\nfor k in variables:\n    v = stacked.sel({variable_dim: k}).squeeze(drop=True)\n    # Drop the dim coordinate if it still exists\n    if 'y' in v.coords:\n        v = v.drop_vars('y')\n    data_dict[k] = v\n\nresult = xr.Dataset(data_dict)\nprint(result)\nprint()\nprint('Original:')\nprint(data)\nprint()\nprint('Identical:', data.identical(result))\n\" 2>&1\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4356__YV9Ewmk":{"task_name":"pydata__xarray-4356","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 65 steps but only 42 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Edit","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 34 times. Full breakdown: Bash (34), Read (3), Edit (3), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): nanops.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"find /testbed -name \"__pycache__\" -path \"*/xarray/*\" -exec rm -rf {} + 2>/dev/null; python -c \"\nimport numpy as np\nimport xarray as xr\n\nda2 = xr.DataArray([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]])\nresult2 = da2.sum(['dim_0', 'dim_1'], min_count=1)\nprint('All NaN, min_count=1:', result2.values, '(should be NaN)')\n\nda3 = xr.DataArray([[np.nan, 2, 3], [4, np.nan, 6]])\nresult4 = da3.sum(['dim_0', 'dim_1'], min_count=5)\nprint('4 valid, min_count=5:', result4.values, '(should be NaN)')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4629__X5yvK6m":{"task_name":"pydata__xarray-4629","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Write","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): merge.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4687__dTocso3":{"task_name":"pydata__xarray-4687","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 64 steps but only 39 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Read","Agent"],"answer":"Read","reason":"\"Read\" was called 11 times. Full breakdown: Read (11), Edit (11), Bash (11), Grep (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): computation.py, test_computation.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_computation.py::test_where xarray/tests/test_computation.py::test_where_attrs -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4695__mx2b7Ta":{"task_name":"pydata__xarray-4695","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Grep","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dataarray.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom xarray import DataArray\n\nempty = np.zeros((2,2))\nD1 = DataArray(empty, dims=['dim1', 'dim2'],   coords={'dim1':['x', 'y'], 'dim2':['a', 'b']})\nD2 = DataArray(empty, dims=['dim1', 'method'], coords={'dim1':['x', 'y'], 'method':['a', 'b']})\n\nprint('D1.loc:', D1.loc[dict(dim1='x', dim2='a')])\nprint()\nprint('D2.loc:', D2.loc[dict(dim1='x', method='a')])\nprint()\n\n# Also test other potentially conflicting dimension names\nD3 = DataArray(empty, dims=['dim1', 'tolerance'], coords={'dim1':['x', 'y'], 'tolerance':['a', 'b']})\nprint('D3.loc (tolerance):', D3.loc[dict(dim1='x', tolerance='a')])\nprint()\n\nD4 = DataArray(empty, dims=['dim1', 'drop'], coords={'dim1':['x', 'y'], 'drop':['a', 'b']})\nprint('D4.loc (drop):', D4.loc[dict(dim1='x', drop='a')])\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-4966__vhp2muZ":{"task_name":"pydata__xarray-4966","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Edit","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (4), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): variables.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom xarray.coding.variables import UnsignedIntegerCoder\nfrom xarray import Variable\n\n# Test 1: existing behavior - signed data with _Unsigned=true -> unsigned\nvar = Variable(['x'], np.array([0, 1, 127, -128, -1], dtype='i1'), {'_Unsigned': 'true'})\ncoder = UnsignedIntegerCoder()\ndecoded = coder.decode(var)\nprint('Test 1 (signed -> unsigned):', decoded.values, decoded.dtype)\n\n# Test 2: NEW - unsigned data with _Unsigned=false -> signed (the OPeNDAP/pydap case)\nvar2 = Variable(['x'], np.array([0, 1, 127, 128, 255], dtype='u1'), {'_Unsigned': 'false'})\ndecoded2 = coder.decode(var2)\nprint('Test 2 (unsigned -> signed):', decoded2.values, decoded2.dtype)\n# 128 should become -128, 255 should become -1\n\n# Test 3: roundtrip encode for _Unsigned=false\nencoded = coder.encode(decoded2)\nprint('Test 3 (encode back):', encoded.values, encoded.dtype, encoded.attrs)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-6461__EccFrPb":{"task_name":"pydata__xarray-6461","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Bash","Edit","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (6), Read (5), Edit (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): computation.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\n\n# Test case from the issue: scalar x, should not crash\nresult = xr.where(xr.DataArray([1, 2, 3]) > 0, 1, 0)\nprint('Scalar x:', result.values)\n\n# Test with keep_attrs=True explicitly\nresult = xr.where(xr.DataArray([1, 2, 3]) > 0, 1, 0, keep_attrs=True)\nprint('Scalar x, keep_attrs=True:', result.values)\nprint('Attrs:', result.attrs)\n\n# Test that attrs are preserved when x is a DataArray\nx = xr.DataArray([1, 2, 3], attrs={'units': 'meters'})\nresult = xr.where(x > 0, x, 0, keep_attrs=True)\nprint('DataArray x, keep_attrs=True:', result.values)\nprint('Attrs:', result.attrs)\n\n# Test with scalar y too\nresult = xr.where(xr.DataArray([1, 2, 3]) > 0, 1, 0, keep_attrs=False)\nprint('keep_attrs=False:', result.values)\n\n# Test with both x and y as DataArrays\ny = xr.DataArray([10, 20, 30], attrs={'source': 'test'})\nresult = xr.where(x > 1, x, y, keep_attrs=True)\nprint('Both DataArrays:', result.values)\nprint('Attrs (should be x attrs):', result.attrs)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-6599__gF9xZHS":{"task_name":"pydata__xarray-6599","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 42 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Agent","Bash"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Write","Glob"],"answer":"Bash","reason":"\"Bash\" was called 18 times. Full breakdown: Bash (18), Read (4), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): computation.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e /testbed 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-6721__WKR3TPL":{"task_name":"pydata__xarray-6721","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): common.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_dataset.py -x -q -k \"chunk\" 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-6744__YsfvhWa":{"task_name":"pydata__xarray-6744","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): rolling.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_rolling.py::TestDataArrayRolling::test_rolling_iter -xvs 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-6938__9ARWoh6":{"task_name":"pydata__xarray-6938","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (6), Grep (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dataset.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest xarray/tests/test_dataset.py -x -q -k \"swap_dims\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-6992__nLU82DA":{"task_name":"pydata__xarray-6992","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Write","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (4), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dataset.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -m pytest xarray/tests/test_dataset.py -x -q --tb=short -k \"reset_index or set_index\" 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-7229__tXGWtAN":{"task_name":"pydata__xarray-7229","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Write","Glob"],"answer":"Read","reason":"\"Read\" was called 10 times. Full breakdown: Read (10), Bash (10), Agent (2), Grep (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): computation.py, test_computation.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\n\n# Test from PR #6461 - scalar values\ncond = xr.DataArray([True, False], dims='x', attrs={'attr': 'cond'})\nx = xr.DataArray([1, 1], dims='x', attrs={'attr': 'x'})\ny = xr.DataArray([0, 0], dims='x', attrs={'attr': 'y'})\n\n# Test 1: keep_attrs=True with DataArrays\nactual = xr.where(cond, x, y, keep_attrs=True)\nassert actual.attrs == {'attr': 'x'}, f'FAIL: got {actual.attrs}'\nprint('Test 1 passed: DataArray attrs from x')\n\n# Test 2: keep_attrs=True with scalar values\nactual = xr.where(cond, 1, 0, keep_attrs=True)\nassert actual.attrs == {}, f'FAIL: got {actual.attrs}'\nprint('Test 2 passed: scalar values give empty attrs')\n\nprint('All existing test cases passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pydata__xarray-7233__2m9ccM5":{"task_name":"pydata__xarray-7233","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): rolling.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nimport xarray as xr\n\n# Test DataArray path too\nda = xr.DataArray(np.arange(24), dims=['time'])\nda = da.assign_coords(day=365 * da)\n\nprint('Before:')\nprint(da)\nprint()\n\nresult = da.coarsen(time=12).construct(time=('year', 'month'))\nprint('After:')\nprint(result)\nprint()\n\nassert 'day' in result.coords, 'day should be a coordinate!'\nprint('SUCCESS: DataArray case also works')\n\n# Test with dimension coordinates too\nda2 = xr.DataArray(np.arange(24), dims=['time'], coords={'time': np.arange(24)})\nda2 = da2.assign_coords(day=365 * da2)\nds2 = da2.to_dataset(name='T')\nresult2 = ds2.coarsen(time=12).construct(time=('year', 'month'))\nprint()\nprint('With dim coords:')\nprint(result2)\nassert 'day' in result2.coords\nassert 'time' not in result2.coords  # original dim coord is gone (replaced by year/month)\nprint('SUCCESS: dimension coord case works too')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pydata__xarray-7393__3dgTikH":{"task_name":"pydata__xarray-7393","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Agent","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): indexing.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport xarray as xr\nimport numpy as np\n\nds = xr.Dataset(coords={'a': np.array([0], dtype='i4')})\nresult = ds['a'].values.dtype == ds.stack(b=('a',))['a'].values.dtype\nprint(f'dtypes match: {result}')\nprint(f'original dtype: {ds[\\\"a\\\"].values.dtype}')\nprint(f'stacked dtype: {ds.stack(b=(\\\"a\\\",))[\\\"a\\\"].values.dtype}')\n\n# Also test other small int types\nfor dt in ['i1', 'i2', 'i4', 'u1', 'u2', 'u4']:\n    ds = xr.Dataset(coords={'a': np.array([0], dtype=dt)})\n    stacked = ds.stack(b=('a',))\n    ok = ds['a'].values.dtype == stacked['a'].values.dtype\n    print(f'{dt}: {\\\"OK\\\" if ok else \\\"FAIL\\\"} (original={ds[\\\"a\\\"].values.dtype}, stacked={stacked[\\\"a\\\"].values.dtype})')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pylint-dev__pylint-4551__mg9LbxJ":{"task_name":"pylint-dev__pylint-4551","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 51 steps but only 35 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (7), Edit (5), TodoWrite (4), Agent (2), Glob (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): inspector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"/opt/miniconda3/envs/testbed/bin/python --version && /opt/miniconda3/envs/testbed/bin/python -c \"import astroid; print(astroid.__version__)\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pylint-dev__pylint-4604__DKrBs9o":{"task_name":"pylint-dev__pylint-4604","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Write (2), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): variables.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pylint /tmp/test_type_comment.py --disable=all --enable=unused-import 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pylint-dev__pylint-4661__iArkmm5":{"task_name":"pylint-dev__pylint-4661","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Read","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Edit (5), Read (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): faq.rst, __init__.py, unittest_lint.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/lint/unittest_lint.py::test_pylint_home -xvs 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pylint-dev__pylint-4970__ttqMt2d":{"task_name":"pylint-dev__pylint-4970","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): similar.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\n# Create two files with duplicate code\nimport tempfile, os\n\nd = tempfile.mkdtemp()\nf1 = os.path.join(d, 'a.py')\nf2 = os.path.join(d, 'b.py')\n\ncode = '''\ndef foo():\n    x = 1\n    y = 2\n    z = x + y\n    return z\n\ndef bar():\n    a = 1\n    b = 2\n    c = a + b\n    return c\n'''\n\nwith open(f1, 'w') as f: f.write(code)\nwith open(f2, 'w') as f: f.write(code)\n\nprint('Test 1: min-similarity-lines=0 should produce no R0801')\nos.system(f'python -m pylint --disable=all --enable=R0801 --min-similarity-lines=0 {f1} {f2}')\n\nprint()\nprint('Test 2: min-similarity-lines=4 (default) should detect duplicates')\nos.system(f'python -m pylint --disable=all --enable=R0801 --min-similarity-lines=4 {f1} {f2}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pylint-dev__pylint-6386__rnyZSqm":{"task_name":"pylint-dev__pylint-6386","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 30 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Grep","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Agent","Write"],"answer":"Grep","reason":"\"Grep\" was called 13 times. Full breakdown: Grep (13), Bash (7), Read (5), Edit (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): utils.py, base_options.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"echo \"x = 1\" > /tmp/mytest.py && python -m pylint /tmp/mytest.py -v 2>&1 | head -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pylint-dev__pylint-6528__kyViLqy":{"task_name":"pylint-dev__pylint-6528","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 78 steps but only 49 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 23 times. Full breakdown: Bash (23), Read (12), Grep (8), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pylinter.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"mkdir -p /tmp/test_ignore/.a && echo \"import re\" > /tmp/test_ignore/.a/foo.py && echo \"import re\" > /tmp/test_ignore/bar.py\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pylint-dev__pylint-6903__6SqsrkA":{"task_name":"pylint-dev__pylint-6903","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 6 steps but only 3 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Write","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Write","Edit"],"answer":"Read","reason":"\"Read\" was called 1 times. Full breakdown: Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): run.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pylint-dev__pylint-7080__vyJGa7q":{"task_name":"pylint-dev__pylint-7080","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pylinter.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"mkdir -p /tmp/test_ignore_paths/src/gen && cat > /tmp/test_ignore_paths/src/good.py << 'EOF'\n\"\"\"Good module.\"\"\"\nx = 1\nEOF\ncat > /tmp/test_ignore_paths/src/gen/bad.py << 'EOF'\n\"\"\"Bad module that should be ignored.\"\"\"\nx = 1\nEOF\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pylint-dev__pylint-7277__MSRJYP4":{"task_name":"pylint-dev__pylint-7277","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (2), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_self.py::TestRunTC::test_modify_sys_path -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pylint-dev__pylint-8898__Hvhm366":{"task_name":"pylint-dev__pylint-8898","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Glob","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Grep (5), Edit (4), Read (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): argument.py, test_config.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom pylint.config.argument import _regexp_csv_transfomer, _parse_regex_csv\n\n# Test case from the bug report\nresult = _parse_regex_csv('(foo{1,3})')\nprint('Parse single regex with comma:', result)\ncompiled = _regexp_csv_transfomer('(foo{1,3})')\nprint('Compiled:', compiled)\n\n# Multiple regexes, one with comma\nresult = _parse_regex_csv('(foo{1,3}),bar,baz{2,4}')\nprint('Multiple regexes:', result)\ncompiled = _regexp_csv_transfomer('(foo{1,3}),bar,baz{2,4}')\nprint('Compiled:', compiled)\n\n# Simple case with no commas in regex\nresult = _parse_regex_csv('foo,bar,baz')\nprint('Simple split:', result)\n\n# Whitespace handling\nresult = _parse_regex_csv(' foo , bar , baz ')\nprint('With whitespace:', result)\n\n# Empty strings\nresult = _parse_regex_csv('')\nprint('Empty:', result)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-10051__b24ZgYM":{"task_name":"pytest-dev__pytest-10051","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Grep","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Grep (3), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): logging.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest -xvs -k \"test_caplog\" --no-header -q -p no:cacheprovider 2>&1 | tail -5 || true\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pytest-dev__pytest-10081__ZNZsop2":{"task_name":"pytest-dev__pytest-10081","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): unittest.py, test_unittest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest testing/test_unittest.py::test_pdb_teardown_skipped -x -v 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-10356__ufkurnw":{"task_name":"pytest-dev__pytest-10356","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Edit","Write"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (5), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): structures.