No Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows
Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Output (I/O) test cases. However, this does not work for scientific workflows, where I/O test cases do not exist, and generating them requires solving the very problem at hand. To address this, we introduce MOSAIC, a training-free multi-agent framework for scientific code generation without I/O supervision. Instead of execution feedback, MOSAIC employs a student-teacher knowledge distillation framework that grounds generation through domain-specific examples and structured problem decomposition. To further mitigate hallucinations across chaine...
| Authors | Siddeshwar Raghavan, Tanwi Mallick |
|---|---|
| Primary category | cs.SE |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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