Diverse Prompt Mixer Hits a Wall in AI Math Challenge

In the race to push AI models to new heights, the AIMO~3 competition put a spotlight on mathematical reasoning. The competition tested diverse strategies across three major AI models with 23 experiments and 50 IMO-level math problems. But the results were underwhelming.

Why Diverse Strategies Failed

The plan was simple yet ambitious: use a Diverse Prompt Mixer to assign different reasoning strategies to various models, hoping to minimize correlated errors. This method aimed to improve the accuracy of majority voting across multiple large language model (LLM) attempts. Yet, despite the innovative approach, it became clear that the diverse strategies didn't deliver the expected results.

The main culprit? High-temperature sampling already did the heavy lifting in decorrelating errors. As a result, the diverse strategies seemed redundant. In fact, they reduced the accuracy per attempt more than they managed to reduce error correlation. It was a classic case of over-engineering a solution.

The Limits of Current AI Models

What truly stood out in these experiments wasn't the ingenuity of the strategies but the stark limit of current AI models. Across a 17-point capability gap, model capability proved to be the dominating factor by a significant margin. No matter the inference-time optimization applied, the models' inherent limitations were laid bare.

This raises a critical question: Are we focusing too much on fine-tuning strategies when the real challenge lies in enhancing model capability? It's a reminder that the AI-AI Venn diagram is getting thicker, yet the underlying compute power and model sophistication still dictate performance.

The Future of AI in Mathematical Reasoning

Despite these setbacks, the pursuit of improved AI-driven mathematical reasoning is far from over. This isn't a partnership announcement. It's a convergence of understanding that the path to AI excellence isn't just through creative strategy but also through reliable model enhancement.

As AI models continue to evolve, the need for better financial plumbing and compute infrastructure becomes more apparent. If agents have wallets, who holds the keys to unlock their full potential? The industry must grapple with empowering models not just through strategies but through substantial capability upgrades.