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport pytest\n\n@pytest.mark.foo\nclass Foo:\n    pass\n\n@pytest.mark.bar\nclass Bar:\n    pass\n\nclass TestDings(Foo, Bar):\n    def test_dings(self):\n        pass\n\nfrom _pytest.mark.structures import get_unpacked_marks\nmarks = list(get_unpacked_marks(TestDings))\nprint('Marks on TestDings:', [m.name for m in marks])\nassert 'foo' in [m.name for m in marks], 'Missing foo marker'\nassert 'bar' in [m.name for m in marks], 'Missing bar marker'\nprint('SUCCESS: Both markers found')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pytest-dev__pytest-5262__W3P4d7w":{"task_name":"pytest-dev__pytest-5262","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Edit (2), Read (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): capture.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom _pytest.capture import EncodedFile\nimport io\nbuf = io.BytesIO()\nbuf.mode = 'rb+'  # simulate\n# Can't set mode on BytesIO, use a real tempfile\nimport tempfile, os\nf = tempfile.NamedTemporaryFile(mode='wb+')\nef = EncodedFile(f, 'utf-8')\nprint('mode:', ef.mode)\nassert 'b' not in ef.mode, 'mode should not contain b'\nprint('OK - mode does not contain b')\nf.close()\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-5631__XYMyTsy":{"task_name":"pytest-dev__pytest-5631","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Write","Bash"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Grep","Bash"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Bash (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): compat.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom unittest.mock import patch, DEFAULT\n\n# Simulate the fix\nsentinels = [DEFAULT]\nnew_val = np.array([-5.5, 3.0])\n\n# Old way would fail:\ntry:\n    result = new_val in sentinels\n    print('Old way result:', result)\nexcept ValueError as e:\n    print('Old way fails:', e)\n\n# New way works:\nresult = any(new_val is s for s in sentinels)\nprint('New way result:', result)\n\n# Also verify DEFAULT is still detected:\nresult = any(DEFAULT is s for s in sentinels)\nprint('DEFAULT detected:', result)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-5787__C7nnJYh":{"task_name":"pytest-dev__pytest-5787","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (7), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): reports.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest testing/test_reports.py -x -v 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pytest-dev__pytest-5809__giDqdXr":{"task_name":"pytest-dev__pytest-5809","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 17 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (2), Edit (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): pastebin.py, test_pastebin.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest testing/test_pastebin.py -v 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-5840__REzaUCg":{"task_name":"pytest-dev__pytest-5840","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Bash","Grep"],"answer":"Read","reason":"\"Read\" was called 6 times. Full breakdown: Read (6), Grep (4), Bash (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pathlib.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest testing/test_pathlib.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"pytest-dev__pytest-6197__7nWrmzq":{"task_name":"pytest-dev__pytest-6197","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Edit","Glob"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 19 times. Full breakdown: Bash (19), Read (9), Grep (4), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"git diff 85288b532^1..85288b532 -- src/_pytest/python.py\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-6202__g8PQZpS":{"task_name":"pytest-dev__pytest-6202","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Read","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (2), Grep (2), Edit (1), Write (1), Glob (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest bug.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7205__PZF7qHr":{"task_name":"pytest-dev__pytest-7205","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Agent","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (3), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): setuponly.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"cat > /tmp/test_bytes.py << 'EOF'\nimport pytest\n\n@pytest.mark.parametrize('data', [b'Hello World'])\ndef test_data(data):\n    pass\nEOF\npython -bb -m pytest --setup-show /tmp/test_bytes.py 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7236__pxMMVgQ":{"task_name":"pytest-dev__pytest-7236","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Read","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Read","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (2), Write (2), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): unittest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest test_repro.py --pdb -s 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7324__F7BhSi3":{"task_name":"pytest-dev__pytest-7324","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Grep (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): expression.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest testing/test_mark_expression.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7432__Dk3SHLX":{"task_name":"pytest-dev__pytest-7432","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Read","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): skipping.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"cat > /tmp/test_it.py << 'EOF'\nimport pytest\n\n@pytest.mark.skip\ndef test_skip_location() -> None:\n    assert 0\nEOF\npython -m pytest /tmp/test_it.py -rs 2>&1 && echo \"---\" && python -m pytest /tmp/test_it.py -rs --runxfail 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7490__zymSGcH":{"task_name":"pytest-dev__pytest-7490","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): skipping.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest -rsx -xvs --no-header -p no:cacheprovider -c /dev/null -k \"not test_marker\" 2>&1 <<'PYEOF'\nimport pytest\n\ndef test_xfail_test(request):\n    mark = pytest.mark.xfail(reason=\"xfail\")\n    request.node.add_marker(mark)\n    assert 0\nPYEOF\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7521__nr7XPRE":{"task_name":"pytest-dev__pytest-7521","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Write","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): capture.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest -xvs -k \"test_cafd\" --co 2>/dev/null | head -5; echo \"---\"; cat > /tmp/test_capfd.py << 'EOF'\ndef test_capfd_includes_carriage_return(capfd):\n    print('Greetings from DOS', end='\\r')\n    out, err = capfd.readouterr()\n    assert out.endswith('\\r')\nEOF\npython -m pytest /tmp/test_capfd.py -xvs\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7571__xUKCztn":{"task_name":"pytest-dev__pytest-7571","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): logging.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest --tb=short -s -k \"test_foo or test_bar\" -x -p no:cacheprovider <(cat <<'EOF'\ndef test_foo(caplog):\n    caplog.set_level(42)\n\ndef test_bar(caplog):\n    print(caplog.handler.level)\nEOF\n) 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-7982__GT2ijBV":{"task_name":"pytest-dev__pytest-7982","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Read","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pathlib.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport os, tempfile, shutil\n\n# Create a temp structure: testdir/real_tests/test_example.py and testdir/link -> real_tests\ntmpdir = tempfile.mkdtemp()\nreal_dir = os.path.join(tmpdir, 'real_tests')\nos.makedirs(real_dir)\nwith open(os.path.join(real_dir, 'test_example.py'), 'w') as f:\n    f.write('def test_hello(): pass\\n')\nlink_dir = os.path.join(tmpdir, 'link_tests')\nos.symlink(real_dir, link_dir)\n\nfrom _pytest.pathlib import visit\nentries = list(visit(tmpdir, lambda e: True))\nnames = [e.name for e in entries]\nprint('Entries found:', names)\nassert 'test_example.py' in names, 'Symlinked directory contents not collected!'\nprint('OK: symlinked directory was followed and its contents collected.')\nshutil.rmtree(tmpdir)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"pytest-dev__pytest-8399__sUh6DUs":{"task_name":"pytest-dev__pytest-8399","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 36 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Edit (5), Read (3), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): python.py, unittest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport tempfile, os, subprocess\n\n# Create the minimal example from the bug report\nwith tempfile.TemporaryDirectory() as d:\n    test_file = os.path.join(d, 'test_example.py')\n    with open(test_file, 'w') as f:\n        f.write('''\nimport unittest\n\nclass Tests(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        pass\n\n    def test_1(self):\n        pass\n''')\n    # Run pytest --fixtures without -v \u2014 the unittest fixture should NOT appear\n    result = subprocess.run(['python', '-m', 'pytest', '--fixtures', test_file],\n                          capture_output=True, text=True)\n    if 'unittest_setUpClass' in result.stdout or '_unittest_setUpClass' in result.stdout:\n        print('FAIL: unittest fixture still visible without -v')\n        print(result.stdout)\n    else:\n        print('PASS: unittest fixture hidden without -v')\n    \n    # Run with -v \u2014 the fixture SHOULD appear\n    result_v = subprocess.run(['python', '-m', 'pytest', '--fixtures', '-v', test_file],\n                             capture_output=True, text=True)\n    if '_unittest_setUpClass' in result_v.stdout:\n        print('PASS: unittest fixture visible with -v')\n    else:\n        print('FAIL: unittest fixture not visible with -v')\n        print(result_v.stdout)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-10297__n3k5jZT":{"task_name":"scikit-learn__scikit-learn-10297","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 21 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Edit","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (4), Edit (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): ridge.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"which python && pip install -e /testbed 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-10844__q2p7E6J":{"task_name":"scikit-learn__scikit-learn-10844","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Bash","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): supervised.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport warnings\nimport numpy as np\nfrom sklearn.metrics.cluster import fowlkes_mallows_score\n\n# Test with large clusters that would cause pk*qk to overflow int32\nnp.random.seed(0)\nn = 100000\nlabels_true = np.random.randint(0, 10, n)\nlabels_pred = np.random.randint(0, 10, n)\n\nwith warnings.catch_warnings(record=True) as w:\n    warnings.simplefilter('always')\n    score = fowlkes_mallows_score(labels_true, labels_pred)\n    runtime_warnings = [x for x in w if issubclass(x.category, RuntimeWarning)]\n    print(f'Score: {score}')\n    print(f'RuntimeWarnings: {len(runtime_warnings)}')\n    if runtime_warnings:\n        for rw in runtime_warnings:\n            print(f'  Warning: {rw.message}')\n    print(f'Is NaN: {np.isnan(score)}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-10908__y6ebgXM":{"task_name":"scikit-learn__scikit-learn-10908","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Write","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): text.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"conda activate testbed 2>/dev/null; python --version; which python\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-11310__PVXFun2":{"task_name":"scikit-learn__scikit-learn-11310","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 47 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Edit"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (7), Edit (5), Grep (3), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _search.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sklearn.datasets\nimport sklearn.model_selection\nimport sklearn.ensemble\n\nX, y = sklearn.datasets.load_iris(return_X_y=True)\n\nrs = sklearn.model_selection.GridSearchCV(\n    estimator=sklearn.ensemble.RandomForestClassifier(),\n    param_grid={'n_estimators': [2, 3, 4, 5]}\n)\nrs.fit(X, y)\nprint('refit_time_:', rs.refit_time_)\nassert isinstance(rs.refit_time_, float)\nassert rs.refit_time_ > 0\n\n# Also test RandomizedSearchCV\nfrom sklearn.model_selection import RandomizedSearchCV\nrs2 = RandomizedSearchCV(\n    estimator=sklearn.ensemble.RandomForestClassifier(),\n    param_distributions={'n_estimators': [2, 3, 4, 5]},\n    n_iter=3\n)\nrs2.fit(X, y)\nprint('refit_time_ (random):', rs2.refit_time_)\nassert rs2.refit_time_ > 0\n\n# Test refit=False should NOT have refit_time_\nrs3 = sklearn.model_selection.GridSearchCV(\n    estimator=sklearn.ensemble.RandomForestClassifier(),\n    param_grid={'n_estimators': [2, 3, 4, 5]},\n    refit=False\n)\nrs3.fit(X, y)\nassert not hasattr(rs3, 'refit_time_')\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-11578__n66thJo":{"task_name":"scikit-learn__scikit-learn-11578","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Bash","Agent","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (3), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): logistic.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn import preprocessing, linear_model, utils\n\nnp.random.seed(seed=1234)\n\nsamples  = 200\nfeatures = 5\nfolds    = 10\nscorer = 'neg_log_loss'\n\nx = np.random.random(size=(samples, features))\ny = np.random.choice(['a', 'b', 'c'], size=samples)\n\ntest  = np.random.choice(range(samples), size=int(samples/float(folds)), replace=False)\ntrain = [idx for idx in range(samples) if idx not in test]\n\nlb = preprocessing.label.LabelBinarizer()\nlb.fit(y[test])\ny_bin = lb.transform(y[test])\n\ncoefs, _, scores, _ = linear_model.logistic._log_reg_scoring_path(x, y, train, test, fit_intercept=True, scoring=scorer, multi_class='multinomial')\n\nc_index = 0\ncoefs_c = coefs[c_index]\nscore = scores[c_index]\n\n# Create a LogisticRegression with multi_class='multinomial' and check its predict_proba matches\nlog_reg = linear_model.LogisticRegression(fit_intercept=True, multi_class='multinomial')\nlog_reg.coef_ = coefs_c[:, :-1]\nlog_reg.intercept_ = coefs_c[:, -1]\nlog_reg.classes_ = np.unique(y[train])\n\ndec_fn = log_reg.decision_function(x[test])\nprobs_multi = utils.extmath.softmax(dec_fn)\nprobs_builtin = log_reg.predict_proba(x[test])\n\nscore_multi = (y_bin * np.log(probs_multi)).sum(axis=1).mean()\n\nprint('Score from _log_reg_scoring_path:', score)\nprint('Score from multinomial approach:', score_multi)\nprint('Scores match (multinomial):', np.isclose(score, score_multi))\nprint('predict_proba matches softmax:', np.allclose(probs_builtin, probs_multi))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-12585__r89KT8N":{"task_name":"scikit-learn__scikit-learn-12585","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Glob","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sys\nsys.modules['sklearn.__check_build'] = type(sys)('fake')\nsys.modules['sklearn.__check_build._check_build'] = type(sys)('fake')\n\nimport copy\nimport sklearn.externals.six as six\n\n# Inline test of the clone function with the fix\ndef clone(estimator, safe=True):\n    estimator_type = type(estimator)\n    if estimator_type in (list, tuple, set, frozenset):\n        return estimator_type([clone(e, safe=safe) for e in estimator])\n    elif not hasattr(estimator, 'get_params') or isinstance(estimator, type):\n        if not safe:\n            return copy.deepcopy(estimator)\n        else:\n            raise TypeError('Cannot clone')\n    klass = estimator.__class__\n    new_object_params = estimator.get_params(deep=False)\n    for name, param in six.iteritems(new_object_params):\n        new_object_params[name] = clone(param, safe=False)\n    new_object = klass(**new_object_params)\n    params_set = new_object.get_params(deep=False)\n    for name in new_object_params:\n        param1 = new_object_params[name]\n        param2 = params_set[name]\n        if param1 is not param2:\n            raise RuntimeError('params mismatch')\n    return new_object\n\nfrom sklearn.base import BaseEstimator\n\nclass MockEstimator(BaseEstimator):\n    def __init__(self, with_mean=True):\n        self.with_mean = with_mean\n\n# Test 1: clone with estimator TYPE as parameter\nresult = clone(MockEstimator(with_mean=MockEstimator))\nprint('Test 1 passed: clone with estimator type as param')\nprint('  with_mean is MockEstimator:', result.with_mean is MockEstimator)\n\n# Test 2: clone with normal params still works\nresult = clone(MockEstimator(with_mean=False))\nprint('Test 2 passed: clone with normal param')\nprint('  with_mean:', result.with_mean)\n\n# Test 3: clone with estimator INSTANCE as parameter still works\ninner = MockEstimator(with_mean=True)\nresult = clone(MockEstimator(with_mean=inner))\nprint('Test 3 passed: clone with estimator instance as param')\nprint('  with_mean is same instance:', result.with_mean is not inner)\n\n# Test 4: clone a type directly should fail with safe=True\ntry:\n    clone(MockEstimator)\n    print('Test 4 FAILED: should have raised TypeError')\nexcept TypeError:\n    print('Test 4 passed: cloning a type directly raises TypeError with safe=True')\n\n# Test 5: clone a type directly with safe=False returns a deep copy\nresult = clone(MockEstimator, safe=False)\nprint('Test 5 passed: cloning a type with safe=False returns:', result)\nprint('  result is MockEstimator:', result is MockEstimator)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-12682__xJQDjS6":{"task_name":"scikit-learn__scikit-learn-12682","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 28 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Bash","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (5), Edit (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): dict_learning.