| title | MARS: Multi-Specialist LLM Relay System for Competitive Programming |
|---|---|
| authors | Andrei Mikhailov, Mikhail Burtsev, Alsu Sagirova |
| date | 2026-08-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.23918 |
| upvotes | 12 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate the choice of algorithmic technique to the backbone alone. We present MARS (Multi-Agent Relay of Specialized LLMs), a prompt-only framework in which each agent is a topic specialist---dynamic programming, graphs, strings, geometry, and so on---grounded by retrieval-augmented generation over an algorithm-theory corpus. Given a problem, retrieval selects a small team of relevant specialists; a starter writes an initial C++17 solution, and each subsequent turn runs the candidate against public examples in a sandbox, lets the active specialist keep, repair, or hand off the draft, and forwards a structured packet to the next specialist. A single infrastructure-fixer pass normalizes boilerplate at the end. On the CodeContests test split with Gemma 4, MARS reaches 0.624 pm 0.006 pass rate at 2.3 recorded pipeline stages per task (+14.4 percentage points over direct prompting), closing most of the gap to CodeSIM (0.731) at 3.3{times} lower wall-clock cost and substantially smaller variance in per-task token spend. The source code is available on GitHub: https://github.com/fckand/mars. |
cite
@misc{mikhailov2026mars,
title={MARS: Multi-Specialist LLM Relay System for Competitive Programming},
author={Andrei Mikhailov and Mikhail Burtsev and Alsu Sagirova},
year={2026},
eprint={2608.23918},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.23918}
}