ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning
  in LLMs
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.
6853f43241586a7b0193a0b1
Feng He
6853f43241586a7b0193a0b2
Zijun Chen
6853f43241586a7b0193a0b3
Xinnian Liang
admin_assigned
2025-06-19T15:13:09.408Z
60adea21e3de7c7440abb863
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Xinnian Liang
user
EricLiang98
6853f43241586a7b0193a0b4
Tingting Ma
admin_assigned
2025-06-19T15:13:33.868Z
64f6be755f2dee8a6b9b5657
/avatars/8e06fe0155ae23432f31f77b406754c0.svg
Tingting Ma
user
violetma007
6853f43241586a7b0193a0b5
Yunqi Qiu
admin_assigned
2025-06-20T12:28:47.102Z
643400887b82474801088c80
/avatars/b4df10ffddc9c27c2ce530d9c758e588.svg
yunqi qiu
user
qiuyunqi
6853f43241586a7b0193a0b6
Shuangzhi Wu
admin_assigned
2025-06-19T15:13:28.504Z
637301f4bb66bd6b13206a25
/avatars/6925439441324f6fd00d167d471edff2.svg
Shuangzhi Wu
user
Shuangzhi
6853f43241586a7b0193a0b7
Junchi Yan
admin_assigned
2025-06-19T15:13:22.902Z
667289f903c802764985d8c6
/avatars/916befcbf0e52ce56be49617f31c7bb2.svg
Junchi Yan
user
Rethinker
ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning
  in LLMs
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.
ProtoReasoning enhances large reasoning models through prototypical representations, leading to improved cross-domain generalization in logical reasoning, planning, and other tasks.
Large Reasoning Models Long Chain-of-Thought cross-domain generalization abstract reasoning prototypes ProtoReasoning prototype representations Prolog PDDL automated prototype construction pipeline verification system Enigmata-Eval MMLU AIME24