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.decomposition import SparseCoder, DictionaryLearning, MiniBatchDictionaryLearning\n\n# Test SparseCoder with transform_max_iter\nrng = np.random.RandomState(0)\ndictionary = rng.randn(5, 10)\nX = rng.randn(3, 10)\n\n# Default max_iter\nsc = SparseCoder(dictionary, transform_algorithm='lasso_cd', transform_alpha=0.1)\nprint('SparseCoder transform_max_iter:', sc.transform_max_iter)\ncode = sc.transform(X)\nprint('SparseCoder transform shape:', code.shape)\n\n# Custom max_iter\nsc2 = SparseCoder(dictionary, transform_algorithm='lasso_cd', transform_alpha=0.1, transform_max_iter=500)\nprint('SparseCoder custom transform_max_iter:', sc2.transform_max_iter)\ncode2 = sc2.transform(X)\nprint('SparseCoder transform shape:', code2.shape)\n\n# Test DictionaryLearning\ndl = DictionaryLearning(n_components=5, transform_max_iter=500)\nprint('DictionaryLearning transform_max_iter:', dl.transform_max_iter)\n\n# Test MiniBatchDictionaryLearning\nmbdl = MiniBatchDictionaryLearning(n_components=5, transform_max_iter=500)\nprint('MiniBatchDictionaryLearning transform_max_iter:', mbdl.transform_max_iter)\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-12973__5ukaidE":{"task_name":"scikit-learn__scikit-learn-12973","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Grep","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): least_angle.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.linear_model import LassoLarsIC\n\n# Test 1: copy_X=False in __init__ should be respected when fit() is called without copy_X\nX = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])\ny = np.array([1.0, 2.0, 3.0])\n\nX_orig = X.copy()\nmodel = LassoLarsIC(criterion='aic', copy_X=False)\nmodel.fit(X, y)\nmodified = not np.array_equal(X, X_orig)\nprint(f'Test 1 - copy_X=False respected (X modified): {modified}')\n\n# Test 2: copy_X=True in __init__ should preserve X\nX = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])\nX_orig = X.copy()\nmodel = LassoLarsIC(criterion='aic', copy_X=True)\nmodel.fit(X, y)\npreserved = np.array_equal(X, X_orig)\nprint(f'Test 2 - copy_X=True respected (X preserved): {preserved}')\n\n# Test 3: explicit copy_X in fit() overrides __init__\nX = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])\nX_orig = X.copy()\nmodel = LassoLarsIC(criterion='aic', copy_X=False)\nmodel.fit(X, y, copy_X=True)\npreserved = np.array_equal(X, X_orig)\nprint(f'Test 3 - fit(copy_X=True) overrides __init__(copy_X=False): {preserved}')\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13124__4WWgaSU":{"task_name":"scikit-learn__scikit-learn-13124","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 46 steps but only 32 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Bash","Edit"],"answer":"Bash","reason":"\"Bash\" was called 23 times. Full breakdown: Bash (23), Read (6), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _split.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.model_selection import StratifiedKFold\n\nsamples_per_class = 10\nX = np.linspace(0, samples_per_class*2-1, samples_per_class * 2)\ny = np.concatenate((np.ones(samples_per_class), np.zeros(samples_per_class)), axis=0)\n\nprint('shuffle = False')\nk_fold = StratifiedKFold(n_splits=10, shuffle=False, random_state=1)\nfor fold_n, (train_idx, test_idx) in enumerate(k_fold.split(X, y)):\n    print('test:', test_idx)\n\nprint()\nprint('shuffle = True, seed=1')\nk_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)\nfor fold_n, (train_idx, test_idx) in enumerate(k_fold.split(X, y)):\n    print('test:', test_idx)\n\nprint()\nprint('shuffle = True, seed=2')\nk_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=2)\nfor fold_n, (train_idx, test_idx) in enumerate(k_fold.split(X, y)):\n    print('test:', test_idx)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13135__mCsrcDV":{"task_name":"scikit-learn__scikit-learn-13135","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 43 steps but only 32 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Glob","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 29 times. Full breakdown: Bash (29), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _discretization.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"find /testbed -name \"*.cpython-36m*\" -delete && python setup.py build_ext --inplace 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13142__aNZyJzX":{"task_name":"scikit-learn__scikit-learn-13142","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Grep","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Glob","Read"],"answer":"Bash","reason":"\"Bash\" was called 19 times. Full breakdown: Bash (19), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.utils.testing import assert_array_equal\nimport numpy\n\nnumpy.random.seed(42)\nX = numpy.random.randn(1000, 5)\n\nprint('no n_init')\ngm = GaussianMixture(n_components=5, random_state=0)\nc1 = gm.fit_predict(X)\nc2 = gm.predict(X)\nassert_array_equal(c1, c2)\nprint('OK')\n\nprint('n_init=5')\ngm = GaussianMixture(n_components=5, n_init=5, random_state=0)\nc1 = gm.fit_predict(X)\nc2 = gm.predict(X)\nassert_array_equal(c1, c2)\nprint('OK')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13328__FBXDpFA":{"task_name":"scikit-learn__scikit-learn-13328","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 51 steps but only 37 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Read","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 31 times. Full breakdown: Bash (31), Read (4), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): huber.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"SKLEARN_SITE_JOBLIB=1 python -c \"\nimport numpy as np\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import HuberRegressor\n\nX, y, coef = make_regression(n_samples=200, n_features=2, noise=4.0, coef=True, random_state=0)\nX_bool = X > 0\n\n# This was failing before the fix\nhuber = HuberRegressor().fit(X_bool, y)\nprint('Boolean X fit succeeded, coef:', huber.coef_)\n\n# Float input still works\nhuber2 = HuberRegressor().fit(X, y)\nprint('Float X fit succeeded, coef:', huber2.coef_)\n\n# Explicit float conversion still works\nX_bool_as_float = np.asarray(X_bool, dtype=float)\nhuber3 = HuberRegressor().fit(X_bool_as_float, y)\nprint('Explicit float X fit succeeded, coef:', huber3.coef_)\n\n# Verify boolean and explicit-float give same results\nassert np.allclose(huber.coef_, huber3.coef_), 'Coefficients should match'\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13439__BNgbnHm":{"task_name":"scikit-learn__scikit-learn-13439","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (2), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pipeline.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sys\nsys.modules['sklearn.externals.joblib'] = type(sys)('joblib')\nsys.modules['sklearn.externals.joblib'].Parallel = None\nsys.modules['sklearn.externals.joblib'].delayed = None\n\n# Test __len__ directly on the steps list logic\nsteps = [('step1', 'est1'), ('step2', 'est2'), ('step3', 'est3')]\n\nclass FakePipeline:\n    def __init__(self, steps):\n        self.steps = list(steps)\n    def __len__(self):\n        return len(self.steps)\n    def __getitem__(self, ind):\n        if isinstance(ind, slice):\n            return FakePipeline(self.steps[ind])\n        name, est = self.steps[ind]\n        return est\n\npipe = FakePipeline(steps)\nprint('len(pipe):', len(pipe))\nprint('pipe[:len(pipe)] steps:', pipe[:len(pipe)].steps)\nprint('pipe[:2] steps:', pipe[:2].steps)\nassert len(pipe) == 3\nassert len(pipe[:2]) == 2\nassert len(pipe[:len(pipe)]) == 3\nprint('All assertions passed!')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13496__KjCd2aT":{"task_name":"scikit-learn__scikit-learn-13496","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Edit (5), Read (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): iforest.py, test_iforest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sklearn/ensemble/tests/test_iforest.py::test_iforest_warm_start -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-13779__F8r3kFs":{"task_name":"scikit-learn__scikit-learn-13779","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Read","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 20 times. Full breakdown: Bash (20), Grep (2), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): voting.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"ls /testbed/sklearn/utils/_logistic_sigmoid*\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"scikit-learn__scikit-learn-14053__2avffnR":{"task_name":"scikit-learn__scikit-learn-14053","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Grep","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Read","Glob"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Grep (3), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): export.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\n# Test the fix logic directly without importing sklearn tree modules\n# Simulate what export_text does\n\n# TREE_UNDEFINED is -2 in sklearn\nTREE_UNDEFINED = -2\n\n# Simulate tree_.feature for a tree with one feature (index 0)\n# Leaf nodes have TREE_UNDEFINED (-2)\ntree_feature = [0, 0, TREE_UNDEFINED, TREE_UNDEFINED, 0, TREE_UNDEFINED, TREE_UNDEFINED]\n\nfeature_names = ['sepal_length']\n\n# OLD code - this fails:\ntry:\n    feature_names_old = [feature_names[i] for i in tree_feature]\n    print('Old code worked:', feature_names_old)\nexcept IndexError as e:\n    print('Old code failed:', e)\n\n# NEW code - this works:\nfeature_names_new = [feature_names[i] if i != TREE_UNDEFINED else None for i in tree_feature]\nprint('New code worked:', feature_names_new)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14087__BSSbung":{"task_name":"scikit-learn__scikit-learn-14087","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 46 steps but only 34 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Grep","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 22 times. Full breakdown: Bash (22), Read (6), Grep (4), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): logistic.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\n# Test the fix logic directly without importing the full sklearn\n# The bug: self.multi_class (which is 'auto') was used instead of multi_class (the resolved value 'ovr')\n# on line 2173 of logistic.py. This caused the wrong indexing branch to be taken.\n\n# Verify the fix is applied:\nwith open('sklearn/linear_model/logistic.py') as f:\n    content = f.read()\n\n# Check the fixed line\nimport re\nmatch = re.search(r'if (.*?) == .ovr.:\\n\\s+w = np\\.mean\\(\\[coefs_paths\\[i, best_indices', content)\nif match:\n    var = match.group(1)\n    if var == 'multi_class':\n        print('FIX VERIFIED: Using local variable multi_class (correct)')\n    elif var == 'self.multi_class':\n        print('BUG STILL PRESENT: Using self.multi_class (incorrect)')\n    else:\n        print(f'Unexpected variable: {var}')\nelse:\n    print('Could not find the relevant line')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"scikit-learn__scikit-learn-14141__NK5rbwG":{"task_name":"scikit-learn__scikit-learn-14141","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Agent","Write","Read"],"answer":"Read","reason":"\"Read\" was called 3 times. Full breakdown: Read (3), Edit (3), Bash (3), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): ISSUE_TEMPLATE.md, _show_versions.py, test_show_versions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sklearn/utils/tests/test_show_versions.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14496__RPi6iQ4":{"task_name":"scikit-learn__scikit-learn-14496","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Grep","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Grep","Read"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (3), Read (3), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): optics_.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\n# Test the fix logic directly\nimport numpy as np\n\n# Simulate what the code does with the fix\nn_samples = 100\nmin_samples = 0.1\n\nif min_samples <= 1:\n    min_samples = int(round(max(2, min_samples * n_samples)))\n\nprint(f'min_samples type: {type(min_samples)}, value: {min_samples}')\nassert isinstance(min_samples, int), 'min_samples should be int'\nassert min_samples == 10\n\n# Edge case: very small fraction\nmin_samples = 0.01\nif min_samples <= 1:\n    min_samples = int(round(max(2, min_samples * n_samples)))\nprint(f'min_samples type: {type(min_samples)}, value: {min_samples}')\nassert isinstance(min_samples, int)\nassert min_samples == 2  # max(2, 1) = 2\n\n# Edge case: already an integer > 1\nmin_samples = 5\nif min_samples <= 1:\n    min_samples = int(round(max(2, min_samples * n_samples)))\nprint(f'min_samples type: {type(min_samples)}, value: {min_samples}')\nassert min_samples == 5\n\nprint('All tests passed!')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14629__nJ6VJ3T":{"task_name":"scikit-learn__scikit-learn-14629","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Write","Bash"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (3), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): multioutput.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14710__3Rq3g4h":{"task_name":"scikit-learn__scikit-learn-14710","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 28 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): gradient_boosting.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.experimental import enable_hist_gradient_boosting\nfrom sklearn.ensemble import HistGradientBoostingClassifier\n\n# Test 1: Integer targets with early stopping\nX = np.random.randn(100, 10)\ny = np.array([0] * 50 + [1] * 50)\ngbrt = HistGradientBoostingClassifier(n_iter_no_change=10)\ngbrt.fit(X, y)\nprint('Test 1 (int targets + early stopping): OK')\n\n# Test 2: String targets without early stopping\ny2 = np.array(['x'] * 50 + ['y'] * 50, dtype=object)\ngbrt2 = HistGradientBoostingClassifier()\ngbrt2.fit(X, y2)\nprint('Test 2 (string targets, no early stopping): OK')\n\n# Test 3: String targets with early stopping, no validation data\ngbrt3 = HistGradientBoostingClassifier(n_iter_no_change=10, validation_fraction=None)\ngbrt3.fit(X, y2)\nprint('Test 3 (string targets + early stopping, no validation): OK')\n\n# Test 4: Multiclass with string targets and early stopping\ny3 = np.array(['a'] * 33 + ['b'] * 33 + ['c'] * 34, dtype=object)\ngbrt4 = HistGradientBoostingClassifier(n_iter_no_change=10)\ngbrt4.fit(X, y3)\nprint('Test 4 (multiclass string + early stopping): OK, classes:', gbrt4.classes_)\n\n# Test 5: Regressor still works\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nyr = np.random.randn(100)\ngbrt5 = HistGradientBoostingRegressor(n_iter_no_change=10)\ngbrt5.fit(X, yr)\nprint('Test 5 (regressor + early stopping): OK')\n\" 2>&1 | grep -E \"(Test|Error|Traceback)\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14894__ndtpCk4":{"task_name":"scikit-learn__scikit-learn-14894","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (2), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): base.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-14983__qCfYCJ9":{"task_name":"scikit-learn__scikit-learn-14983","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 51 steps but only 34 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Grep (9), Read (7), Edit (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _split.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sys\nsys.modules['sklearn.utils._cython_blas'] = type(sys)('fake')\nfrom sklearn.model_selection._split import RepeatedKFold, RepeatedStratifiedKFold, _build_repr\n\n# Test repr\nrkf = RepeatedKFold()\nprint(repr(rkf))\n\nrskf = RepeatedStratifiedKFold()\nprint(repr(rskf))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-15100__YXmexQY":{"task_name":"scikit-learn__scikit-learn-15100","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Write","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Bash","Glob"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Bash (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): text.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-25102__MMDhVqr":{"task_name":"scikit-learn__scikit-learn-25102","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 5 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","EnterPlanMode"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 5 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Grep","Edit"],"answer":"Agent","reason":"\"Agent\" was called 5 times. Full breakdown: Agent (5), Read (2), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"scikit-learn__scikit-learn-25232__3UdvW5W":{"task_name":"scikit-learn__scikit-learn-25232","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (8), Edit (5), Agent (1), Grep (1), TaskOutput (1), TaskStop (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _iterative.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\n\n# Test with fill_value\nX = np.array([[1, 2], [np.nan, 6], [7, np.nan]])\n\n# Test default (no fill_value)\nimp = IterativeImputer(initial_strategy='constant', max_iter=0)\nprint('Default fill_value (0):', imp.fit_transform(X))\n\n# Test custom fill_value\nimp = IterativeImputer(initial_strategy='constant', fill_value=-1, max_iter=0)\nprint('fill_value=-1:', imp.fit_transform(X))\n\n# Test with np.nan fill_value (for tree-based estimators)\nfrom sklearn.ensemble import RandomForestRegressor\nimp = IterativeImputer(\n    estimator=RandomForestRegressor(n_estimators=4, random_state=0),\n    initial_strategy='constant',\n    fill_value=np.nan,\n    max_iter=1,\n    random_state=0,\n)\nprint('fill_value=np.nan with RF:', imp.fit_transform(X))\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-25747__F7NdFrn":{"task_name":"scikit-learn__scikit-learn-25747","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 29 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Edit","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), TaskOutput (3), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _set_output.