LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics

TL;DR

This paper introduces a dynamic, multi-round contest framework called CSD for evaluating LLMs across multiple benchmarks, capturing their competitive fitness and risk profile more effectively than static scores.

Contribution

The paper proposes the novel CSD framework that simulates sequential contests and uses Monte Carlo simulation to robustly rank LLMs across diverse benchmarks.

Findings

CSD provides nuanced, context-aware rankings of LLMs.

It captures models' risk appetite and robustness.

It outperforms traditional static scoring methods.

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.