LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
Jiashuo Liu, Jiayun Wu, Chunjie Wu, Jingkai Liu, Zaiyuan Wang, Huan Zhou, Wenhao Huang, Hongseok Namkoong
2025-12-25
Summary
This paper introduces a new way to evaluate Large Language Models (LLMs), moving beyond simply averaging scores on different tests to a system that simulates a competition between the models.
What's the problem?
Currently, evaluating LLMs is tricky because we use a lot of different tests, and it's hard to figure out how much each test *should* matter when comparing models. Existing methods also don't show how a model performs under pressure – like if it's consistently good or if it gets easily tripped up when facing a series of challenging tasks one after another. Basically, current evaluations are static and don't reflect a real-world, dynamic competitive environment.
What's the solution?
The researchers created a framework called Competitive Swiss-System Dynamics (CSD). Think of it like a tournament where models are paired up based on how well they've done so far. After each 'round' (a set of tests), models are matched against others with similar records. To make the results more reliable, they ran the tournament many times using computer simulations. They also analyzed how models handle failure, figuring out if they're consistently reliable or take big risks to achieve high scores.
Why it matters?
This new evaluation method is important because it gives a more realistic and detailed picture of an LLM's abilities. It doesn't just tell you a model's overall score, but also how it performs in different situations and how likely it is to fail under pressure. This is crucial for choosing the right model for a specific task and understanding its limitations, leading to safer and more effective use of these powerful AI tools.
Abstract
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that effectively aggregates performance across multiple ability dimensions. Primarily using static scoring, current evaluation methods are fundamentally limited. They struggle to determine the proper mix ratio across diverse benchmarks, and critically, they fail to capture a model's dynamic competitive fitness or its vulnerability when confronted with sequential, high-stakes tasks. To address this, we introduce the novel Competitive Swiss-System Dynamics (CSD) framework. CSD simulates a multi-round, sequential contest where models are dynamically paired across a curated sequence of benchmarks based on their accumulated win-loss record. And Monte Carlo Simulation (N=100,000 iterations) is used to approximate the statistically robust Expected Win Score (E[S_m]), which eliminates the noise of random pairing and early-round luck. Furthermore, we implement a Failure Sensitivity Analysis by parameterizing the per-round elimination quantity (T_k), which allows us to profile models based on their risk appetite--distinguishing between robust generalists and aggressive specialists. We demonstrate that CSD provides a more nuanced and context-aware ranking than traditional aggregate scoring and static pairwise models, representing a vital step towards risk-informed, next-generation LLM evaluation.