Scatter-Based Innovation Propagation in Large Language Models for Multi-Stage Process Adaptation

TL;DR

This paper introduces a scatter-based model that helps large language models generalize and propagate localized innovations across multi-stage processes by leveraging structural redundancies, improving their ability to reuse ideas beyond initial contexts.

Contribution

The paper presents a novel four-step scatter-based model that guides LLMs in identifying, generalizing, and applying innovations across similar process stages, enhancing their adaptability.

Findings

Enables LLMs to extend innovations across stages

Improves generalization and reuse of ideas

Demonstrates effectiveness through verification results

Abstract

Large Language Models (LLMs) exhibit strong capabilities in reproducing and extending patterns observed during pretraining but often struggle to generalize novel ideas beyond their original context. This paper addresses the challenge of applying such localized innovations - introduced at a specific stage or component - to other parts of a multi-stage process. We propose a scatter-based innovation expansion model (innovation scatter model) that guides the LLM through a four-step process: (1) identifying the core innovation by comparing the user's input with its surrounding context, (2) generalizing the innovation by removing references to specific stages or components, (3) determining whether the generalized innovation applies to a broader scope beyond the original stage, and (4) systematically applying it to other structurally similar stages using the LLM. This model leverages structural redundancy across stages to improve the applicability of novel ideas. Verification results demonstrate that the innovation scatter model enables LLMs to extend innovations across structurally similar stages, thereby enhancing generalization and reuse.