We compared different LLMs on IMO 2026 [R]

The recent comparison of Large Language Models (LLMs) against International Mathematical Olympiad (IMO) problems provides a fascinating, and somewhat humbling, glimpse into the current state of AI reasoning. The choice of IMO problems as a benchmark is particularly insightful; their novelty ensures they haven’t been absorbed into training datasets, and their complexity serves as a strong proxy for general intelligence. This aligns with ongoing efforts to rigorously evaluate LLMs beyond simple pattern recognition. As highlighted in a recent evaluation of frontier models across bias benchmarks [Evaluated 6 frontier LLMs (GPT-5.4, Claude Sonnet 4.6, Claude Opus 4.7, Gemini Pro/Flash, Grok 4.3) on political, gender, and racial bias across 8 benchmarks (~20,600 examples) [R]], the ability to handle nuanced and complex scenarios remains a critical area for improvement. The SignalPilot Labs study reinforces this, demonstrating that while frontier models like Sol and Fable achieved impressive results, even optimized harnesses couldn't consistently match their performance. The need for sophisticated orchestration, as explored with AutoFyn, underscores the challenges of effectively leveraging LLMs for intricate problem-solving.

The findings surrounding Sonnet and Opus are particularly noteworthy. Their comparatively poor performance initially, and the subsequent improvement through provider harnesses and AutoFyn, suggest that the model architecture itself might be less critical than the *way* it’s utilized. This echoes observations in other areas of LLM application; a well-designed system that intelligently guides and verifies the model's reasoning can often outperform a raw, powerful model operating in isolation. Interestingly, the GLM open-weight model showed promising results, performing similarly to Sonnet without harness and demonstrating comparable improvement with AutoFyn. This hints at the potential for more accessible and customizable solutions to emerge, democratizing access to advanced reasoning capabilities. We’ve seen similar trends in other areas, such as the development of smaller, specialized models for specific tasks, such as the recent success of 4B models in Swedish medical question answering [Open-weight 4B models approach o3-level medical question answering in Swedish [P]]. The ability to adapt and fine-tune open-weight models like GLM adds another layer of flexibility and control for developers.

However, the persistent issue of hallucination, even in a rigorously defined domain like mathematics, is a sobering reminder of the limitations of current LLMs. The failure of all sub-frontier models, including a lengthy 20-hour run, to grasp a key reduction in the most challenging problem (P3) highlights a fundamental gap between mimicking reasoning and genuinely *understanding* the underlying principles. While harnesses provided retrieval and verification capabilities, they couldn't bridge this conceptual divide. This reinforces the ongoing debate around whether current LLMs are truly “thinking” or simply generating statistically plausible sequences of words. The use of a separate frontier model for grading, combined with manual verification by former IMO medalists, demonstrates the level of scrutiny required to validate AI-generated solutions, a process that requires human oversight and expertise. Examining the audit trails [Audit Trails] provides a valuable window into the reasoning process, further illuminating where the models falter.

Looking ahead, the focus shouldn't solely be on increasing model size or complexity. Instead, we need to prioritize the development of more robust and reliable reasoning frameworks - systems that can not only generate potential solutions but also critically evaluate them and identify fundamental misunderstandings. The success of AutoFyn, despite not fully closing the gap with frontier models, offers a glimpse of a potential path forward. Can we design harnesses that actively encourage and evaluate conceptual understanding, rather than simply orchestrating the execution of pre-existing knowledge? This ongoing exploration of harness engineering, combined with the increasing accessibility of open-weight models, will be crucial in shaping the future of AI-powered problem-solving and determining whether AI can truly transcend pattern recognition to achieve genuine intelligent reasoning.