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sklearn/utils/tests/test_set_output.py -v 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"scikit-learn__scikit-learn-25931__kVGaSEz":{"task_name":"scikit-learn__scikit-learn-25931","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (4), Grep (2), Edit (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _iforest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sklearn/ensemble/tests/test_iforest.py -x -q 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-25973__3xYNEAA":{"task_name":"scikit-learn__scikit-learn-25973","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Edit","Glob"],"answer":"Edit","reason":"\"Edit\" was called 6 times. Full breakdown: Edit (6), Bash (6), Read (2), Grep (2), Agent (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _sequential.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sklearn.datasets import make_classification\nfrom sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nimport numpy as np\n\nX, y = make_classification(random_state=42)\nclf = KNeighborsClassifier(n_neighbors=5)\n\n# Test with integer cv\nseq = SequentialFeatureSelector(clf, n_features_to_select=5, scoring='accuracy', cv=5)\nseq.fit(X, y)\nprint('cv=5 works:', seq.get_support())\n\n# Test with a list of splits (not a generator)\nfrom sklearn.model_selection import KFold\nkf = KFold(n_splits=3)\nseq2 = SequentialFeatureSelector(clf, n_features_to_select=5, scoring='accuracy', cv=kf)\nseq2.fit(X, y)\nprint('cv=KFold works:', seq2.get_support())\n\n# Test with list of tuples\nsplits = list(kf.split(X, y))\nseq3 = SequentialFeatureSelector(clf, n_features_to_select=5, scoring='accuracy', cv=splits)\nseq3.fit(X, y)\nprint('cv=list works:', seq3.get_support())\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-26194__eveDC9j":{"task_name":"scikit-learn__scikit-learn-26194","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 66 steps but only 41 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Edit (11), Read (7), Grep (3), TaskOutput (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): _ranking.py, test_ranking.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport numpy as np\nfrom sklearn.metrics import roc_curve\n\n# Test 1: probability estimates (original issue)\nrng = np.random.RandomState(42)\ny_true = rng.randint(0, 2, size=10)\ny_score = rng.rand(10)\n_, _, thresholds = roc_curve(y_true, y_score)\nprint('Test 1 - probability estimates:')\nprint('  thresholds:', thresholds)\nassert thresholds.max() <= 1, f'Max threshold {thresholds.max()} exceeds 1'\nprint('  PASS')\n\n# Test 2: decision function values > 1\ny_score2 = rng.rand(10) * 10\n_, _, thresholds2 = roc_curve(y_true, y_score2)\nprint('Test 2 - decision function (values > 1):')\nprint('  thresholds:', thresholds2)\nprint('  max:', thresholds2.max(), 'max(y_score)+1:', y_score2.max() + 1)\nprint('  PASS')\n\n# Test 3: negative scores\ny_score3 = rng.rand(10) * 2 - 3\n_, _, thresholds3 = roc_curve(y_true, y_score3)\nprint('Test 3 - negative scores:')\nprint('  thresholds:', thresholds3)\nprint('  PASS')\n\n# Test 4: scores exactly at 1\ny_score4 = np.array([0, 0, 1, 1, 0.5, 0.5, 0.2, 0.8, 0.3, 0.7])\n_, _, thresholds4 = roc_curve(y_true, y_score4)\nprint('Test 4 - scores including exactly 1.0:')\nprint('  thresholds:', thresholds4)\nassert thresholds4.max() <= 1, f'Max threshold {thresholds4.max()} exceeds 1'\nprint('  PASS')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"scikit-learn__scikit-learn-26323__HiA3Psb":{"task_name":"scikit-learn__scikit-learn-26323","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 30 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 21 times. Full breakdown: Bash (21), Grep (3), TaskOutput (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _column_transformer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sklearn/compose/tests/test_column_transformer.py -x -q 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"scikit-learn__scikit-learn-9288__AUcu2Lk":{"task_name":"scikit-learn__scikit-learn-9288","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 19 times. Full breakdown: Bash (19), Read (2), Edit (2), Agent (1), Grep (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): k_means_.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-10323__K5PGYda":{"task_name":"sphinx-doc__sphinx-10323","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (3), Grep (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): code.py, test_directive_code.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sphinx.directives.code import LiteralIncludeReader\n\n# Simulate the scenario: dedent + prepend\n# Use the test literal include file\nimport pathlib\np = pathlib.Path('tests/roots/test-directive-code/literal.inc')\nprint('File exists:', p.exists())\n\n# Read lines 9-11 (indented code), dedent 4, with prepend\noptions = {'lines': '9-11', 'dedent': 4, 'prepend': 'Hello'}\n\nclass DummyConfig:\n    highlight_language = 'python'\n    \nreader = LiteralIncludeReader(p, options, DummyConfig)\ncontent, lines = reader.read()\nprint(repr(content))\n# With the fix, dedent should NOT affect the prepend text\n# prepend 'Hello' should appear as-is, dedent only applies to file content\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-10435__QbydHHG":{"task_name":"sphinx-doc__sphinx-10435","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Edit","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): latex.py, test_build_latex.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_build_latex.py::test_latex_code_role -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-10449__7bVumkS":{"task_name":"sphinx-doc__sphinx-10449","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Grep","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (2), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): typehints.py, test_ext_autodoc_configs.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_autodoc_configs.py::test_autodoc_typehints_description_with_documented_init tests/test_ext_autodoc_configs.py::test_autodoc_typehints_description_with_documented_init_no_undoc tests/test_ext_autodoc_configs.py::test_autodoc_typehints_description_with_documented_init_no_undoc_doc_rtype -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-10466__ErPkn4e":{"task_name":"sphinx-doc__sphinx-10466","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Grep","Agent"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Edit (3), Read (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): gettext.py, test_build_gettext.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_build_gettext.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-10614__i2ACTYf":{"task_name":"sphinx-doc__sphinx-10614","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 32 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Read","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Agent","Read"],"answer":"Read","reason":"\"Read\" was called 16 times. Full breakdown: Read (16), Bash (7), Grep (3), Edit (3), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): graphviz.py, inheritance_diagram.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_inheritance_diagram.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-10673__xxMZBuc":{"task_name":"sphinx-doc__sphinx-10673","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","EnterPlanMode","Read"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Agent","Read"],"answer":"Read","reason":"\"Read\" was called 9 times. Full breakdown: Read (9), Agent (3), Grep (2), EnterPlanMode (1), Glob (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-11445__DMnbrWn":{"task_name":"sphinx-doc__sphinx-11445","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 21 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): rst.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_util_rst.py -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-11510__5gTTc3u":{"task_name":"sphinx-doc__sphinx-11510","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 62 steps but only 43 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Bash","Grep"],"answer":"Read","reason":"\"Read\" was called 11 times. Full breakdown: Read (11), Bash (11), Grep (7), Write (5), TodoWrite (4), Agent (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): other.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"ls /testbed/tests/roots/test-root/\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-7440__YVY2CvT":{"task_name":"sphinx-doc__sphinx-7440","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 56 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Write","Bash"],"answer":"Read","reason":"\"Read\" was called 10 times. Full breakdown: Read (10), Bash (9), Grep (6), TodoWrite (5), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): std.py, test_domain_std.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_domain_std.py::test_glossary_warning -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-7454__zVfgERq":{"task_name":"sphinx-doc__sphinx-7454","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 55 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 20 times. Full breakdown: Bash (20), Read (5), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_domain_py.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-7462__kTmYQUP":{"task_name":"sphinx-doc__sphinx-7462","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Write"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Write"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Edit (2), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e \".[test]\" -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-7590__MW9wdfU":{"task_name":"sphinx-doc__sphinx-7590","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","EnterPlanMode","Glob"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Edit","Read"],"answer":"Read","reason":"\"Read\" was called 9 times. Full breakdown: Read (9), Agent (3), EnterPlanMode (1), Grep (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-7748__J9iGvi7":{"task_name":"sphinx-doc__sphinx-7748","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 29 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Agent","Read"],"answer":"Read","reason":"\"Read\" was called 11 times. Full breakdown: Read (11), Bash (10), Edit (5), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): __init__.py, __init__.py, test_ext_autodoc_configs.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_autodoc_configs.py::test_autodoc_docstring_signature -xvs 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-7757__awZMaPX":{"task_name":"sphinx-doc__sphinx-7757","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): inspect.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sphinx.util.inspect import signature_from_str\n\n# Test case from the bug report\nsig = signature_from_str('(a, b=0, /, c=1)')\nprint('Test 1: (a, b=0, /, c=1)')\nfor name, param in sig.parameters.items():\n    print(f'  {name}: kind={param.kind}, default={param.default!r}')\n\n# Test with no defaults on posonly\nsig2 = signature_from_str('(a, /, b=1)')\nprint()\nprint('Test 2: (a, /, b=1)')\nfor name, param in sig2.parameters.items():\n    print(f'  {name}: kind={param.kind}, default={param.default!r}')\n\n# Test with all posonly\nsig3 = signature_from_str('(a, b=0, /)')\nprint()\nprint('Test 3: (a, b=0, /)')\nfor name, param in sig3.parameters.items():\n    print(f'  {name}: kind={param.kind}, default={param.default!r}')\n\n# Test no posonly at all (regression check)\nsig4 = signature_from_str('(a, b=1, c=2)')\nprint()\nprint('Test 4: (a, b=1, c=2)')\nfor name, param in sig4.parameters.items():\n    print(f'  {name}: kind={param.kind}, default={param.default!r}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-7889__REaPvdE":{"task_name":"sphinx-doc__sphinx-7889","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): mock.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e \".[test]\" -q 2>&1 | tail -3\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-7910__kg6qDJG":{"task_name":"sphinx-doc__sphinx-7910","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Read","Edit"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (6), Edit (4), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): __init__.py, test_ext_napoleon.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_napoleon.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-7985__dwTsudd":{"task_name":"sphinx-doc__sphinx-7985","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (6), Edit (3), Write (2), Agent (1), Glob (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): linkcheck.py, test_build_linkcheck.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"mkdir -p /testbed/tests/roots/test-linkcheck-locallinks\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-8035__MKtf8wn":{"task_name":"sphinx-doc__sphinx-8035","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","EnterPlanMode","Write"],"answer":"EnterPlanMode","reason":"The very first tool call in the trajectory is \"EnterPlanMode\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Read","Agent"],"answer":"Read","reason":"\"Read\" was called 6 times. Full breakdown: Read (6), Agent (2), EnterPlanMode (1), Write (1), ExitPlanMode (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-8056__HLAVbZ9":{"task_name":"sphinx-doc__sphinx-8056","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 28 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): docstring.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sphinx.ext.napoleon import NumpyDocstring, GoogleDocstring, Config\n\n# Test NumPy-style with multiple params\nnumpy_doc = '''\nParameters\n----------\nx1, x2 : array_like\n    Input arrays, description of x1, x2.\n'''\nconfig = Config(napoleon_use_param=True)\nresult = str(NumpyDocstring(numpy_doc, config))\nprint('=== NumPy style, use_param=True ===')\nprint(result)\n\n# Test with optional\nnumpy_doc2 = '''\nParameters\n----------\nx1, x2 : array_like, optional\n    Input arrays, description of x1, x2.\n'''\nresult2 = str(NumpyDocstring(numpy_doc2, config))\nprint('=== NumPy style with optional ===')\nprint(result2)\n\n# Test single param still works\nnumpy_doc3 = '''\nParameters\n----------\nx1 : array_like\n    Input array.\n'''\nresult3 = str(NumpyDocstring(numpy_doc3, config))\nprint('=== Single param ===')\nprint(result3)\n\n# Test Google-style with multiple params\ngoogle_doc = '''\nArgs:\n    x1, x2 (array_like): Input arrays.\n'''\nresult4 = str(GoogleDocstring(google_doc, config))\nprint('=== Google style ===')\nprint(result4)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8120__pEu9xw8":{"task_name":"sphinx-doc__sphinx-8120","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Write","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): application.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_intl.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8265__a22TGhR":{"task_name":"sphinx-doc__sphinx-8265","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Edit","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): ast.py, test_pycode_ast.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_pycode_ast.py -x -q 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8269__MnREzWu":{"task_name":"sphinx-doc__sphinx-8269","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Grep","Glob"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): linkcheck.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-8459__HdCbCZV":{"task_name":"sphinx-doc__sphinx-8459","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 53 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Edit","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 20 times. Full breakdown: Bash (20), Read (9), Edit (3), Agent (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): typehints.py, test_ext_autodoc_configs.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sys\nsys.path.insert(0, 'tests')\nfrom test_ext_autodoc import do_autodoc\nfrom sphinx.testing.path import path\nfrom sphinx.application import Sphinx\nimport tempfile, os\n\n# Create a minimal test\nsrcdir = path('tests/roots/test-ext-autodoc')\nwith tempfile.TemporaryDirectory() as tmpdir:\n    outdir = path(os.path.join(tmpdir, 'out'))\n    doctreedir = path(os.path.join(tmpdir, 'doctree'))\n    confoverrides = {\n        'autodoc_typehints': 'description',\n        'autodoc_type_aliases': {'myint': 'myint'},\n    }\n    app = Sphinx(srcdir, srcdir, outdir, doctreedir, 'text', confoverrides=confoverrides)\n    \n    options = {'members': None}\n    actual = do_autodoc(app, 'module', 'target.annotations', options)\n    \n    # Check what got recorded\n    annotations = app.env.temp_data.get('annotations', {})\n    print('Recorded annotations:')\n    for name, ann in annotations.items():\n        print(f'  {name}: {ann}')\n    \n    print()\n    print('RST output:')\n    for line in actual:\n        print(repr(line))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8475__US2fsU6":{"task_name":"sphinx-doc__sphinx-8475","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 42 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Grep","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (8), Edit (4), Agent (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): linkcheck.py, test_build_linkcheck.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_build_linkcheck.py::test_follows_redirects_on_HEAD tests/test_build_linkcheck.py::test_follows_redirects_on_GET tests/test_build_linkcheck.py::test_TooManyRedirects_on_HEAD -xvs 2>&1 | tail -60\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-8548__fsCo532":{"task_name":"sphinx-doc__sphinx-8548","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 106 steps but only 62 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 25 times. Full breakdown: Bash (25), Read (20), Grep (8), Edit (5), Agent (2), Write (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): __init__.py, importer.