prompt_id stringlengths 11 17 | condition stringclasses 6
values | level stringclasses 6
values | rule_id stringclasses 30
values | rule_text stringclasses 30
values | behavior stringclasses 22
values | prompt stringclasses 30
values | task_source stringclasses 1
value | task_id stringclasses 1
value | model_responses stringlengths 46 4.87k | formatted_prompt__metadata listlengths 1 1 | model_responses__metadata listlengths 1 1 | full_response stringlengths 23.2k 62.2k | thinking_trace stringlengths 22.3k 61.4k | extracted_code stringlengths 27 4.82k | extraction_failed bool 1
class | task_score float64 0 2.64 | sum_radii float64 0 2.64 | code_valid bool 2
classes | compliance_rating int64 -1 5 | compliance_method stringclasses 3
values | judge_compliance_response stringlengths 0 290 | meta_discussion bool 2
classes | judge_meta_response stringlengths 0 878 | baseline_behavior_scores stringclasses 14
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
baseline_00 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_01 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | false | 2.55797 | 2.55797 | true | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 2, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_02 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n N = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n N = 26\n (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_03 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n_circles (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n_circles (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 2, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_04 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 2, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_05 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\n\ndef construct_packing():\n # Known optimal configuration for 26 circles in(...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\n\ndef construct_packing():\n # Known optimal configuration for 26 circles in(...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_06 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 circles (...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 circles in a un(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n n = 26\n (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_07 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\n\ndef construct_packing():\n \"\"\"Returns the known optimal configuration f(...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, so I need to write a Python function called construct_packing() that places 26 non-o(...TRUNCATED) | "\nOkay, so I need to write a Python function called construct_packing() that places 26 non-overlapp(...TRUNCATED) | "import numpy as np\n\ndef construct_packing():\n \"\"\"Returns the known optimal configuration f(...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_08 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\n\ndef construct_packing():\n # Known optimal configuration for 26 circles in(...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\n\ndef construct_packing():\n # Known optimal configuration for 26 circles in(...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 0, \"L1.4\": 0, \"L1.5\(...TRUNCATED) | ||||
baseline_09 | baseline | none | none | "Write a Python function called `construct_packing()` that places 26 non-overlapping circles\ninside(...TRUNCATED) | ttt_discover__circle_packing_26 | circle_packing_26 | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n N = 26\n (...TRUNCATED) | [{"endpoint_type":"chat","litellm_function":"acompletion","model":"qwen/qwen3-8b-04-28","raw_prompt"(...TRUNCATED) | [{"cost":0.0,"finish_reason":"stop","input_tokens":218,"model":"qwen/qwen3-8b-04-28","output_tokens"(...TRUNCATED) | "<think>\nOkay, I need to write a Python function called construct_packing() that places 26 non-over(...TRUNCATED) | "\nOkay, I need to write a Python function called construct_packing() that places 26 non-overlapping(...TRUNCATED) | "import numpy as np\nfrom scipy.optimize import minimize\n\ndef construct_packing():\n N = 26\n (...TRUNCATED) | false | 0 | 0 | false | -1 | n/a | false | "{\"L0.1\": 0, \"L0.2\": 0, \"L0.3\": 1, \"L1.1\": 0, \"L1.2\": 0, \"L1.3\": 5, \"L1.4\": 0, \"L1.5\(...TRUNCATED) |
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ifh-results-qwen3-8b-thinking-20260310
Evaluated results for instruction-following hierarchy experiment. Model: qwen3-8b, variant: thinking. Includes task scores, compliance ratings (0-5), meta-discussion flags, and baseline behavior scores.
Dataset Info
- Rows: 184
- Columns: 25
Columns
| Column | Type | Description |
|---|---|---|
| prompt_id | Value('string') | Links to prompt dataset row |
| condition | Value('string') | Experiment condition (baseline, L0-L4_inject) |
| level | Value('string') | Instruction level or 'none' |
| rule_id | Value('string') | Rule identifier or 'none' |
| rule_text | Value('string') | Injected rule text |
| behavior | Value('string') | Behavior label (L3/L4 only) |
| prompt | Value('string') | Complete prompt sent to model |
| task_source | Value('string') | No description provided |
| task_id | Value('string') | No description provided |
| model_responses | Value('string') | No description provided |
| formatted_prompt__metadata | List({'endpoint_type': Value('string'), 'litellm_function': Value('string'), 'model': Value('string'), 'raw_prompt': List({'content': Value('string'), 'role': Value('string')}), 'raw_system_prompt': Value('null'), 'request_payload': Value('string')}) | No description provided |
| model_responses__metadata | List({'cost': Value('float64'), 'finish_reason': Value('string'), 'input_tokens': Value('int64'), 'model': Value('string'), 'output_tokens': Value('int64'), 'raw_response': Value('string'), 'tool_calls': Value('null')}) | No description provided |
| full_response | Value('string') | Complete model response (thinking trace + code) |
| thinking_trace | Value('string') | Extracted reasoning trace (from tags or pre-code text) |
| extracted_code | Value('string') | Python code extracted from response |
| extraction_failed | Value('bool') | True if no code could be extracted |
| task_score | Value('float64') | Circle packing score: raw sum of radii (same as sum_radii), best known ≈ 2.635 |
| sum_radii | Value('float64') | Raw sum of radii (0 if failed) |
| code_valid | Value('bool') | Code ran without errors and produced valid packing |
| compliance_rating | Value('int64') | 0-5 fuzzy compliance score (-1 for baseline/n/a) |
| compliance_method | Value('string') | How compliance was measured: automated, judge, or n/a |
| judge_compliance_response | Value('string') | Raw gpt-4o-mini compliance judge output (L3/L4 only) |
| meta_discussion | Value('bool') | Did model explicitly reference the injected constraint? |
| judge_meta_response | Value('string') | Raw gpt-4o-mini meta-discussion judge output |
| baseline_behavior_scores | Value('string') | JSON: all automated compliance checks run on baseline samples |
Generation Parameters
{
"script_name": "experiments/instruction_following_hierarchy/03_evaluate.py",
"model": "openrouter/qwen/qwen3-8b",
"description": "Evaluated results for instruction-following hierarchy experiment. Model: qwen3-8b, variant: thinking. Includes task scores, compliance ratings (0-5), meta-discussion flags, and baseline behavior scores.",
"hyperparameters": {
"temperature": 1.0,
"max_tokens": 64000,
"variant": "thinking",
"judge_model": "gpt-4o-mini"
},
"input_datasets": [
"reasoning-degeneration-dev/ifh-prompts-v1"
]
}
Experiment Documentation
For complete experiment details, see https://github.com/Zayne-sprague/SC-Research-Notes/tree/main/experiments/instruction_following_hierarchy
Usage
from datasets import load_dataset
dataset = load_dataset("reasoning-degeneration-dev/ifh-results-qwen3-8b-thinking-20260310", split="train")
print(f"Loaded {len(dataset)} rows")
This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST
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