From Found to Designed: Concepts as a Design Axis for Large Language Models

תקציר מקורי באנגליתarXiv:2607.26825v1 Announce Type: new Abstract: Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than as explicit, structured, compositional concepts. Consequently, concept-level structure is typically \emph{found} rather than \emph{designed}: it is recovered after training through probing or dictionary learning, with no architectural guarantee of stability, compositionality, controllability, or alignment with human conceptual organization. We argue that concepts should instead be treated as a design axis for LLMs, and map the design space along two dimensions: the pipeline stage at which concept structure is introduced (training objective, core architecture, inference, or post-hoc interpretat