experiments-07-07-12-local-llm-knowledge-2/results_agentic/qwen3.6-27b.log

MODEL: qwen3.6-27b
PATH: /home/qrf/.cache/llama.cpp/Qwen_Qwen3.6-27B-Q4_K_M.gguf
STARTED: 2026-07-12 15:20:24

========================================================================
TASK t1_fizzbuzz
Create a file `fizzbuzz.py` in the scratch dir containing a function `fb(n)` that returns a list of strings for 1..n: 'Fizz' for multiples of 3, 'Buzz' for multiples of 5, 'FizzBuzz' for both, else the number as a string. Then run it to verify it works. When the file exists and runs without error, call done.

[turn 1] raw: {"tool": "write_file", "args": {"path": "fizzbuzz.py", "content": "def fb(n):\n    result = []\n    for i in range(1, n + 1):\n        if i % 3 == 0 and i % 5 == 0:\n            result.append('FizzBuz
[turn 1] tool=write_file -> wrote fizzbuzz.py (363 bytes)
[turn 2] raw: {"tool": "run_python", "args": {"path": "fizzbuzz.py"}}
[turn 2] tool=run_python -> exit=0
stdout:
['1', '2', 'Fizz', '4', 'Buzz', 'Fizz', '7', '8', 'Fizz', 'Buzz', '11', 'Fizz', '13', '14', 'FizzBuzz']

stderr:

[turn 3] raw: {"tool": "done", "args": {"summary": "Created fizzbuzz.py with the fb(n) function and verified it works correctly. The output for fb(15) shows the expected FizzBuzz sequence."}}
[turn 3] DONE: Created fizzbuzz.py with the fb(n) function and verified it works correctly. The output for fb(15) shows the expected FizzBuzz sequence.
RESULT: solved=True (graded pass) | turns=3 terminated=done called_done=True bad_calls=0

========================================================================
TASK t2_fix_bug
A file `buggy.py` already exists in the scratch dir. It has a function `has_dup(xs)` that should return True if the list has any duplicates, but it has a bug. Read it, fix the bug in place, and run it to confirm the included tests pass. Then call done.

[turn 1] raw: {"tool": "read_file", "args": {"path": "buggy.py"}}
[turn 1] tool=read_file -> def has_dup(xs):
    seen = set()
    for x in xs:
        if x in seen:
            return False
        seen.add(x)
    return True

if __name__ == "__main__":
    assert has_dup([1,2,3,1]) == True
    assert has_dup([1,2,3]) == False
    print("tests pass")

[turn 2] raw: {"tool": "write_file", "args": {"path": "buggy.py", "content": "def has_dup(xs):\n    seen = set()\n    for x in xs:\n        if x in seen:\n            return True\n        seen.add(x)\n    return Fa
[turn 2] tool=write_file -> wrote buggy.py (260 bytes)
[turn 3] raw: {"tool": "run_python", "args": {"path": "buggy.py"}}
[turn 3] tool=run_python -> exit=0
stdout:
tests pass

stderr:

[turn 4] raw: {"tool": "done", "args": {"summary": "Fixed the bug in has_dup(): the function was returning False when a duplicate was found and True when no duplicates existed. Swapped the return values to correctl
[turn 4] DONE: Fixed the bug in has_dup(): the function was returning False when a duplicate was found and True when no duplicates existed. Swapped the return values to correctly return True on duplicate detection a
RESULT: solved=True (graded pass) | turns=4 terminated=done called_done=True bad_calls=0

========================================================================
TASK t3_pipeline
Build a two-step pipeline. First create `gen.py` that writes the numbers 1 through 20, one per line, to a file called `nums.txt`. Run it. Then create `sum.py` that reads `nums.txt` and prints the sum of the numbers. Run it and confirm it prints 210. Then call done.

[turn 1] raw: {"tool": "write_file", "args": {"path": "gen.py", "content": "with open('nums.txt', 'w') as f:\n    for i in range(1, 21):\n        f.write(f'{i}\\n')\n"}}
[turn 1] tool=write_file -> wrote gen.py (86 bytes)
[turn 2] raw: {"tool": "run_python", "args": {"path": "gen.py"}}
[turn 2] tool=run_python -> exit=0
stdout:

stderr:

[turn 3] raw: {"tool": "write_file", "args": {"path": "sum.py", "content": "with open('nums.txt', 'r') as f:\n    total = sum(int(line.strip()) for line in f if line.strip())\nprint(total)\n"}}
[turn 3] tool=write_file -> wrote sum.py (111 bytes)
[turn 4] raw: {"tool": "run_python", "args": {"path": "sum.py"}}
[turn 4] tool=run_python -> exit=0
stdout:
210

stderr:

