title: Learning to Reason via Mixture-of-Thought for Logical Reasoning
summary: Human beings naturally utilize multiple reasoning modalities to learn and
  solve logical problems, i.e., different representational formats such as
  natural language, code, and symbolic logic. In contrast, most existing
  LLM-based approaches operate with a single reasoning modality during training,
  typically natural language. Although some methods explored modality selection
  or augmentation at inference time, the training process remains modality-blind,
  limiting synergy among modalities. To fill in this gap, we propose
  Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three
  complementary modalities: natural language, code, and a newly introduced
  symbolic modality, truth-table, which systematically enumerates logical cases
  and partially mitigates key failure modes in natural language reasoning. MoT
  adopts a two-phase design: (1) self-evolving MoT training, which jointly learns
  from filtered, self-generated rationales across modalities; and (2) MoT
  inference, which fully leverages the synergy of three modalities to produce
  better predictions. Experiments on logical reasoning benchmarks including FOLIO
  and ProofWriter demonstrate that our MoT framework consistently and
  significantly outperforms strong LLM baselines with single-modality
  chain-of-thought approaches, achieving up to +11.7pp average accuracy gain.
  Further analyses show that our MoT framework benefits both training and
  inference stages; that it is particularly effective on harder logical reasoning
  problems; and that different modalities contribute complementary strengths,
  with truth-table reasoning helping to overcome key bottlenecks in natural
  language inference.
paper_authors._id: 682ec132fbbbc6e3a91b106e
paper_authors.name: Tong Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:15:27.903Z
paper_authors._id: 6623ea65b642e29cdf90a1b4
paper_authors.avatarUrl: /avatars/e32e90574c1162b2be87ed78604e3e4d.svg
paper_authors.fullname: TongZheng
paper_authors.type: user
paper_authors.user: TongZheng1999
paper_authors._id: 682ec132fbbbc6e3a91b106f
paper_authors.name: Lichang Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:20.088Z
paper_authors._id: 62323bb408bcea92917e42ee
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62323bb408bcea92917e42ee/2vHxkv-oSROtLteOnqa8P.jpeg
paper_authors.fullname: Lichang Chen
paper_authors.type: user
paper_authors.user: Lichang-Chen
paper_authors._id: 682ec132fbbbc6e3a91b1070
paper_authors.name: Simeng Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:19:40.053Z
paper_authors._id: 617ad22b941993035fbaf2b1
paper_authors.avatarUrl: /avatars/3a4b9b06b85dc9be824c0c70ecd4a37f.svg
paper_authors.fullname: Simeng Han
paper_authors.type: user
paper_authors.user: Simeng
paper_authors._id: 682ec132fbbbc6e3a91b1071
paper_authors.name: R. Thomas McCoy
paper_authors._id: 682ec132fbbbc6e3a91b1072
paper_authors.name: Heng Huang
paper_title: Learning to Reason via Mixture-of-Thought for Logical Reasoning
paper_summary: Human beings naturally utilize multiple reasoning modalities to learn and
  solve logical problems, i.e., different representational formats such as
  natural language, code, and symbolic logic. In contrast, most existing
  LLM-based approaches operate with a single reasoning modality during training,
  typically natural language. Although some methods explored modality selection
  or augmentation at inference time, the training process remains modality-blind,
  limiting synergy among modalities. To fill in this gap, we propose
  Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three
  complementary modalities: natural language, code, and a newly introduced
  symbolic modality, truth-table, which systematically enumerates logical cases
  and partially mitigates key failure modes in natural language reasoning. MoT
  adopts a two-phase design: (1) self-evolving MoT training, which jointly learns
  from filtered, self-generated rationales across modalities; and (2) MoT
  inference, which fully leverages the synergy of three modalities to produce
  better predictions. Experiments on logical reasoning benchmarks including FOLIO
  and ProofWriter demonstrate that our MoT framework consistently and
  significantly outperforms strong LLM baselines with single-modality
  chain-of-thought approaches, achieving up to +11.7pp average accuracy gain.
  Further analyses show that our MoT framework benefits both training and
  inference stages; that it is particularly effective on harder logical reasoning
  problems; and that different modalities contribute complementary strengths,
  with truth-table reasoning helping to overcome key bottlenecks in natural
  language inference.
paper_ai_summary: A Mixture-of-Thought framework enables LLMs to reason across natural language, code, and symbolic logic, improving accuracy on logical reasoning tasks compared to single-modality approaches.
paper_ai_keywords: LMM-based approaches reasoning modality natural language code symbolic logic modality-blind self-evolving MoT training MoT inference FOLIO ProofWriter truth-table logical reasoning