Mapping Before Generating: IMAP for Inductive Reasoning in Large Language Models

Large language models excel at deductive reasoning and context learning, but their inductive reasoning capabilities remain limited by the fact that the models tend to fit instance-level answers rather than abstract reusable general patterns from a finite set of examples. This shortcoming severely hampers the generation of scientific hypotheses, cross-domain generalization, and efficient learning from samples. We propose IMAP, a reinforcement learning-based intelligent mapping framework that integrates the inductive reasoning paradigm into model inference. We designed a paradigm mapping structure comprising four core elements: chains of thought (CoT), cases, patterns, and rationality. We also propose the RL Paradigm Model (RLP) for paradigm generation. By using inductive reasoning as an input cue, we guided multiple large models to generate an average of 270 results. Comparative experiments demonstrate that combining input cues with inductive reasoning performs well across most models and enhances the quality of generated results. By framing inductive reasoning as a learnable paradigm-mapping process, our work unlocks its generative potential and offers a new perspective for understanding and deconstructing the reasoning mechanisms of large language models.