MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding

Siddeshwar Raghavan, Tanwi Mallick

Keywords: LLM code generation, scientific coding, multi-agentic system

Abstract: We present MOSAIC, a large language model (LLM) based multi-agent framework for tackling complex scientific coding tasks. Unlike general-purpose programming, scientific coding requires rigorous algorithmic reasoning, substantial domain expertise, high numerical precision, extended context management, and the ability to decompose and solve interdependent subproblems under a larger objective. To address these challenges, we design and integrate specialized agents responsible for self-reflection, planning, coding, and debugging inspired by a teacher–student paradigm. This architecture balances open-ended exploration, ensuring executability while maintaining a consolidated context to minimize hallucinations. We evaluate MOSAIC on scientific coding benchmark and show that our framework, together with its specialized agents, outperforms existing approaches in accuracy, robustness, and interpretability.

Submission Number: 65