Title: ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Agentic code generation, Iterative refinement, sandbox verification, Chain-of-thought prompting, Retrieval-Augmented Generation

Abstract: We present Agentic Retrieval-Augmented Code Synthesis (ARCS), a system that improves LLM-based code generation without fine-tuning. ARCS operates through a budgeted synthesize–execute–repair loop over a frozen model: it retrieves relevant code context before generation, proposes candidates, executes them against tests, and repairs based on execution feedback. This retrieval-before-generation design reduces hallucination and accelerates convergence. We formalize ARCS as a state-action process with provable guarantees on termination, monotonic improvement, and bounded cost. A tiered controller (Small/Medium/Large) trades latency for accuracy predictably. On HumanEval, ARCS achieves up to 87.2\% pass@1 with Llama-3.1-405B, surpassing CodeAgent (82.3\%) while using simpler control than tree-search methods. On TransCoder, it achieves $\geq 90\%$ accuracy on most translation pairs. On a LANL scientific corpus, it improves CodeBLEU by +0.115 over baseline RAG. ARCS provides a practical, reproducible approach to reliable code synthesis using existing LLM checkpoints.

TL;DR: ARCS adapts a frozen LLM to local code by retrieving before generating and using an execute-and-repair loop, achieving competitive code synthesis and translation without fine-tuning.