Title: Recurrent model for Sequential reasoning
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Recurrent Model, Sequencial Reasoning, Test time scaling

Abstract: Recently, recurrent models with test-time scaling capability, such as chain of thought and latent recurrent reasoning models, have emerged as an effective structure for solving non-trivial tasks that require deep reasoning, such as math and coding. 
These models possess the ability to improve their performance by allowing longer reasoning iterations at the time of inference. 
However, these models are not designed to accept sequential and dynamical input, while many real-world applications as well as the survival of organisms in nature require real-time input to be processed in parallel with the reasoning. 
In this work, we provide an effective strategy for extending the test-time scaling capability of a recurrent model to real-time sequential reasoning. 
We demonstrate the efficacy of our approach on the reinforcement learning tasks and challenging synthetic problems. 

Code: https://anonymous.4open.science/r/fastslow-81DB