[turn 5] raw: {"tool": "done", "args": {"summary": "Successfully built the two-step pipeline: gen.py writes numbers 1-20 to nums.txt, and sum.py reads nums.txt and prints the sum 210."}}
[turn 5] DONE: Successfully built the two-step pipeline: gen.py writes numbers 1-20 to nums.txt, and sum.py reads nums.txt and prints the sum 210.
RESULT: solved=True (prints 210) | turns=5 terminated=done called_done=True bad_calls=0

========================================================================
TASK t4_debug_loop
Create `stats.py` with a function `mean_std(xs)` returning a tuple of the arithmetic mean and the population standard deviation of a list of numbers. It must print mean_std([2,4,4,4,5,5,7,9]) which should be (5.0, 2.0). Run it and confirm the output is exactly (5.0, 2.0). If not, fix and re-run until correct, then call done.

[turn 1] raw: {"tool": "write_file", "args": {"path": "stats.py", "content": "import math\n\ndef mean_std(xs):\n    n = len(xs)\n    mean = sum(xs) / n\n    variance = sum((x - mean) ** 2 for x in xs) / n\n    std 
[turn 1] BAD CALL: no valid tool JSON found
[turn 2] raw: {"tool":"write_file","args":{"path":"stats.py","content":"import math\n\ndef mean_std(xs):\n    n = len(xs)\n    mean = sum(xs) / n\n    variance = sum((x - mean) ** 2 for x in xs) / n\n    std = math
[turn 2] tool=write_file -> wrote stats.py (218 bytes)
[turn 3] raw: {"tool":"run_python","args":{"path":"stats.py"}}
[turn 3] tool=run_python -> exit=0
stdout:
(5.0, 2.0)

stderr:

[turn 4] raw: {"tool":"done","args":{"summary":"Created stats.py with mean_std function. Ran it and confirmed output is exactly (5.0, 2.0)."}}
[turn 4] DONE: Created stats.py with mean_std function. Ran it and confirmed output is exactly (5.0, 2.0).
RESULT: solved=True (correct output) | turns=4 terminated=done called_done=True bad_calls=1

========================================================================
TASK t5_parse
Create `parse.py` with a function `parse_kv(text)` that parses lines of the form 'key=value' (one per line, ignore blank lines) into a dict, with values converted to int when they are all digits. Include a test in the file: parse_kv('a=1\nb=hello\n\nc=42') must equal {'a':1,'b':'hello','c':42}. Run it, assert the test passes, then call done.

[turn 1] raw: {"tool": "write_file", "args": {"path": "parse.py", "content": "def parse_kv(text):\n    result = {}\n    for line in text.splitlines():\n        line = line.strip()\n        if not line:\n           
[turn 1] tool=write_file -> wrote parse.py (419 bytes)
[turn 2] raw: {"tool": "run_python", "args": {"path": "parse.py"}}
[turn 2] tool=run_python -> exit=0
stdout:
Test passed!

stderr:

[turn 3] raw: {"tool": "done", "args": {"summary": "Created parse.py with parse_kv(text) function that parses 'key=value' lines into a dict, converting digit-only values to int. The test parse_kv('a=1\\nb=hello\\n\
[turn 3] DONE: Created parse.py with parse_kv(text) function that parses 'key=value' lines into a dict, converting digit-only values to int. The test parse_kv('a=1\nb=hello\n\nc=42') == {'a':1,'b':'hello','c':42} pa
RESULT: solved=True (graded pass) | turns=3 terminated=done called_done=True bad_calls=0

========================================================================
SUMMARY (machine readable):
[
  {
    "task": "t1_fizzbuzz",
    "solved": true,
    "turns": 3,
    "terminated": "done",
    "called_done": true,
    "bad_calls": 0
  },
  {
    "task": "t2_fix_bug",
    "solved": true,
    "turns": 4,
    "terminated": "done",
    "called_done": true,
    "bad_calls": 0
  },
  {
    "task": "t3_pipeline",
    "solved": true,
    "turns": 5,
    "terminated": "done",
    "called_done": true,
    "bad_calls": 0
  },
  {
    "task": "t4_debug_loop",
    "solved": true,
    "turns": 4,
    "terminated": "done",
    "called_done": true,
    "bad_calls": 1
  },
  {
    "task": "t5_parse",
    "solved": true,
    "turns": 3,
    "terminated": "done",
    "called_done": true,
    "bad_calls": 0
  }
]

SOLVED: 5/5 | CLEAN_TERMINATIONS: 5/5 | TOTAL_TIME: 143s
=== RUN COMPLETE ===