Papers › CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven...
CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging
Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez
Large Language Models (LLMs) have made significant strides in code generation and problem solving. Current approaches employ external tool-based iterative debuggers that use compiler or other tool-based runtime feedback to refine coarse programs generated by various methods. However, the effectiveness of these approaches heavily relies on the quality of the initial code generation, which remains an open challenge. In this paper, we introduce CodeSim, a novel multi-agent code generation framework that comprehensively addresses the stages of program synthesis-planning, coding, and debugging-through a human-like perception approach. As human verifies their understanding of any algorithms through visual simulation, CodeSim uniquely features a method of plan verification and internal debugging through the step-by-step simulation of input/output. Extensive experiments across seven challenging competitive problem-solving and program synthesis benchmarks demonstrate CodeSim's remarkable code generation capabilities. Our framework achieves new state-of-the-art (pass@1) results-(HumanEval 95.1%, MBPP 90.7%, APPS 22%, and CodeContests 29.1%). Furthermore, our method shows potential for even greater enhancement when cascaded with external debuggers. To facilitate further research and development in this area, we have open-sourced our framework in this link (https://kagnlp.github.io/codesim.github.io/).
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | APPS | CodeSim (GPT4) | Competition Pass@1 | 0.81 | #9 of 18 | Archive leaderboard | report |
| Code Generation | APPS | CodeSim (GPT4) | Interview Pass@1 | 4.21 | #9 of 18 | Archive leaderboard | report |
| Code Generation | APPS | CodeSim (GPT4) | Introductory Pass@1 | 26.04 | #9 of 18 | Archive leaderboard | report |
| Code Generation | CodeContests | CodeSim (GPT4) | Test Set pass@1 | 28.4 | #4 of 8 | Archive leaderboard | report |
| Code Generation | MBPP | CodeSim (GPT4o) | Accuracy | 90.7 | #6 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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