Persona-Driven Benchmarking for Generalizable and Human-Aware Artificial General Intelligence

This research paper, "Persona-Driven Benchmarking for Generalizable and Human-Aware Artificial General Intelligence," proposes a novel architectural solution to transition current Large Language Models (LLMs) from sophisticated pattern-matchers to genuine Artificial General Intelligence (AGI) agents. The core argument of the paper is that achieving AGI is primarily an architectural challenge (creating a "gearbox" of metacognitive functions) rather than solely a scaling or fundamental modeling problem (the "engine"). The proposed framework is an architectural overlay composed of three synergistic components that address the key limitations of contemporary LLMs: lack of persistent memory, poor accountability, and narrow human-centric reasoning. Key Components of the Proposed Architecture Persona Module (for Dynamic Contextualization and Self-Awareness): Function: Handles the dynamic contextualization of all interactions, performs causal intent inference regarding the user's needs, and manages the AGI's evolving, internal self-identity (or "self-model"). Goal: To enable the AGI to move beyond simple response generation to goal-oriented optimization based on a deep, human-aware understanding of the user and its own capabilities. Benchmarking Loop (for Metacognitive Self-Correction): Function: This component acts as the AGI's metacognitive self-correction engine. It rigorously audits the LLM's output against a goal-specific metric, performs causal inference to determine why a failure occurred, and then dictates a permanent learning path. Goal: To foster an internal, continuous learning cycle that ensures the AGI is accountable and capable of generalized skill transfer from one task to a similar, new one. FedRAG (Federated Reflective Augmented Generation): Function: A novel, persistent external memory layer that functions as a dynamic knowledge graph. It is crucial because it decouples learned skills and state from the static LLM weights. It stores successful outcomes, critical causal inferences from the Benchmarking Loop, user preferences, and Persona state updates. Goal: To provide the AGI with persistent, generalizable memory and ensure that learning is permanent, efficient, and does not lead to catastrophic forgetting or excessive model scaling. Central Thesis The paper asserts that the integrated, recursive loop formed by these three components—where the Benchmarking Loop's reflective output is written to the FedRAG memory for use by the Persona Module in future interactions—is the necessary architectural scaffold to achieve true, generalizable, and human-aware AGI.