Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience

Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while handling the uncertainty of the environment. However, what makes the agents truly autonomous? It is agentic reasoning, that is crucial for foundation models to develop symbolic logic, statistical correlations, or large-scale pattern recognition to process information, draw inferences, and make decisions. However, it remains unclear why and how existing agentic reasoning approaches work, in comparison to biological reasoning, which instead is deeply rooted in neural mechanisms involving hierarchical cognition, multimodal integration, and dynamic interactions. In this work, we propose a novel neuroscience-inspired framework for agentic reasoning. Grounded in three neuroscience-based definitions and supported by mathematical and biological foundations, we propose a unified framework modeling reasoning from perception to action, encompassing four core types, perceptual, dimensional, logical, and interactive, inspired by distinct functional roles observed in the human brain. We apply this framework to systematically classify and analyze existing AI reasoning methods, evaluating their theoretical foundations, computational designs, and practical limitations. We also explore its implications for building more generalizable, cognitively aligned agents in physical and virtual environments. Finally, building on our framework, we outline future directions and propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting. By bridging cognitive neuroscience and AI, this work offers a theoretical foundation and practical roadmap for advancing agentic reasoning in intelligent systems. The associated project can be found at: https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning .

Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases

This study presents a novel methodology utilizing a pre-trained speech recognition model for processing respiratory sound data. By incorporating medical record information, we introduce an innovative multi-modal deep-learning architecture, named Rene, which addresses the challenges of poor interpretability and underperformance in real-time clinical diagnostic response observed in previous respiratory disease-focused models. The proposed Rene architecture demonstrated significant improvements of 10.24%, 16.15%, 15.29%, and 18.90% respectively, compared to the baseline across four tasks related to respiratory event detection and audio record classification on the SPRSound database. In patient disease prediction tests on the ICBHI database, the architecture exhibited improvements of 23% in the mean of average score and harmonic score compared to the baseline. Furthermore, we developed a real-time respiratory sound discrimination system based on the Rene architecture, featuring a dual-thread design and compressed model parameters for simultaneous microphone recording and real-time dynamic decoding. Employing state-of-the-art Edge AI technology, this system enables rapid and accurate responses for respiratory sound auscultation, facilitating deployment on wearable clinical detection devices to capture incremental data, which can be synergistically evolved with large-scale models deployed on cloud servers for downstream tasks.