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

2025-12-24

AI Summary

Existing LLM evaluation methods rely on static weighting or pairwise scoring, limiting their ability to integrate multidimensional capabilities, determine principled benchmark weights, and characterize models’ dynamic adaptability and fragility in sequential high-stakes tasks. This paper proposes a Dynamic Competitive Evaluation Framework: it employs a Swiss-system multi-round pairing mechanism to track win/loss trajectories in real time over serialized benchmarks; applies Monte Carlo simulation (N=10⁵) to mitigate stochastic bias; formalizes an expected score model E[Sₘ] and a parametric淘汰 mechanism Tₖ; and introduces failure sensitivity analysis—a novel method to quantify risk preference. The framework significantly enhances contextual awareness and risk discernment in rankings. Empirical results demonstrate superior discriminative power in distinguishing robust generalists from high-risk specialists, outperforming conventional static and pairwise approaches.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Sampling/Simulation-based Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Develops a competitive ranking system for LLMs across multiple benchmarks
• Addresses limitations of static scoring in capturing dynamic competitive fitness
• Introduces risk profiling to distinguish robust generalists from aggressive specialists

Innovation

• Simulates multi-round sequential contests using Swiss-system dynamics
• Uses Monte Carlo simulation to calculate robust Expected Win Score
• Implements Failure Sensitivity Analysis to profile model risk appetite