The Mind Settles: Self-Equilibrating Recurrent Networks for Latent Reasoning

Key Points

• This research aims to evaluate how Self-Equilibrating Recurrent Networks (SERN) improve reasoning and efficiency in computational models.
• Developed a Self-Equilibrating Recurrent Network (SERN-G) prototype for gameplay testing with algebra solving.
• Tested the SERN-G model against standard static models to assess error margins and state dynamics.
• Analyzed the internal state refining process of the network before outputting results.
• SERN-G achieved a solution error margin of 0.000179, outperforming standard models.
• Internal dynamics in SERN lead to faster processing times and improved reasoning capabilities.
• The study suggests a shift away from traditional static layer models toward more dynamic approaches.

Abstract