NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bangla Instruction to Python Code Generation

Hossain Shaikh Saadi, Faria Alam, Mario Sanz-Guerrero, Minh Duc Bui, Manuel Mager +1 more
11/20/2025
cs.CLcs.SE

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Abstract

This paper presents JGU Mainz's winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a $Pass@1$ score of 95.4. We also make our code public.

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Cite this paper

@article{saadi2025nalamainz,
  title  = {NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bangla Instruction to Python Code Generation},
  author = {Hossain Shaikh Saadi and Faria Alam and Mario Sanz-Guerrero and Minh Duc Bui and Manuel Mager and Katharina von der Wense},
  year   = {2025},
  eprint = {2511.16787},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.16787v1}
}

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