Leveraging Symmetry in Multi-Agent Code Generation: A Cross-Verification Collaboration Protocol for Competitive Programming
Aoyu Song, Afizan Azman · Symmetry · 2025
Competitive programming has emerged as a critical benchmark for evaluating large language models (LLMs) in solving algorithmic problems under competitive conditions. Existing methods, such as the Sequential One-Agent Pipeline (SOP) approach, suffer from significant limitations, including the inability to effectively manage semantic drift across multiple stages, a lack of coordinated adversarial testing, and suboptimal final solutions. These issues lead to high rates of wrong answer (WA) and time-limit exceeded (TLE) errors, especially on complex problems. In this paper, we propose the Cross-Verification Collaboration Protocol (CVCP), a multi-agent framework that integrates symmetry detection, symmetry-guided adversarial testing, Round-Trip Review Protocol (RTRP), and Asynchronous Voting Resolution (AVR) to address these shortcomings. We evaluate our method on the CodeELO dataset, showing significant improvements in performance, with Elo Ratings increasing by up to 7.1% and Pass Rates for hard problems improving by as much as 1.8 times compared to the SOP baseline.