The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles

• URL: http://arxiv.org/abs/2502.01081v1
• Date: Mon, 03 Feb 2025 05:47:04 GMT
• Title: The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles
• Authors: Vernon Y. H. Toh, Yew Ken Chia, Deepanway Ghosal, Soujanya Poria,
• Abstract summary: OpenAI's releases of o1 and o3 mark a paradigm shift in Large Language Models towards advanced reasoning capabilities.<n>We track the evolution of the GPT-[n] and o-[n] series models on challenging multimodal puzzles.<n>The superior performance of o1 comes at nearly 750 times the computational cost of GPT-4o, raising concerns about its efficiency.
• Score: 29.214813685163218
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The releases of OpenAI's o1 and o3 mark a significant paradigm shift in Large Language Models towards advanced reasoning capabilities. Notably, o3 outperformed humans in novel problem-solving and skill acquisition on the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI). However, this benchmark is limited to symbolic patterns, whereas humans often perceive and reason about multimodal scenarios involving both vision and language data. Thus, there is an urgent need to investigate advanced reasoning capabilities in multimodal tasks. To this end, we track the evolution of the GPT-[n] and o-[n] series models on challenging multimodal puzzles, requiring fine-grained visual perception with abstract or algorithmic reasoning. The superior performance of o1 comes at nearly 750 times the computational cost of GPT-4o, raising concerns about its efficiency. Our results reveal a clear upward trend in reasoning capabilities across model iterations, with notable performance jumps across GPT-series models and subsequently to o1. Nonetheless, we observe that the o1 model still struggles with simple multimodal puzzles requiring abstract reasoning. Furthermore, its performance in algorithmic puzzles remains poor. We plan to continuously track new models in the series and update our results in this paper accordingly. All resources used in this evaluation are openly available https://github.com/declare-lab/LLM-PuzzleTest.

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