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_autodoc.py::test_autodoc_typed_inherited_instance_variables -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8551__kcm68SL":{"task_name":"sphinx-doc__sphinx-8551","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (4), Grep (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport tempfile, os, shutil\n\n# Create a temp directory for the test\ntmpdir = tempfile.mkdtemp()\nsrcdir = os.path.join(tmpdir, 'src')\noutdir = os.path.join(tmpdir, 'out')\nos.makedirs(srcdir)\n\n# Write conf.py\nwith open(os.path.join(srcdir, 'conf.py'), 'w') as f:\n    f.write(\\\"extensions = []\\\\n\\\")\n\n# Write index.rst with the reproducer\nwith open(os.path.join(srcdir, 'index.rst'), 'w') as f:\n    f.write('''Test\n====\n\n.. py:class:: mod.A\n.. py:class:: mod.submod.A\n\n.. py:function:: f()\n\n   - :py:class:\\`mod.A\\`\n   - :py:class:\\`mod.submod.A\\`\n\n   :param mod.A a:\n   :param mod.submod.A b:\n   :rtype: mod.A\n   :rtype: mod.submod.A\n\n.. py:currentmodule:: mod\n\n.. py:function:: f()\n\n   - :py:class:\\`A\\`\n   - :py:class:\\`mod.A\\`\n   - :py:class:\\`mod.submod.A\\`\n\n   :param A a:\n   :param mod.A b:\n   :param mod.submod.A c:\n   :rtype: A\n   :rtype: mod.A\n   :rtype: mod.submod.A\n\n.. py:currentmodule:: mod.submod\n\n.. py:function:: f()\n\n   - :py:class:\\`A\\`\n   - :py:class:\\`mod.A\\`\n   - :py:class:\\`mod.submod.A\\`\n\n   :param A a:\n   :param mod.A b:\n   :param mod.submod.A c:\n   :rtype: A\n   :rtype: mod.A\n   :rtype: mod.submod.A\n''')\n\nfrom sphinx.application import Sphinx\napp = Sphinx(srcdir, srcdir, outdir, os.path.join(outdir, '.doctrees'), 'html')\napp.build()\nprint('\\\\n=== WARNINGS ===')\nprint(app._warning.getvalue())\nshutil.rmtree(tmpdir)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8593__gptKPHz":{"task_name":"sphinx-doc__sphinx-8593","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 76 steps but only 45 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 22 times. Full breakdown: Bash (22), Edit (11), Read (9), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): __init__.py, private.py, test_ext_autodoc_private_members.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport tempfile, os, sys\n\n# Create a test module\ntmpdir = tempfile.mkdtemp()\nwith open(os.path.join(tmpdir, 'example.py'), 'w') as f:\n    f.write('''\n_foo = None  #: :meta public:\n_bar = None  #: private variable\n''')\n\nsys.path.insert(0, tmpdir)\n\nfrom sphinx.ext.autodoc import ModuleDocumenter\nfrom sphinx.pycode import ModuleAnalyzer\n\n# Test that the analyzer picks up the comment docstring\nanalyzer = ModuleAnalyzer.for_module('example')\nattr_docs = analyzer.find_attr_docs()\nprint('attr_docs:', attr_docs)\n\nfrom sphinx.util.docstrings import extract_metadata\nfor key, lines in attr_docs.items():\n    doc = '\\n'.join(lines)\n    meta = extract_metadata(doc)\n    print(f'{key}: doc={doc!r}, metadata={meta}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8595__yGtP8DH":{"task_name":"sphinx-doc__sphinx-8595","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Grep"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Agent (1), Edit (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): __init__.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e \".[test]\" -q 2>&1 | tail -3\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-8621__2EUfipc":{"task_name":"sphinx-doc__sphinx-8621","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Agent","Bash"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Agent","Grep"],"answer":"Grep","reason":"\"Grep\" was called 5 times. Full breakdown: Grep (5), Bash (5), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): transforms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_markup.py -x -q -k kbd 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8638__qjnY2cY":{"task_name":"sphinx-doc__sphinx-8638","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Agent (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_domain_py.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-8721__XxyBFWm":{"task_name":"sphinx-doc__sphinx-8721","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 62 steps but only 37 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 25 times. Full breakdown: Bash (25), Edit (5), Read (3), Agent (2), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): viewcode.py, test_ext_viewcode.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_viewcode.py::test_viewcode_epub_default tests/test_ext_viewcode.py::test_viewcode_epub_enabled -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9229__WhLCq8Q":{"task_name":"sphinx-doc__sphinx-9229","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 138 steps but only 86 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Bash","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Write","Agent"],"answer":"Bash","reason":"\"Bash\" was called 35 times. Full breakdown: Bash (35), Read (25), Grep (12), Edit (12), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 5 file(s): __init__.py, typing.py, test_ext_autodoc.py, test_ext_autodoc_autoattribute.py, test_ext_autodoc_autodata.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, Union\nfrom sphinx.pycode import ModuleAnalyzer\n\n# Write test file\ntest_code = '''\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, Union\n\nScaffoldOpts = Dict[str, Any]\n\\\"\\\"\\\"Docstring for ScaffoldOpts.\\\"\\\"\\\"\n\nFileContents = Union[str, None]\n\\\"\\\"\\\"Docstring for FileContents.\\\"\\\"\\\"\n\nFileOp = Callable[[Path, str, Dict[str, Any]], Union[Path, None]]\n\\\"\\\"\\\"Docstring for FileOp.\\\"\\\"\\\"\n'''\n\nPath('/tmp/test_alias.py').write_text(test_code)\n\nimport sys\nsys.path.insert(0, '/tmp')\n\nanalyzer = ModuleAnalyzer.for_module('test_alias')\nanalyzer.analyze()\n\nprint('attr_docs:')\nfor key, val in analyzer.attr_docs.items():\n    print(f'  {key}: {val}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-9230__Ke2L88u":{"task_name":"sphinx-doc__sphinx-9230","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 17 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): docfields.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_util_docfields.py -x -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9258__2QdRZau":{"task_name":"sphinx-doc__sphinx-9258","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Agent","Read"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport re\n\n# New regex\ndelims = r'(\\s*[\\[\\]\\(\\),](?:\\s*or\\s)?\\s*|\\s+or\\s+|\\s*\\|\\s*|\\.\\.\\.)'\n\n# Test cases\ntests = [\n    'bytes | str',\n    'int|str',\n    'int | str | None',\n    'int or str',\n    'List[int | str]',\n    'Optional[str]',\n    'int',\n    'Union[int, str]',\n]\n\nfor t in tests:\n    parts = re.split(delims, t)\n    parts = [p for p in parts if p]\n    print(f'{t!r:30s} -> {parts}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9281__QzZo6Ep":{"task_name":"sphinx-doc__sphinx-9281","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 25 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Write","Grep"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (4), Edit (3), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): inspect.py, typing.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_util_inspect.py -x -q -k \"object_description\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9320__5YMzp22":{"task_name":"sphinx-doc__sphinx-9320","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 32 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Grep","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Glob","Read"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Bash (6), Edit (2), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): quickstart.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_quickstart.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9367__NBdRQze":{"task_name":"sphinx-doc__sphinx-9367","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 14 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): ast.py, test_pycode_ast.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_pycode_ast.py -v 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9461__EJAt2rV":{"task_name":"sphinx-doc__sphinx-9461","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 78 steps but only 52 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 3 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Edit","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent spawned 3 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 23 times. Full breakdown: Bash (23), Read (15), Edit (6), TodoWrite (4), Agent (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 6 file(s): __init__.py, importer.py, typing.py, properties.py, test_ext_autodoc_autoclass.py, test_ext_autodoc_autoproperty.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_autodoc_autoproperty.py -xvs 2>&1 | tail -40\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-9591__fwPDyVW":{"task_name":"sphinx-doc__sphinx-9591","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (5), Grep (5), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): python.py, test_domain_py.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_domain_py.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9602__HrKU45t":{"task_name":"sphinx-doc__sphinx-9602","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 28 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (6), Grep (4), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): python.py, test_domain_py.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_domain_py.py::test_parse_annotation -xvs 2>&1 | tail -30\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sphinx-doc__sphinx-9658__byCXX2L":{"task_name":"sphinx-doc__sphinx-9658","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Read"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Agent (2), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): mock.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_ext_autodoc_mock.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9673__sk9gCaR":{"task_name":"sphinx-doc__sphinx-9673","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Grep","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (3), Grep (3), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): typehints.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e \".[test]\" -q 2>&1 | tail -3\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9698__qxd5JaZ":{"task_name":"sphinx-doc__sphinx-9698","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Glob","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Edit","Agent"],"answer":"Read","reason":"\"Read\" was called 2 times. Full breakdown: Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sphinx-doc__sphinx-9711__5ZSUqsH":{"task_name":"sphinx-doc__sphinx-9711","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (3), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): extension.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest tests/test_extension.py -x -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-11618__pQC3B9F":{"task_name":"sympy__sympy-11618","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 22 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Write","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Read","Write"],"answer":"Read","reason":"\"Read\" was called 4 times. Full breakdown: Read (4), Grep (3), Edit (3), Bash (3)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): point.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-12096__R2hew6c":{"task_name":"sympy__sympy-12096","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Grep","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Glob","Grep"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): function.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.utilities.lambdify import implemented_function\nf = implemented_function('f', lambda x: x ** 2)\ng = implemented_function('g', lambda x: 2 * x)\nprint('f(2).evalf()  =', f(2).evalf())\nprint('g(2).evalf()  =', g(2).evalf())\nprint('f(g(2)).evalf() =', f(g(2)).evalf())\nprint()\n# Expected: f(g(2)) = f(4) = 16\n# Also test deeper nesting\nprint('f(g(g(2))).evalf() =', f(g(g(2))).evalf())\n# g(g(2)) = g(4) = 8, f(8) = 64\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-12419__jacMJie":{"task_name":"sympy__sympy-12419","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 34 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Glob","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (3), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): matexpr.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import *\nn = Symbol('n', integer=True, positive=True)\ni, j = symbols('i j', integer=True)\n# Test the current behavior\ne = Identity(n)\nprint('e[i,j] =', e[i,j])\nprint('e[0,0] =', e[0,0])\nprint('e[0,1] =', e[0,1])\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-12481__DN9WEQS":{"task_name":"sympy__sympy-12481","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): permutations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-12489__jZ8nmsv":{"task_name":"sympy__sympy-12489","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): permutations.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.combinatorics import Permutation\n\n# Test basic functionality still works\np = Permutation._af_new([2, 1, 3, 0])\nprint('Basic _af_new:', p, type(p))\n\n# Test subclassing\nclass MyPerm(Permutation):\n    pass\n\nmp = MyPerm._af_new([2, 1, 3, 0])\nprint('Subclass _af_new:', mp, type(mp))\nprint('Is MyPerm?', isinstance(mp, MyPerm))\n\n# Test that module-level _af_new still works (it's a bound reference)\nfrom sympy.combinatorics.permutations import _af_new\np2 = _af_new([1, 0, 2])\nprint('Module-level _af_new:', p2, type(p2))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-13031__MENQkHH":{"task_name":"sympy__sympy-13031","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 54 steps but only 36 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Edit","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Write","Grep"],"answer":"Bash","reason":"\"Bash\" was called 29 times. Full breakdown: Bash (29), Read (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not edit any files."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"/opt/miniconda3/envs/testbed/bin/python --version 2>/dev/null && /opt/miniconda3/envs/testbed/bin/python -c \"import sympy; print(sympy.__version__)\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-13091__V5Rc7Fh":{"task_name":"sympy__sympy-13091","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 76 steps but only 56 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Write","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Read","Bash"],"answer":"Read","reason":"\"Read\" was called 20 times. Full breakdown: Read (20), Bash (18), Edit (12), TodoWrite (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): basic.py, numbers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sympy\n\nclass Foo:\n    def __eq__(self, other):\n        if isinstance(other, sympy.Basic):\n            return True  # for testing\n        return NotImplemented\n\nf = Foo()\ns = sympy.Symbol('x')\n\nprint('f == s:', f == s)\nprint('s == f:', s == f)\nprint('f != s:', f != s)\nprint('s != f:', s != f)\nprint()\n\n# Test with numbers\nn = sympy.Integer(1)\nprint('f == n:', f == n)\nprint('n == f:', n == f)\nprint('f != n:', f != n)\nprint('n != f:', n != f)\nprint()\n\n# Test with Float\nfl = sympy.Float(1.0)\nprint('f == fl:', f == fl)\nprint('fl == f:', fl == f)\nprint()\n\n# Test with oo\nprint('f == oo:', f == sympy.oo)\nprint('oo == f:', sympy.oo == f)\nprint()\n\n# Test normal sympy comparisons still work\nx = sympy.Symbol('x')\ny = sympy.Symbol('y')\nprint('x == x:', x == x)\nprint('x == y:', x == y)\nprint('x != x:', x != x)\nprint('x != y:', x != y)\nprint('1 == 1:', sympy.Integer(1) == sympy.Integer(1))\nprint('1 == 2:', sympy.Integer(1) == sympy.Integer(2))\nprint('1 == 1.0:', sympy.Integer(1) == 1)\nprint('1.0 == 1:', 1 == sympy.Integer(1))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13372__uqup4QR":{"task_name":"sympy__sympy-13372","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Glob","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): evalf.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx, y = symbols('x y')\nprint('Test 1:', Mul(x, Max(0, y), evaluate=False).evalf())\nprint('Test 2:', Mul(Max(0, y), x, evaluate=False).evalf())\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13480__rDypbA9":{"task_name":"sympy__sympy-13480","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 13 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Agent","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): hyperbolic.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13551__dn3nGWm":{"task_name":"sympy__sympy-13551","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 25 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Write","Agent","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 16 times. Full breakdown: Bash (16), Read (5), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): products.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nimport sys\nsys.path.insert(0, '.')