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| # AI_Fiends_4_This |
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| Execution-verified repair trajectory dataset developed in collaboration with WithIn Us AI. |
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| ## Overview |
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| AI_Fiends_4_This is a large-scale execution-grounded synthetic repair dataset focused on: |
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| - autonomous debugging |
| - repair supervision |
| - execution-aware reasoning |
| - traceback interpretation |
| - assertion verification |
| - process-supervised fine-tuning |
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| ## Dataset Statistics |
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| - Total Rows: 50000 |
| - Domains: math, code, science, reasoning, algorithms, data_processing, automation, debugging |
| - Bug Types: syntax, name_error, type_error, logic, assertion, index_error, key_error, zero_division |
| - Repair Success Rate: 100.0% |
| - Execution Verified: Yes |
| - Assertions Included: Yes |
| - Deduplicated: Yes |
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| ## Schema |
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| - prompt |
| - domain |
| - bug_type |
| - difficulty |
| - code_v1 |
| - stdout_v1 |
| - stderr_v1 |
| - repair_reasoning |
| - code_v2 |
| - execution_output |
| - stderr_v2 |
| - repair_verified |
| - runtime_ms |
| - traceback_chars |
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| ## Focus |
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| This dataset is designed for: |
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| - execution-aware LLMs |
| - autonomous coding agents |
| - repair-focused SFT |
| - debugging systems |
| - process reward modeling |
| - recursive execution training |
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| ## Developed By |
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| WithIn Us AI |
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