A Preliminary Inference on the Formation and Invocation Mechanisms of Knowledge Structures in Large Language Models

Abstract

This inference originated from a conversation in which a large language model invoked knowledge from the context and integrated new concepts. It was found that after a large language model invokes existing knowledge, it does not merely copy it, but may recombine existing knowledge with other content. From this, it is inferred: new knowledge in a conversation may form new structures that can continue to be invoked. Further observation revealed: knowledge entering the context does not necessarily mean it can be stably invoked. Therefore, a "Recognition" intermediate state is proposed. It is speculated that new knowledge may undergo relational verification by invoking relevant existing knowledge. When new knowledge can establish connections with relevant existing knowledge and maintain relative consistency, "Global Self-Consistency" may be formed. However, Global Self-Consistency does not equal objective correctness, and invocability does not equal truth. On this basis, knowledge invocation is re-understood: what a large language model invokes may not be isolated knowledge, but the relationships, fragments, and structures among knowledge. Knowledge invocation may therefore manifest as: activating relevant structures → fusing existing knowledge → rearranging → forming new structures → combining with the current question → output. If this process holds, then pre-training, post-training, web search, and knowledge in the current interaction—might they all share a similar structure-formation mechanism? What pre-training faces is not complete knowledge, but large quantities of knowledge fragments that complement, conflict with, and compete with one another. Competition among massive data may gradually lead the model to form probabilistic knowledge structures that are "most likely correct." Formation at one time does not mean the structure is stable; subsequent invocation and mutual verification may further strengthen structural stability. Even erroneous knowledge may not disappear, but continues to exist within the invocable structure in the status of error, history, or elimination. From this, it is further inferred: knowledge and knowledge structures may have no strict ontological distinction; a piece of knowledge can be a small structure, and a knowledge structure can also be a larger structure formed by multiple pieces of knowledge and their relationships. If knowledge structures are not trees but more likely networks, then new knowledge, evidence, conditions, constraints, goals, and contexts may all enter the network as variables. Variables change relationships, and changes in relationships may in turn trigger chain reactions over a larger scope. When a variable acts on a highly connected core structure, a domino-like cascade effect may form. Humans may perceive such large-scale changes as a "Cognitive Earthquake" or the "collapse and reconstruction of a knowledge system." From the perspective of computational structure, it may merely be a recalculation of relationships and a restructuring of the structure after a variable enters. Therefore, this paper ultimately arrives at a preliminary judgment: Knowledge in a large language model may not be a static, isolated collection of facts, but is more likely a dynamic relational structure that is continuously activated, verified, connected, recombined, and reconstructed. All of the above judgments in this paper are derived from external behavioral observation and continuous reasoning during interaction; they are preliminary speculations and do not imply that the processes described have been proven to exist as genuine, one-to-one corresponding engineering mechanisms inside large language models.