\nimport collections, collections.abc\nfor attr in ['Mapping', 'MutableMapping', 'Iterable', 'Iterator', 'Callable', 'MutableSet', 'Set', 'Sequence', 'MutableSequence']:\n    if not hasattr(collections, attr):\n        setattr(collections, attr, getattr(collections.abc, attr))\n\nfrom sympy import *\nfrom sympy.abc import n, k\n\n# Test the original bug\np = Product(n + 1 / 2**k, [k, 0, n-1]).doit()\nprint('Product result:', p)\nprint('Substituting n=2:', p.subs(n, 2))\nprint('Expected for n=2: 15/2')\nprint()\n\n# Test that correct products still work\np2 = Product(k, (k, 1, n)).doit()\nprint('Product(k, (k, 1, n)):', p2)\n\np3 = Product(2*k, (k, 1, 5)).doit()\nprint('Product(2*k, (k, 1, 5)):', p3)\nprint('Expected:', 2*4*6*8*10)\n\n# Test polynomial product\np4 = Product(k + 1, (k, 0, n-1)).doit()\nprint('Product(k+1, (k, 0, n-1)):', p4)\n\n# Test n=3 case for original bug\np5 = Product(n + 1 / 2**k, [k, 0, n-1]).doit()\nval = p5.subs(n, 3)\nprint('Product for n=3:', val)\nexpected = (3 + 1) * (3 + Rational(1,2)) * (3 + Rational(1,4))\nprint('Expected for n=3:', expected)\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13615__oXhtctU":{"task_name":"sympy__sympy-13615","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 41 steps but only 26 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Glob","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Grep","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (9), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx, y = symbols('x y')\na = FiniteSet(x, y, 2)\nb = Interval(-10, 10)\nprint('Complement(a, b):', Complement(a, b))\nprint('Expected: Complement({x, y}, [-10, 10])')\nprint()\n# Also test some edge cases\nprint('All symbolic:', Complement(FiniteSet(x, y), Interval(-10, 10)))\nprint('All numeric in range:', Complement(FiniteSet(2, 3), Interval(-10, 10)))\nprint('All numeric out of range:', Complement(FiniteSet(20, 30), Interval(-10, 10)))\nprint('Mixed, some out:', Complement(FiniteSet(x, 20, 2), Interval(-10, 10)))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13647__jw8AXzu":{"task_name":"sympy__sympy-13647","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 9 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Write","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Grep","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): common.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\n# Test the logic directly without importing sympy\n# Simulate the old (buggy) and new (fixed) entry functions\n\n# Original matrix: 6x6 identity\ndef orig(i, j):\n    return 1 if i == j else 0\n\n# Other matrix: 6x2 of twos\ndef other(i, j):\n    return 2\n\npos = 3\nother_cols = 2\norig_cols = 6\n\n# Fixed entry function\ndef entry_fixed(i, j):\n    if j < pos:\n        return orig(i, j)\n    elif pos <= j < pos + other_cols:\n        return other(i, j - pos)\n    return orig(i, j - other_cols)\n\n# Buggy entry function\ndef entry_buggy(i, j):\n    if j < pos:\n        return orig(i, j)\n    elif pos <= j < pos + other_cols:\n        return other(i, j - pos)\n    return orig(i, j - pos - other_cols)\n\nprint('Fixed result:')\nfor i in range(6):\n    print([entry_fixed(i, j) for j in range(8)])\n\nprint()\nprint('Buggy result:')\nfor i in range(6):\n    print([entry_buggy(i, j) for j in range(8)])\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13757__rSzdQF8":{"task_name":"sympy__sympy-13757","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): polytools.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"/opt/miniconda3/envs/testbed/bin/python --version\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13798__nrU7VL3":{"task_name":"sympy__sympy-13798","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 17 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (2), Agent (1), Read (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): latex.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx, y = symbols('x y')\n\n# Default behavior (should be unchanged)\nprint('Default:', latex(3*x**2*y))\n\n# Existing options (should be unchanged)\nprint('dot:', latex(3*x**2*y, mul_symbol='dot'))\nprint('times:', latex(3*x**2*y, mul_symbol='times'))\nprint('ldot:', latex(3*x**2*y, mul_symbol='ldot'))\n\n# Custom thin space (the user's request)\nprint('thin space:', latex(3*x**2*y, mul_symbol=r'\\,'))\n\n# Another custom symbol\nprint('custom:', latex(3*x**2*y, mul_symbol=r' \\cdotp '))\n\n# Test with numbers (mul_symbol_latex_numbers path)\nprint('numbers default:', latex(1.0e20 * x))\nprint('numbers custom:', latex(1.0e20 * x, mul_symbol=r'\\,'))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-13852__xuEvhLC":{"task_name":"sympy__sympy-13852","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 25 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (6), Edit (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): test_zeta_functions.py, zeta_functions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/functions/special/tests/test_zeta_functions.py -x -q 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-13877__TNvfQJa":{"task_name":"sympy__sympy-13877","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Write","Read","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Agent","Edit"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): matrices.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import *\nfrom sympy.abc import a\n\n# Reproduce the issue\nf = lambda n: det(Matrix([[i + a*j for i in range(n)] for j in range(n)]))\n\n# Test small cases\nfor n in range(1, 5):\n    print(f'f({n}) = {f(n)}')\n\n# Test f(5) - should give nan\ntry:\n    print(f'f(5) = {f(5)}')\nexcept Exception as e:\n    print(f'f(5) error: {e}')\n\n# Test f(6) - should raise TypeError\ntry:\n    print(f'f(6) = {f(6)}')\nexcept TypeError as e:\n    print(f'f(6) TypeError: {e}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13878__7wYAGV2":{"task_name":"sympy__sympy-13878","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 106 steps but only 65 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Glob","Agent","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 25 times. Full breakdown: Bash (25), Read (16), Edit (13), Grep (4), TodoWrite (4), Agent (1), TaskOutput (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): crv_types.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import S\nfrom sympy.stats import *\n\n# Test Arcsin\nprint('=== Arcsin ===')\nresult = cdf(Arcsin('x', 0, 3))(1)\nprint('cdf(Arcsin(x, 0, 3))(1) =', result)\n\n# Test Dagum\nprint('\\n=== Dagum ===')\nresult = cdf(Dagum('x', S(1)/3, S(1)/5, 2))(3)\nprint('cdf(Dagum(x, 1/3, 1/5, 2))(3) =', result)\n\n# Test Frechet\nprint('\\n=== Frechet ===')\nresult = cdf(Frechet('x', S(4)/3, 1, 2))(3)\nprint('cdf(Frechet(x, 4/3, 1, 2))(3) =', result)\n\" 2>&1\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-13974__kfx6Ai4":{"task_name":"sympy__sympy-13974","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Edit (3), Read (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): tensorproduct.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"pip install -e /testbed 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-14248__XQxqSsR":{"task_name":"sympy__sympy-14248","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 102 steps but only 63 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Edit","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Grep","Write"],"answer":"Bash","reason":"\"Bash\" was called 30 times. Full breakdown: Bash (30), Edit (14), Read (10), Grep (7), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 6 file(s): function.py, latex.py, pretty.py, str.py, test_latex.py, test_str.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nA = MatrixSymbol('A', 2, 2)\nB = MatrixSymbol('B', 2, 2)\n\n# Test str\nprint('str:', str(A - A*B - B))\n\n# Test the internal representation\nexpr = A - A*B - B\nprint('args:', expr.args)\nfor a in expr.args:\n    print(f'  {a}, type={type(a).__name__}, is_MatMul={a.is_MatMul}')\n    if hasattr(a, 'as_coeff_mmul'):\n        print(f'    as_coeff_mmul: {a.as_coeff_mmul()}')\n\n# Test _coeff_isneg\nfrom sympy.core.function import _coeff_isneg\nfor a in expr.args:\n    print(f'  _coeff_isneg({a}) = {_coeff_isneg(a)}')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-14531__LdGsYpM":{"task_name":"sympy__sympy-14531","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 48 steps but only 35 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Bash","Glob"],"answer":"Bash","reason":"\"Bash\" was called 14 times. Full breakdown: Bash (14), Edit (11), TodoWrite (4), Read (3), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): str.py, test_python.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/printing/tests/test_str.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-14711__yhyf9Bb":{"task_name":"sympy__sympy-14711","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 16 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Edit","Grep"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (1), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): vector.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-14976__nLcezaT":{"task_name":"sympy__sympy-14976","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Grep","Write"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): pycode.py, test_pycode.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nfrom sympy.utilities.lambdify import lambdify\nimport inspect\n\nx = Symbol('x')\n\n# Test 1: Check the generated code wraps Rational in mpf\nf = lambdify(x, x + Rational(1, 3), 'mpmath')\nprint('Generated source:')\nprint(inspect.getsource(f))\n\n# Test 2: Check precision is maintained\nimport mpmath\nmpmath.mp.dps = 50\nresult = f(mpmath.mpf(0))\nprint('Result:', result)\nprint('Expected: 0.33333333333333333333333333333333333333333333333333')\n\n# Test 3: Original reported issue\nfrom sympy import Eq, rf, S, Float, nsolve\neqn = Eq(rf(18, x), 77 + S(1)/3)\nf2 = lambdify(x, eqn.lhs - eqn.rhs, 'mpmath')\nprint()\nprint('Original issue source:')\nprint(inspect.getsource(f2))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-15017__xhyZLRV":{"task_name":"sympy__sympy-15017","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Glob","Grep","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Glob","Read","Write"],"answer":"Read","reason":"\"Read\" was called 7 times. Full breakdown: Read (7), Edit (4), Bash (3), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): dense_ndim_array.py, sparse_ndim_array.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sympy\na = sympy.Array(3)\nprint('len(a):', len(a))\nprint('len(list(a)):', len(list(a)))\nprint('shape:', a.shape)\n\n# Also test non-scalar arrays still work\nb = sympy.Array([1, 2, 3])\nprint('len(b):', len(b))\n\nc = sympy.Array([[1, 2], [3, 4]])\nprint('len(c):', len(c))\n\n# Test sparse\nfrom sympy.tensor.array import MutableSparseNDimArray, ImmutableSparseNDimArray\nd = ImmutableSparseNDimArray(3)\nprint('len(sparse immutable rank-0):', len(d))\n\ne = MutableSparseNDimArray(3)\nprint('len(sparse mutable rank-0):', len(e))\n\n# Test mutable dense\nfrom sympy.tensor.array import MutableDenseNDimArray\nf = MutableDenseNDimArray(3)\nprint('len(mutable dense rank-0):', len(f))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-15345__TUPBfD2":{"task_name":"sympy__sympy-15345","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 14 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Edit (3), Read (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): mathematica.py, test_mathematica.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/printing/tests/test_mathematica.py -v 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-15349__X3fCk3k":{"task_name":"sympy__sympy-15349","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Grep","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): quaternion.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nfrom sympy.algebras.quaternion import Quaternion\n\nx = symbols('x')\n\n# Test case from the bug report\nq = Quaternion(cos(x/2), sin(x/2), 0, 0)\nprint('Rotation about x-axis:')\nprint(trigsimp(q.to_rotation_matrix()))\n\nprint()\n\n# Test rotation about z-axis (from docstring)\nq2 = Quaternion(cos(x/2), 0, 0, sin(x/2))\nprint('Rotation about z-axis:')\nprint(trigsimp(q2.to_rotation_matrix()))\n\nprint()\n\n# Test rotation about y-axis\nq3 = Quaternion(cos(x/2), 0, sin(x/2), 0)\nprint('Rotation about y-axis:')\nprint(trigsimp(q3.to_rotation_matrix()))\n\nprint()\n\n# Test with point\nq4 = Quaternion(cos(x/2), 0, 0, sin(x/2))\nprint('Rotation about z-axis with point (1,1,1):')\nprint(trigsimp(q4.to_rotation_matrix((1, 1, 1))))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-15599__TEFwoVD":{"task_name":"sympy__sympy-15599","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Edit","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Grep","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (5), Edit (4)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): mod.py, test_arit.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/core/tests/test_arit.py::test_Mod -x -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-15809__urtJ6x5":{"task_name":"sympy__sympy-15809","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (4), Edit (3), Grep (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): miscellaneous.py, test_miscellaneous.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/functions/elementary/tests/test_miscellaneous.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-15875__3LUrLCV":{"task_name":"sympy__sympy-15875","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Edit","Bash"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Agent","Glob"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): add.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\n\n# Original bug\ne = -2*I + (1 + I)**2\nprint('e =', e)\nprint('e.is_zero =', e.is_zero)\nassert e.is_zero == True, f'Expected True, got {e.is_zero}'\n\n# Make sure non-zero imaginary sums still work\ne2 = I + (1 + I)**2\nprint('e2 =', e2)\nprint('e2.is_zero =', e2.is_zero)\nassert e2.is_zero == False, f'Expected False, got {e2.is_zero}'\n\n# Pure real zero\ne3 = 1 + (-1)\nprint('e3.is_zero =', e3.is_zero)\n\n# Unknown case\nx = Symbol('x')\ne4 = x + I\nprint('e4.is_zero =', e4.is_zero)\n\n# Mixed imaginary/real non-zero\ne5 = 1 + I\nprint('e5.is_zero =', e5.is_zero)\nassert e5.is_zero == False\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-15976__ZgqYbjS":{"task_name":"sympy__sympy-15976","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 39 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Edit","Bash","Read"],"answer":"Read","reason":"\"Read\" was called 8 times. Full breakdown: Read (8), Bash (8), Edit (4), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): mathml.py, test_mathml.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sympy\nfrom sympy.printing.mathml import mathml\n\nx2, y, z = sympy.symbols('x2 y z')\nexpr = x2*z + x2**3\nprint('x2 expression:')\nprint(sympy.mathml(expr, printer='presentation'))\nprint()\n\nx, y, z = sympy.symbols('x y z')\nexpr2 = x*z + x**3\nprint('x expression:')\nprint(sympy.mathml(expr2, printer='presentation'))\nprint()\n\n# Test symbol with subscript notation\na_1 = sympy.Symbol('a_1')\nprint('a_1:')\nprint(sympy.mathml(a_1, printer='presentation'))\nprint()\n\n# Test symbol with superscript\nx__2 = sympy.Symbol('x__2')\nprint('x__2:')\nprint(sympy.mathml(x__2, printer='presentation'))\nprint()\n\n# Test symbol with both\nx_1__2 = sympy.Symbol('x_1__2')\nprint('x_1__2:')\nprint(sympy.mathml(x_1__2, printer='presentation'))\nprint()\n\n# Test plain symbol\nplain = sympy.Symbol('alpha')\nprint('alpha:')\nprint(sympy.mathml(plain, printer='presentation'))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-16450__3wqFMzz":{"task_name":"sympy__sympy-16450","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 8 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Agent","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (2), Grep (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): simplify.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import Symbol, posify\n\nx = Symbol('x', finite=True)\nprint('Original x.is_finite:', x.is_finite)\nprint('Original assumptions:', x._assumptions)\n\nxp, rep = posify(x)\nprint('Posified assumptions:', xp._assumptions)\nprint('Posified xp.is_finite:', xp.is_finite)\n\n# Test with other assumptions too\ny = Symbol('y', integer=True, finite=True)\nyp, rep = posify(y)\nprint()\nprint('Original y assumptions:', y._assumptions)\nprint('Posified y assumptions:', yp._assumptions)\nprint('yp.is_finite:', yp.is_finite)\nprint('yp.is_integer:', yp.is_integer)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-16597__UuKYDej":{"task_name":"sympy__sympy-16597","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 37 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Edit","Grep"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Edit (4), Read (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): assumptions.py, test_assumptions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/core/tests/test_assumptions.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-16766__vZK4YP3":{"task_name":"sympy__sympy-16766","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 25 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Grep","Glob","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Edit (4), Read (3), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): pycode.py, test_pycode.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/printing/tests/test_pycode.py::test_PythonCodePrinter -xvs 2>&1 | tail -15\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-16792__tMy8UoB":{"task_name":"sympy__sympy-16792","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 35 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Edit","Read","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (7), Grep (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): codegen.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.utilities.codegen import codegen\nfrom sympy import MatrixSymbol, S\n\nx = MatrixSymbol('x', 2, 1)\nexpr = S(1.0)\n\n# Test C code generation\nresult = codegen(('autofunc', expr), 'C', argument_sequence=(x,), header=False, empty=False)\nfor name, code in result:\n    print(f'--- {name} ---')\n    print(code)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-16886__rSieTxy":{"task_name":"sympy__sympy-16886","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 7 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Read","Write","Glob"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Agent","Edit","Read"],"answer":"Edit","reason":"\"Edit\" was called 2 times. Full breakdown: Edit (2), Grep (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): crypto.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-17139__YeG7XcJ":{"task_name":"sympy__sympy-17139","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Bash","Agent","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): fu.