ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning
  in LLMs
Authors: name: Feng He, name: Zijun Chen, name: Xinnian Liang, name: Tingting Ma, name: Yunqi Qiu, name: Shuangzhi Wu, name: Junchi Yan

Recent advances in Large Reasoning Models (LRMs) trained with Long
Chain-of-Thought (Long CoT) reasoning have demonstrated remarkable cross-domain
generalization capabilities. However, the underlying mechanisms supporting such
transfer remain poorly understood. We hypothesize that cross-domain
generalization arises from shared abstract reasoning prototypes -- fundamental
reasoning patterns that capture the essence of problems across domains. These
prototypes minimize the nuances of the representation, revealing that seemingly
diverse tasks are grounded in shared reasoning structures.Based on this
hypothesis, we propose ProtoReasoning, a framework that enhances the reasoning
ability of LLMs by leveraging scalable and verifiable prototypical
representations (Prolog for logical reasoning, PDDL for
planning).ProtoReasoning features: (1) an automated prototype construction
pipeline that transforms problems into corresponding prototype representations;
(2) a comprehensive verification system providing reliable feedback through
Prolog/PDDL interpreters; (3) the scalability to synthesize problems
arbitrarily within prototype space while ensuring correctness. Extensive
experiments show that ProtoReasoning achieves 4.7% improvement over baseline
models on logical reasoning (Enigmata-Eval), 6.3% improvement on planning
tasks, 4.0% improvement on general reasoning (MMLU) and 1.0% on mathematics
(AIME24). Significantly, our ablation studies confirm that learning in
prototype space also demonstrates enhanced generalization to structurally
similar problems compared to training solely on natural language
representations, validating our hypothesis that reasoning prototypes serve as
the foundation for generalizable reasoning in large language models.