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NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons

Paper · 2026 · Haonan Dong, Kehan Jiang, Haoran Ye et al.

Summary

Large Reasoning Models still make three kinds of mistakes. Within a step, they get calculations or derivations wrong. Across steps, they loop or stall. At the instance level, they over-think easy questions. Earlier fixes target only one of these, rely on opaque RL training, and offer little insight or control.

The authors use the DePass attribution method on the middle layer of the model to find small neuron groups, called Mixture of Neurons (MoN), that track each failure type. They then use Fourier analysis to describe how each group's activity changes when a failure occurs. NeuReasoner builds on this:

  • Lightweight MLPs watch the MoN signals during generation.
  • When a failure is detected, a special token is inserted.
  • SFT on a reconstructed dataset teaches the model to respond to each token with a specific diagnose-then-correct behavior.

The method was tested on six benchmarks, six backbones (8B–70B) and nine baselines. It improves accuracy by up to 27.0% and cuts token use by 19.6–63.3%. It also offers an interpretable, steerable alternative to purely RL-driven reasoning.

Key points

  • Failure breakdown (DeepSeek-R1-Distill-Qwen-7B on MATH): intra-step 43.5%, inter-step 26.1%, instance level 21.7%, other 8.7%.
  • Where the MoN experts sit: intra-step and instance-level experts are mostly in the FFN, while inter-step experts are in the attention heads.
  • Activity signatures: intra-step errors show sharp spikes (high-frequency). Inter-step loops show periodic oscillation (dominant frequency). Easy questions show a rapid activation drop (low-frequency).
  • Test-time scaling (DeepSeek-R1-Distill-Qwen-32B on GPQA-Diamond, self-consistency): NeuReasoner at k=8 matches the vanilla model at k=64. At k=64 it beats the vanilla model by 3.5–10.0%.
  • Ablation:
    • Removing the intra-step MLP causes the largest accuracy loss.
    • Removing the instance-level MLP sharply increases token use; with DeepSeek-R1-Distill-Qwen-32B on MATH500, tokens rise from 1318 to 2389.
  • Limitations: the monitoring adds some latency, and the pipeline is not yet fully end-to-end.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Haonan Dong, Kehan Jiang, Haoran Ye, Wenhao Zhu, Zhaolu Kang and Guojie Song. 2026. NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1033

@inproceedings{dong2026neureasoner,
  title = {{NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons}},
  author = {Haonan Dong, Kehan Jiang, Haoran Ye, Wenhao Zhu, Zhaolu Kang and Guojie Song},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1033},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1033}
}

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