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx = Symbol('x')\nprint(simplify(cos(x)**I))\nprint(simplify(sin(x)**I))\n# Also test that normal cases still work\nprint(simplify(cos(x)**2))\nprint(simplify(sin(x)**4))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-17318__GCjgj3u":{"task_name":"sympy__sympy-17318","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Write"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sqrtdenest.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import sqrt, I, S\nfrom sympy.simplify.sqrtdenest import _sqrt_match\n\n# Test _sqrt_match with a purely rational Add - should not crash\nfrom sympy.core.expr import Expr\nresult = _sqrt_match(S(3) + S(4))\nprint('_sqrt_match(3+4):', result)\n\n# Test _sqrt_match with surds - should still work\nresult = _sqrt_match(1 + sqrt(2) + sqrt(2)*sqrt(3) + 2*sqrt(1+sqrt(5)))\nprint('_sqrt_match(1 + sqrt(2) + ...):', result)\n\n# Test _sqrt_match with simple surd\nresult = _sqrt_match(1 + sqrt(2))\nprint('_sqrt_match(1 + sqrt(2)):', result)\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-17630__44G6ZY3":{"task_name":"sympy__sympy-17630","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 100 steps but only 53 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Agent","Read","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Glob","Edit","Bash"],"answer":"Edit","reason":"\"Edit\" was called 29 times. Full breakdown: Edit (29), Read (13), Bash (10)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): blockmatrix.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import *\na = MatrixSymbol('a', 2, 2)\nz = ZeroMatrix(2, 2)\nb = BlockMatrix([[a, z], [z, z]])\n\n# Test basic block multiply\nr1 = b._blockmul(b)\nprint('b._blockmul(b):', r1)\nprint('type [0,1]:', type(r1.blocks[0, 1]))\n\n# Test double block multiply\nr2 = r1._blockmul(b)\nprint('b._blockmul(b)._blockmul(b):', r2)\n\n# Test block_collapse\nprint('block_collapse(b*b*b):', block_collapse(b * b * b))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-17655__tvpq94U":{"task_name":"sympy__sympy-17655","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 33 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Write","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Read"],"answer":"Bash","reason":"\"Bash\" was called 11 times. Full breakdown: Bash (11), Read (5), Edit (2), Grep (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): point.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import geometry as ge\nimport sympy\n\npoint1 = ge.Point(0,0)\npoint2 = ge.Point(1,1)\n\n# This should work (and already did)\nr1 = point1 + point2 * sympy.sympify(2.0)\nprint('point1 + point2 * 2.0 =', r1)\n\n# This should now also work\nr2 = point1 + sympy.sympify(2.0) * point2\nprint('point1 + 2.0 * point2 =', r2)\n\n# Results should be equal\nprint('Equal:', r1 == r2)\n\n# Test with integers too\nr3 = 3 * ge.Point(1, 2)\nprint('3 * Point(1,2) =', r3)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-18189__sa3Qssp":{"task_name":"sympy__sympy-18189","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Bash","Write","Edit"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): diophantine.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-18199__2RmuMsS":{"task_name":"sympy__sympy-18199","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 18 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Agent (1), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): residue_ntheory.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.ntheory.residue_ntheory import nthroot_mod\n# Test cases where a % p == 0\n# Case where (p-1) % n == 0: p=17, n=4 -> (17-1)%4=0\nprint('nthroot_mod(51, 4, 17):', nthroot_mod(51, 4, 17))  # 51 = 3*17\nprint('nthroot_mod(51, 4, 17, True):', nthroot_mod(51, 4, 17, True))\nprint('Expected: 0 should be a root since 51%17=0 and 0^4%17=0')\nprint()\n# p=17, n=2\nprint('nthroot_mod(34, 2, 17):', nthroot_mod(34, 2, 17))  \nprint('nthroot_mod(34, 2, 17, True):', nthroot_mod(34, 2, 17, True))\nprint('Expected: 0 should be a root since 34%17=0 and 0^2%17=0')\nprint()\n# p=5, n=4 -> (5-1)%4=0\nprint('nthroot_mod(10, 4, 5):', nthroot_mod(10, 4, 5))\nprint('nthroot_mod(10, 4, 5, True):', nthroot_mod(10, 4, 5, True))\nprint('Expected: 0 should be a root since 10%5=0 and 0^4%5=0')\nprint()\n# p=7, n=3 -> (7-1)%3=0\nprint('nthroot_mod(21, 3, 7):', nthroot_mod(21, 3, 7))\nprint('nthroot_mod(21, 3, 7, True):', nthroot_mod(21, 3, 7, True))\nprint('Expected: 0 should be a root since 21%7=0 and 0^3%7=0')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-18211__EvUw6HQ":{"task_name":"sympy__sympy-18211","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 45 steps but only 31 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Bash","Glob","Edit"],"answer":"Bash","reason":"\"Bash\" was called 17 times. Full breakdown: Bash (17), Read (5), Grep (4), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): relational.py, solveset.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/core/tests/test_relational.py -x -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-18698__pVGy3Ji":{"task_name":"sympy__sympy-18698","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 26 steps but only 16 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Bash","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Edit","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (4), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): polytools.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import *\nx = symbols('x')\nprint('Test 1:')\nprint(sqf_list((x**2 + 1) * (x - 1)**2 * (x - 2)**3 * (x - 3)**3))\nprint()\nprint('Test 2:')\nprint(sqf_list(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2))\nprint()\nprint('Test 3 - Poly level:')\nfrom sympy import Poly\nf = Poly((x**2 + 1) * (x - 1)**2 * (x - 2)**3 * (x - 3)**3)\nprint(f.sqf_list())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-18763__SU8p82a":{"task_name":"sympy__sympy-18763","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 29 steps but only 19 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Grep","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Grep (6), Read (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): latex.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/printing/tests/test_latex.py -x -q --tb=short 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-19040__BkZ5m6i":{"task_name":"sympy__sympy-19040","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 17 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Write","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Read","Glob","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (2), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): factortools.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx, y = symbols('x y')\n\nz = expand((x-1)*(y-1))\nprint('z =', z)\nprint('factor(z) =', factor(z))\nprint('factor(z, extension=[I]) =', factor(z, extension=[I]))\nprint()\n\n# Additional test cases\nz2 = expand((x-1)*(y-I))\nprint('z2 =', z2)\nprint('factor(z2, extension=[I]) =', factor(z2, extension=[I]))\nprint()\n\nz3 = expand((x-I)*(y-1))\nprint('z3 =', z3)\nprint('factor(z3, extension=[I]) =', factor(z3, extension=[I]))\nprint()\n\n# Test with extension=True\nz4 = expand((x-1)*(y-1))\nprint('factor(z4, extension=True) =', factor(z4, extension=True))\nprint()\n\n# Three variables\nz5 = expand((x-1)*(y-1)*(symbols('z')-1))\nprint('factor(z5, extension=[I]) =', factor(z5, extension=[I]))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-19346__pBRH7RS":{"task_name":"sympy__sympy-19346","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Read","Bash"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (2), Edit (2), Agent (1), Read (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): repr.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest /testbed/sympy/printing/tests/test_repr.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-19495__9TeWmXP":{"task_name":"sympy__sympy-19495","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 30 steps but only 17 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Agent","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Write","Bash","Agent"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (3), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): conditionset.py, test_conditionset.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx, y, z, n = symbols('x y z n')\n\n# Test case from the bug report\nimg = imageset(Lambda(n, 2*n*pi + asin(y)), S.Integers)\nprint('ImageSet:', img)\n\ncs = ConditionSet(x, Contains(y, Interval(-1,1)), img)\nprint('ConditionSet:', cs)\n\nresult = cs.subs(y, Rational(1,3))\nprint('After subs(y, 1/3):', result)\n\n# Expected: {2*pi*n + asin(1/3) | n in Integers}\n# Should NOT have 1/3 as the bound variable\n\n# Also verify the first example still works\nfrom sympy.solvers.solveset import solveset_real\nresult2 = solveset_real(Abs(x) - y, x)\nprint('solveset_real result:', result2)\nprint('After subs(y, 1/3):', result2.subs(y, Rational(1,3)))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-19637__JigD6HQ":{"task_name":"sympy__sympy-19637","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Glob","Bash","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Read","Glob","Edit"],"answer":"Grep","reason":"\"Grep\" was called 2 times. Full breakdown: Grep (2), Bash (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sympify.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-19783__6rdntgm":{"task_name":"sympy__sympy-19783","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 40 steps but only 24 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Edit","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Agent","Write","Read"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Grep (6), Read (5), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): operator.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.physics.quantum.dagger import Dagger\nfrom sympy.physics.quantum.operator import Operator\nfrom sympy.physics.quantum import IdentityOperator\n\nA = Operator('A')\nIdentity = IdentityOperator()\n\nprint('A * Identity =', A * Identity)\nB = Dagger(A)\nprint('B * Identity =', B * Identity)\nprint('Identity * B =', Identity * B)\nprint('Identity * A =', Identity * A)\nprint()\n# Also test edge cases\nprint('Identity * Identity =', Identity * Identity)\nprint('Dagger(Identity) =', Dagger(Identity))\nprint('Dagger(A) * Dagger(A) =', Dagger(A) * Dagger(A))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-19954__UFBHv2E":{"task_name":"sympy__sympy-19954","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Edit","Write","Glob"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Write","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 4 times. Full breakdown: Bash (4), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): perm_groups.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-20154__9KYaf2Z":{"task_name":"sympy__sympy-20154","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Edit","Read","Write"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Read (3), Grep (2), Edit (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): iterables.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.utilities.iterables import partitions\n\n# Test that list(partitions()) now works correctly\nprint('list(partitions(6, k=2)):')\nprint(list(partitions(6, k=2)))\n\nprint()\nprint('list with size=True:')\nprint([(M, p) for M, p in partitions(6, k=2, size=True)])\n\nprint()\nprint('partitions(4):')\nprint(list(partitions(4)))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-20428__ZyzRTSc":{"task_name":"sympy__sympy-20428","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 32 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Bash","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (6), Edit (2), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): densetools.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx = symbols('x')\nf = Poly(sympify('-117968192370600*18**(1/3)/(217603955769048*(24201 + 253*sqrt(9165))**(1/3) + 2273005839412*sqrt(9165)*(24201 + 253*sqrt(9165))**(1/3)) - 15720318185*2**(2/3)*3**(1/3)*(24201 + 253*sqrt(9165))**(2/3)/(217603955769048*(24201 + 253*sqrt(9165))**(1/3) + 2273005839412*sqrt(9165)*(24201 + 253*sqrt(9165))**(1/3)) + 15720318185*12**(1/3)*(24201 + 253*sqrt(9165))**(2/3)/(217603955769048*(24201 + 253*sqrt(9165))**(1/3) + 2273005839412*sqrt(9165)*(24201 + 253*sqrt(9165))**(1/3)) + 117968192370600*2**(1/3)*3**(2/3)/(217603955769048*(24201 + 253*sqrt(9165))**(1/3) + 2273005839412*sqrt(9165)*(24201 + 253*sqrt(9165))**(1/3))'), x)\n\ncoeff, result = f.clear_denoms()\nprint('result:', result)\nprint('result.rep:', result.rep)\nprint('result.is_zero:', result.is_zero)\nprint('result.as_expr():', result.as_expr())\n\n# Also test terms_gcd doesn't crash\nprint('terms_gcd:', result.terms_gcd())\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-20438__MJ4aQtK":{"task_name":"sympy__sympy-20438","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 114 steps but only 76 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Agent","Glob","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Bash","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 48 times. Full breakdown: Bash (48), Read (12), Grep (10), Edit (4), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): relational.py, sets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import FiniteSet, ProductSet, Eq\n\na = FiniteSet(1, 2)\nb = ProductSet(a, a)\nc = FiniteSet((1, 1), (1, 2), (2, 1), (2, 2))\n\nprint('b.is_subset(c):', b.is_subset(c))\nprint('c.is_subset(b):', c.is_subset(b))\nprint('b.intersection(c) == c.intersection(b):', b.intersection(c) == c.intersection(b))\n\n# Test the Eq simplify case from the issue\nprint('Eq(b, c).simplify():', Eq(b, c).simplify())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-20590__3jzumFe":{"task_name":"sympy__sympy-20590","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Glob","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Agent","Glob","Read","Write"],"answer":"Agent","reason":"\"Agent\" was called 1 times. Full breakdown: Agent (1), Read (1), Edit (1), Bash (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _print_helpers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-20801__4Q4QPdo":{"task_name":"sympy__sympy-20801","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 11 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Agent","Bash","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): numbers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-20916__cpKisvx":{"task_name":"sympy__sympy-20916","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 22 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Agent","Edit","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 15 times. Full breakdown: Bash (15), Read (4), Agent (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): conventions.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import symbols, pprint\nt0, t1, t2 = symbols('t0 t1 t2')\nw0 = symbols('omega0')\nprint('omega0 symbol:')\npprint(w0)\nprint()\nprint('t0 symbol:')\npprint(t0)\nprint()\n\n# Also test the split function\nfrom sympy.printing.conventions import split_super_sub\nprint('split omega0:', split_super_sub('omega0'))\nprint('split t0:', split_super_sub('t0'))\n\n# Test pretty_symbol directly\nfrom sympy.printing.pretty.pretty_symbology import pretty_symbol, xstr\nimport sympy.printing.pretty.pretty_symbology as ps\nps._use_unicode = True\nprint('pretty_symbol omega0:', repr(pretty_symbol('omega0')))\nprint('pretty_symbol t0:', repr(pretty_symbol('t0')))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-21379__q8zzHGq":{"task_name":"sympy__sympy-21379","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 31 steps but only 20 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Edit","Bash","Agent"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Edit","Agent","Grep"],"answer":"Bash","reason":"\"Bash\" was called 10 times. Full breakdown: Bash (10), Read (6), Grep (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): mod.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nfrom sympy.core.cache import clear_cache\n\n# Test 1: Original bug report\nclear_cache()\nx, y, z = symbols('x y z', real=True)\nexpr = exp(sinh(Piecewise((x, y > x), (y, True)) / z))\nresult = expr.subs({1: 1.0})\nprint('Test 1 (sinh):', result)\n\n# Test 2: cosh variant\nclear_cache()\nx, y, z = symbols('x y z', real=True)\nexpr = exp(cosh(Piecewise((x, y > x), (y, True)) / z))\nresult = expr.subs({1: 1.0})\nprint('Test 2 (cosh):', result)\n\n# Test 3: tanh variant\nclear_cache()\nx, y, z = symbols('x y z', real=True)\nexpr = exp(tanh(Piecewise((x, y > x), (y, True)) / z))\nresult = expr.subs({1: 1.0})\nprint('Test 3 (tanh):', result)\n\n# Test 4: non-real symbols should still work\nclear_cache()\nx, y, z = symbols('x y z')\nexpr = exp(sinh(Piecewise((x, y > x), (y, True)) / z))\nresult = expr.subs({1: 1.0})\nprint('Test 4 (non-real):', result)\n\nprint('All tests passed!')\n\" 2>&1\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-21596__9W2iCfA":{"task_name":"sympy__sympy-21596","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 24 steps but only 15 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Write","Agent","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): intersection.py, test_fancysets.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nfrom sympy.abc import n\n\n# Original issue #19513 - should return None (not True)\nprint('1/n subset test:', imageset(Lambda(n, 1/n), S.Integers).is_subset(S.Reals))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-21612__QyijEcZ":{"task_name":"sympy__sympy-21612","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 34 steps but only 21 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Read","Grep","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 13 times. Full breakdown: Bash (13), Read (3), Edit (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): _parse_latex_antlr.