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(How) Do reasoning models reason?
Subbarao Kambhampati1, Kaya Stechly1, Karthik Valmeekam1
1School of Computing & Augmented Intelligence, Arizona State University, Tempe, Arizona, USA.
Large reasoning models show promise and power, but understanding their capabilities and limitations is crucial for future AI development. This perspective offers a unifying view of these advanced AI systems.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
Background:
- Recent advancements have led to sophisticated large reasoning models.
- Models like OpenAI o1 and DeepSeek R1 represent a new frontier in AI.
Purpose of the Study:
- To offer a broad, unifying perspective on large reasoning models.
- To discuss their potential, underlying power, common misconceptions, and inherent limitations.
Main Methods:
- Literature review of recent large reasoning models.
- Conceptual analysis of their capabilities and limitations.
Main Results:
- Identified key sources of power in these models.
- Clarified prevalent misconceptions surrounding their abilities.
- Outlined significant limitations that warrant further research.
Conclusions:
- A comprehensive understanding of large reasoning models is essential.
- Further research is needed to address their limitations and harness their full potential.
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