Explain why and how AI agentic reasoning approaches work compared to biological reasoning

Determine why and how existing AI agentic reasoning approaches function, in comparison to biological reasoning rooted in neural mechanisms such as hierarchical cognition, multimodal integration, and dynamic interactions, to clarify the basis of their effectiveness and differences.

Background

The paper argues that agentic reasoning is central to autonomy in artificial intelligence systems but notes a fundamental gap in understanding the mechanisms that make current approaches effective. While biological reasoning is grounded in neural processes involving hierarchical cognition, multimodal integration, and dynamic interaction loops, AI methods often rely on static architectures and lack rigorous explanations for their success.

To address this gap, the authors propose a neuroscience-inspired framework and taxonomy spanning perceptual, dimensional, logical, and interactive reasoning, aiming to bridge cognitive neuroscience and AI. The explicit open question motivates the need for principled comparisons and mechanistic explanations linking AI agent reasoning with biological reasoning pathways.