Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding

A new research paper submitted to arXiv argues that shaping schemas through advanced language representation is the critical next step for expanding Large Language Model (LLM) intelligence. The authors contend that while natural language is the default medium for LLMs, its limited expressive capacity creates bottlenecks in complex problem-solving. Mere scaling and knowledge internalization are insufficient for effective application. Instead, the study posits that an LLM's knowledge activation and organization depend heavily on the structural and symbolic sophistication of the language used to represent tasks. The paper provides both a formalization of this claim and empirical evidence. It reviews recent methodologies showing performance gains from deliberate language representation design without modifying model parameters. Additionally, controlled experiments demonstrate that LLM performance and internal feature activations vary significantly under different language representations of the same task. These findings suggest that optimizing language representation design is a promising direction for future AI research, offering a pathway to enhance LLM capabilities beyond current scaling limits.