ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs

• URL: http://arxiv.org/abs/2506.15211v1
• Date: Wed, 18 Jun 2025 07:44:09 GMT
• Title: ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs
• Authors: Feng He, Zijun Chen, Xinnian Liang, Tingting Ma, Yunqi Qiu, Shuangzhi Wu, Junchi Yan,
• Abstract summary: ProtoReasoning is a framework that enhances the reasoning ability of Large Reasoning Models.<n>ProtoReasoning transforms problems into corresponding prototype representations.<n>ProtoReasoning achieves 4.7% improvement over baseline models on logical reasoning.
• Score: 54.154593699263074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Solving Formal Math Problems by Decomposition and Iterative Reflection [30.54275542622631]
  textbfDelta Prover orchestrates the interaction between a general-purpose LLM and the Lean 4 proof environment.<n>bftextDelta Prover achieves a state-of-the-art 95.9% success rate on the miniF2F-test benchmark.
  arXiv  Detail & Related papers  (2025-07-21T03:56:35Z)
• CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
  Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-10T21:32:18Z)
• Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models [0.0]
  Large Language Models (LLMs) have demonstrated remarkable capabilities at solving complex reasoning tasks with Chain-of-Thought prompting.<n>We introduce textbfinverse reasoning, a novel paradigm enabling LLMs to decompose and explain their own reasoning chains post-hoc.<n>Our work creates new avenues for transparent AI systems and closes significant gaps in AI safety, education, and scientific discovery.
  arXiv  Detail & Related papers  (2025-06-30T09:53:41Z)
• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study [34.29839553042609]
  We propose FineLogic, a fine-grained evaluation framework that assesses logical reasoning across three dimensions.<n>We conduct a study on the effects of supervision format during fine-tuning.<n>Our findings reveal that natural language supervision yields strong generalization even on out-of-distribution and long-context tasks.
  arXiv  Detail & Related papers  (2025-06-05T09:34:12Z)
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models [54.04678363287392]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks.<n>Recent advancements in OpenAI o1 and DeepSeek-R1 have further improved performance in System-2 reasoning domains.
  arXiv  Detail & Related papers  (2025-03-20T17:59:38Z)
• A NotSo Simple Way to Beat Simple Bench [0.0]
  This paper presents a novel framework for enhancing reasoning capabilities in large language models (LLMs)<n>We propose a multi-step prompting strategy coupled with global consistency checks to improve model accuracy and robustness.<n>Our results reveal model-specific strengths: Claude excels in maintaining logical consistency, while GPT-4o exhibits exploratory creativity but struggles with ambiguous prompts.
  arXiv  Detail & Related papers  (2024-12-12T16:04:31Z)
• ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement [70.09541267910974]
  Post-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities.<n>Existing self-synthesizing methods suffer from poor generalization to out-of-domain (OOD) reasoning tasks.<n>We propose Reasoning Generalist via Self-Improvement (ReGenesis), a method to self-synthesize reasoning paths as post-training data.
  arXiv  Detail & Related papers  (2024-10-03T00:09:15Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models [63.14196038655506]
  We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) Our methodology reveals significant gaps in LLMs' learning of logical rules, with identified reasoning failures ranging from 29% to 90% across different models. We leverage these findings to construct targeted demonstration examples and fine-tune data, notably enhancing logical reasoning in models like GPT-4o by up to 5%.
  arXiv  Detail & Related papers  (2024-01-01T13:53:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.