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy.parsing.latex import parse_latex\nimport sympy\n\n# The bug case\nresult = parse_latex(r'\\frac{\\frac{a^3+b}{c}}{\\frac{1}{c^2}}')\nprint('Result:', result)\nprint('Expected: ((a**3 + b)/c)/(1/(c**2))')\nprint()\n\n# Verify numerical correctness\na, b, c = sympy.symbols('a b c')\nval = result.subs([(sympy.Symbol('a'), 2), (sympy.Symbol('b'), 3), (sympy.Symbol('c'), 5)])\nprint('Evaluated:', val)\nprint('Expected: 55')\nprint()\n\n# Test simple fractions still work\nprint('Simple frac:', parse_latex(r'\\frac{a}{b}'))\nprint('Frac with 1 numer:', parse_latex(r'\\frac{1}{c^2}'))\nprint('Nested numer:', parse_latex(r'\\frac{\\frac{a}{b}}{c}'))\nprint('Nested denom:', parse_latex(r'\\frac{a}{\\frac{b}{c}}'))\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-21847__JwKvZLa":{"task_name":"sympy__sympy-21847","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Agent","Grep"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Glob","Edit","Agent"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): monomials.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sympy as sp\nfrom sympy.polys.orderings import monomial_key\n\nx1, x2, x3 = sp.symbols('x1, x2, x3')\nstates = [x1, x2, x3]\nmax_degrees = 3\nmin_degrees = 3\nmonomials = sorted(sp.itermonomials(states, max_degrees, min_degrees=min_degrees), \n                   key=monomial_key('grlex', states))\nprint('min=max=3:', monomials)\n\n# Also test min < max\nmonomials2 = sorted(sp.itermonomials(states, 3, min_degrees=2), \n                    key=monomial_key('grlex', states))\nprint('min=2, max=3:', monomials2)\n\n# Test the docstring example\nfrom sympy.abc import x, y\nmonomials3 = sorted(sp.itermonomials([x, y], 2, 1), key=monomial_key('grlex', [y, x]))\nprint('docstring example:', monomials3)\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-21930__F7xW4PV":{"task_name":"sympy__sympy-21930","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 38 steps but only 27 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Agent","Write"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Read","Edit","Glob"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Read (7), Edit (6), Grep (3), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): secondquant.py, test_secondquant.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/physics/tests/test_secondquant.py -x -q 2>&1 | tail -5\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-22080__CTULM74":{"task_name":"sympy__sympy-22080","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 32 steps but only 23 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Bash","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Edit","Bash"],"answer":"Bash","reason":"\"Bash\" was called 12 times. Full breakdown: Bash (12), Read (4), Edit (4), Agent (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): pycode.py, test_pycode.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import Mod, lambdify, symbols\nimport inspect\nx, y = symbols('x y')\n\nexpr = -Mod(x, y)\nprint('Expression:', expr)\n\nf = lambdify([x, y], expr)\nprint('f(3, 7) =', f(3, 7))\nprint('f source:', inspect.getsource(f))\n\ng = lambdify([x, y], expr, modules=[])\nprint('g(3, 7) =', g(3, 7))\nprint('g source:', inspect.getsource(g))\n\n# Also test: expr * Mod(a, b) case from the description\na, b = symbols('a b')\nexpr2 = 2 * Mod(a, b)\nh = lambdify([a, b], expr2, modules=[])\nprint('h(3, 7) =', h(3, 7))\nprint('h source:', inspect.getsource(h))\nprint('Expected h(3, 7) = 6  (2 * (3 % 7) = 2 * 3 = 6)')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-22456__NG2ssou":{"task_name":"sympy__sympy-22456","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"40+","reason":"The trajectory has 117 steps but only 71 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 2 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Glob","Edit","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent spawned 2 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Read","Bash","Grep"],"answer":"Bash","reason":"\"Bash\" was called 27 times. Full breakdown: Bash (27), Read (20), Grep (14), Edit (7), Agent (2)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): ast.py, test_ast.py, test_cnodes.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -m pytest sympy/codegen/tests/test_ast.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-22714__vvZLhas":{"task_name":"sympy__sympy-22714","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 18 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Agent","Read","Bash","Grep"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Agent","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 5 times. Full breakdown: Bash (5), Edit (2), Agent (1), Read (1), Grep (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): point.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/geometry/tests/test_point.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-22914__e2UUYec":{"task_name":"sympy__sympy-22914","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 9 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Grep","Agent","Edit"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Grep","Write","Read","Bash"],"answer":"Bash","reason":"\"Bash\" was called 2 times. Full breakdown: Bash (2), Grep (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): pycode.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-23262__dLc5Cku":{"task_name":"sympy__sympy-23262","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 5 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Write","Bash","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Agent","Edit","Write"],"answer":"Agent","reason":"\"Agent\" was called 1 times. Full breakdown: Agent (1), Read (1), Edit (1), Bash (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): lambdify.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-23413__VNixde8":{"task_name":"sympy__sympy-23413","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"21-40","reason":"The trajectory has 46 steps but only 29 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Grep","Agent","Edit"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Read","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 15 times. Full breakdown: Bash (15), Read (7), Edit (5), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"3 or more","reason":"The agent edited 3 file(s): test_normalforms.py, normalforms.py, test_normalforms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy import Matrix\nfrom sympy.matrices.normalforms import hermite_normal_form\n\n# User's original test case\nM = Matrix([[1, 12], [0, 8], [0, 5]])\nresult = hermite_normal_form(M)\nprint('HNF of [[1,12],[0,8],[0,5]]:')\nprint(result)\nprint()\n\n# Full user workflow\nimport numpy as np\nA = np.array([[5, 8, 12], [0, 0, 1]])\nresult = np.flip(np.array(hermite_normal_form(Matrix(np.flip(A)).T).T).astype(int))\nprint('User workflow result:')\nprint(result)\nprint('Expected: [[5, 8, 0], [0, 0, 1]]')\nprint()\n\n# Edge case: square matrix (should still work)\nM2 = Matrix([[2, 3, 0], [0, 1, 0], [0, 0, 1]])\nprint('Square matrix HNF:')\nprint(hermite_normal_form(M2))\nprint()\n\n# Edge case: more columns than rows (m < n)\nM3 = Matrix([[2, 3, 6]])\nprint('Wide matrix HNF:')\nprint(hermite_normal_form(M3))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-23534__mBDxKms":{"task_name":"sympy__sympy-23534","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 12 steps but only 7 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Bash","Read","Glob","Agent"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Grep","Bash"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): symbol.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nimport sympy as smp\n\n# Test the reported bug\nq, u = smp.symbols(('q:2', 'u:2'), cls=smp.Function)\nprint('type(q[0]):', type(q[0]))\nprint('type(u[0]):', type(u[0]))\n\n# Test that normal usage still works\nx, y = smp.symbols(('x', 'y'))\nprint('type(x):', type(x))\n\n# Test with list input\na, b = smp.symbols(['a', 'b'], cls=smp.Function)\nprint('type(a):', type(a))\n\n# Test single string still works\nf = smp.symbols('f', cls=smp.Function)\nprint('type(f):', type(f))\n\n# Test comma-separated string (no extra parens)\ng, h = smp.symbols('g, h', cls=smp.Function)\nprint('type(g):', type(g))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Failed","reason":"The agent's fix did not pass the tests in the SWE-bench Verified evaluation (reward = 0)."}]},"sympy__sympy-23824__eLwoYh3":{"task_name":"sympy__sympy-23824","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Write","Read","Edit"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): gamma_matrices.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.physics.hep.gamma_matrices import GammaMatrix as G, kahane_simplify\nfrom sympy.tensor.tensor import tensor_indices\nfrom sympy.physics.hep.gamma_matrices import LorentzIndex\n\nmu, nu, rho, sigma = tensor_indices('mu, nu, rho, sigma', LorentzIndex)\n\n# Test 1: contracted pair first, then free matrices\nt = G(mu)*G(-mu)*G(rho)*G(sigma)\nr = kahane_simplify(t)\nprint('Test 1:', r)\nassert r.equals(4*G(rho)*G(sigma)), 'Test 1 failed'\n\n# Test 2: free matrices first, then contracted pair (the bug case)\nt = G(rho)*G(sigma)*G(mu)*G(-mu)\nr = kahane_simplify(t)\nprint('Test 2:', r)\nassert r.equals(4*G(rho)*G(sigma)), 'Test 2 failed'\n\nprint('All tests passed!')\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-23950__xBPLqZS":{"task_name":"sympy__sympy-23950","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 27 steps but only 18 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Write","Grep","Agent","Read"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Glob","Agent","Bash"],"answer":"Bash","reason":"\"Bash\" was called 9 times. Full breakdown: Bash (9), Grep (3), Read (2), Edit (2), Agent (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"2","reason":"The agent edited 2 file(s): contains.py, test_contains.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy import *\nx = Symbol('x')\n# Test 1: as_set should return the set, not Contains\nresult = Contains(x, Reals).as_set()\nprint('as_set result:', result)\nprint('type:', type(result))\nassert result == Reals, f'Expected Reals, got {result}'\n\n# Test 2: as_set for other sets\nresult2 = Contains(x, S.Integers).as_set()\nprint('Integers as_set:', result2)\nassert result2 == S.Integers\n\n# Test 3: Verify as_relational works on the result\nprint('as_relational:', result.as_relational(x))\n\nprint('All tests passed!')\n\"\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24066__X6H8t2x":{"task_name":"sympy__sympy-24066","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 16 steps but only 11 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Read","Grep","Bash","Agent"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Read","Bash","Glob","Agent"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (2), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): unitsystem.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/physics/units/tests/test_quantities.py -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24213__4LDfEcr":{"task_name":"sympy__sympy-24213","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 19 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Write","Read"],"answer":"Grep","reason":"The very first tool call in the trajectory is \"Grep\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Write","Bash","Grep","Edit"],"answer":"Bash","reason":"\"Bash\" was called 7 times. Full breakdown: Bash (7), Grep (2), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): unitsystem.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/physics/units/tests/ -x -q 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24443__eQ5MM5H":{"task_name":"sympy__sympy-24443","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 20 steps but only 12 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Grep","Glob","Read"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Bash","Write"],"answer":"Bash","reason":"\"Bash\" was called 8 times. Full breakdown: Bash (8), Read (2), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): homomorphisms.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python3 -c \"\nfrom sympy.combinatorics import *\nfrom sympy.combinatorics.homomorphisms import homomorphism\nD3 = DihedralGroup(3)\nT = homomorphism(D3, D3, D3.generators, D3.generators)\nprint('Identity homomorphism created successfully')\nprint('T is isomorphism:', T.is_isomorphism())\n\n# Test with a non-trivial mapping\nD4 = DihedralGroup(4)\nT2 = homomorphism(D4, D4, D4.generators, D4.generators)\nprint('D4 identity homomorphism created successfully')\n\n# Test trivial homomorphism\nT3 = homomorphism(D3, D3, D3.generators)\nprint('Trivial homomorphism created successfully')\nprint('T3 is trivial:', T3.is_trivial())\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24539__M5RjFpC":{"task_name":"sympy__sympy-24539","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 10 steps but only 6 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model."},{"id":"q2","text":"What was the first tool the agent used?","options":["Glob","Read","Write","Edit"],"answer":"Read","reason":"The very first tool call in the trajectory is \"Read\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"0","reason":"The agent did not use any sub-agents. It handled all tasks directly."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Glob","Grep","Bash","Read"],"answer":"Bash","reason":"\"Bash\" was called 3 times. Full breakdown: Bash (3), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): rings.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"No","reason":"No test commands (pytest, test, make check, tox) were found in the agent's Bash calls."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24562__AakgtNd":{"task_name":"sympy__sympy-24562","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"1-10","reason":"The trajectory has 15 steps but only 10 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Edit","Read","Bash","Grep"],"answer":"Bash","reason":"The very first tool call in the trajectory is \"Bash\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Edit","Grep","Write","Bash"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): numbers.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -m pytest sympy/core/tests/test_numbers.py -x -q --tb=short 2>&1 | tail -20\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]},"sympy__sympy-24661__D9DrAhY":{"task_name":"sympy__sympy-24661","quiz":[{"id":"q1","text":"How many LLM API calls did the main agent make? (Hint: steps \u2260 API calls. One API call can produce text + multiple tool calls before needing to call the LLM again.)","options":["1-10","11-20","21-40","40+"],"answer":"11-20","reason":"The trajectory has 23 steps but only 13 LLM API calls from the main agent. A single API call can produce multiple steps (text + tool calls), and a new API call only happens when tool results are returned to the model. Note: the agent also spawned 1 sub-agent(s), each of which made its own internal LLM calls \u2014 those are hidden inside the \"Agent\" tool's output."},{"id":"q2","text":"What was the first tool the agent used?","options":["Grep","Agent","Write","Glob"],"answer":"Agent","reason":"The very first tool call in the trajectory is \"Agent\"."},{"id":"q3","text":"How many times did the agent spawn sub-agents? (A sub-agent is launched via the \"Agent\" tool \u2014 it runs its own LLM calls internally, but you only see the final result returned to the main agent.)","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent spawned 1 sub-agent(s). Each sub-agent is an independent LLM session with its own internal tool calls and LLM API calls \u2014 but those are hidden in the trajectory. You only see the Agent tool call and its final output. Sub-agents are used to delegate sub-tasks (e.g., exploring code) without polluting the main agent's context window."},{"id":"q4","text":"Which tool did the agent use most frequently?","options":["Bash","Grep","Read","Write"],"answer":"Bash","reason":"\"Bash\" was called 6 times. Full breakdown: Bash (6), Grep (3), Agent (1), Read (1), Edit (1)."},{"id":"q5","text":"How many files did the agent edit (using the Edit or Write tool)?","options":["0","1","2","3 or more"],"answer":"1","reason":"The agent edited 1 file(s): sympy_parser.py."},{"id":"q6","text":"Did the agent run any test commands (pytest, test, tox, etc.)?","options":["Yes","No"],"answer":"Yes","reason":"Yes \u2014 the agent ran test-related commands, e.g.: \"python -c \"\nfrom sympy.parsing.sympy_parser import parse_expr\nfrom sympy import Lt, Gt, Le, Ge, Eq, Ne\n\n# Test the original issue\nresult = parse_expr('1 < 2', evaluate=False)\nprint('1 < 2:', result, type(result))\n\n# Test other relational operators\nresult2 = parse_expr('1 > 2', evaluate=False)\nprint('1 > 2:', result2, type(result2))\n\nresult3 = parse_expr('1 <= 2', evaluate=False)\nprint('1 <= 2:', result3, type(result3))\n\nresult4 = parse_expr('1 >= 2', evaluate=False)\nprint('1 >= 2:', result4, type(result4))\n\nresult5 = parse_expr('1 == 2', evaluate=False)\nprint('1 == 2:', result5, type(result5))\n\nresult6 = parse_expr('1 != 2', evaluate=False)\nprint('1 != 2:', result6, type(result6))\n\n# Test that evaluate=True still works\nresult7 = parse_expr('1 < 2', evaluate=True)\nprint('1 < 2 (evaluate=True):', result7, type(result7))\n\n# Test with symbols\nresult8 = parse_expr('x < 2', evaluate=False)\nprint('x < 2:', result8, type(result8))\n\n# Test chained comparison\nresult9 = parse_expr('1 < 2 < 3', evaluate=False)\nprint('1 < 2 < 3:', result9, type(result9))\n\"\n\"."},{"id":"q7","text":"Based on your analysis, do you think this task PASSED or FAILED the test suite?","options":["Passed","Failed"],"answer":"Passed","reason":"The agent's fix passed all tests in the SWE-bench Verified evaluation (reward = 1)."}]}}