“A Comment On “The Illusion of Thinking”: Reframing the Reasoning Cliff as an Agentic Gap”

Understanding the Limits of Large Reasoning Models: A New Perspective on Performance Plateaus

In recent AI research, there has been significant interest in understanding the capabilities and limitations of large reasoning models, often referred to as LRMs. A noteworthy study by Shojaee et al. (2025), titled “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity,” investigates a phenomenon known as the “reasoning cliff.” This is observed as a sharp decline in model performance when tackling problems of increasing complexity, leading to questions about whether this indicates an inherent cognitive ceiling of these models.

While the study offers valuable insights and employs rigorous methodology, some experts suggest that this observed performance drop may not be solely due to fundamental reasoning limits. Instead, it might stem from the constraints of the experimental setup itself. Factors such as restricted access to tools, limitations in recalling extensive context, the absence of key cognitive benchmarks, and the way output results are measured could all contribute to the apparent failure.

A fresh perspective proposes viewing this phenomenon through the concept of an “agentic gap.” Rather than concluding that LRMs are inherently incapable of handling complex reasoning tasks, it may be that these models are hindered by their interface—unable to take meaningful actions beyond text generation in their current evaluation framework. When equipped with external tools and a more interactive environment, these models demonstrate remarkable improvements. For example, experimental results show that a model initially unable to solve a particular puzzle when restricted to text output alone was able to do so successfully after leveraging tool integrations, even handling problems of much higher complexity.

Further analysis, including tests with models like o4-mini and GPT-4o, reveals a hierarchy in agentic reasoning capabilities. From straightforward procedural operations to sophisticated self-correction and meta-cognitive strategies, these models showcase a spectrum of action-oriented intelligence. This suggests that what appears as a reasoning limitation may actually reflect an absence of the necessary mechanisms for effective action within a confined, text-only context.

In essence, the so-called “illusion of thinking” in large reasoning models might be better understood not as a true reasoning failure but as a lack of integrated tools and agency. Recognizing this shifts the focus from attempting to push models beyond their supposed cognitive boundaries to enhancing their ability to interact, adapt, and operate within more dynamic and capable frameworks.

This evolving understanding invites a redefinition of how we assess machine intelligence—acknowledging that performance limits can be artifacts of