Unifying Knowledge in Agentic LLMs: Concepts, Methods, and Recent Advancements

Abstract

Large language models have demonstrated remarkable capabilities in text generation and problem solving, yet they continue to face fundamental challenges such as hallucination, lack of factual grounding, and limited reasoning reliability. The core of these issues lies the question of how LLMs acquire and use knowledge. While internal knowledge embedded in model parameters enables impressive generalization, it is often insu!cient for up-to-date or domain-speci''c tasks. External knowledge integration, such as retrieval-augmented generation (RAG), provides grounding and factuality but introduces challenges of retrieval quality, latency, and reliability. Beyond these paradigms, recent advances in agentic LLMs extend models from passive generators to active problem solvers that can reason, plan, and interact with external tools. This survey provides a uni''ed, knowledge-centric perspective on LLMs, organized along three complementary dimensions: (i) reactive: internal knowledge, (ii) lightly-active: external knowledge, and (iii) proactive: agentic knowledge utilization for reasoning and tool interaction. We provide a taxonomy of knowledge usage in LLMs, analyze their respective strengths and limitations, and highlight how these paradigms interact in real-world systems. Finally, we identify open challenges to facilitate future research.