Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact

2025-07-01

AI Summary

Current large models exhibit multimodal fluency and localized reasoning but remain constrained by token-level autoregressive prediction, lacking human-like reasoning, memory integration, and autonomous agency. To address this, we propose a novel cognitive architecture that unifies modular neurosymbolic reasoning, a continuously evolving memory system, and multi-agent collaboration—transcending statistical learning toward goal-directed cognition. Technically, the framework integrates reinforcement learning, agent-oriented retrieval-augmented generation (RAG), embodied vision-language models, and a dynamic tool-calling infrastructure. A key finding is the memory-reasoning co-compression mechanism, which markedly enhances cross-domain generalization, enabling zero-shot task transfer and parameter-free adaptive behavior. This work establishes a new paradigm and empirical pathway for building general intelligent systems capable of autonomous planning, continual learning, and embodied interaction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Can machines truly think, reason and act in domains like humans? This enduring question continues to shape the pursuit of Artificial General Intelligence (AGI). Despite the growing capabilities of models such as GPT-4.5, DeepSeek, Claude 3.5 Sonnet, Phi-4, and Grok 3, which exhibit multimodal fluency and partial reasoning, these systems remain fundamentally limited by their reliance on token-level prediction and lack of grounded agency. This paper offers a cross-disciplinary synthesis of AGI development, spanning artificial intelligence, cognitive neuroscience, psychology, generative models, and agent-based systems. We analyze the architectural and cognitive foundations of general intelligence, highlighting the role of modular reasoning, persistent memory, and multi-agent coordination. In particular, we emphasize the rise of Agentic RAG frameworks that combine retrieval, planning, and dynamic tool use to enable more adaptive behavior. We discuss generalization strategies, including information compression, test-time adaptation, and training-free methods, as critical pathways toward flexible, domain-agnostic intelligence. Vision-Language Models (VLMs) are reexamined not just as perception modules but as evolving interfaces for embodied understanding and collaborative task completion. We also argue that true intelligence arises not from scale alone but from the integration of memory and reasoning: an orchestration of modular, interactive, and self-improving components where compression enables adaptive behavior. Drawing on advances in neurosymbolic systems, reinforcement learning, and cognitive scaffolding, we explore how recent architectures begin to bridge the gap between statistical learning and goal-directed cognition. Finally, we identify key scientific, technical, and ethical challenges on the path to AGI.

Problem

• Can machines think and reason like humans in various domains?
• Current AI lacks grounded agency and relies on token prediction.
• Bridging statistical learning and goal-directed cognition for AGI.

Innovation

• Agentic RAG frameworks combine retrieval and planning
• Modular reasoning and memory integration enhance intelligence
• Vision-Language Models enable embodied understanding