---
title: "NEAT: Neuron-Based Early Exit for Large Reasoning Models"
description: "NEAT offers a training-free option that works at the level of individual neurons. 36 references, cited by 1 paper here."
authors: ["Kang Liu", "YongKang Liu", "Xiaocui Yang", "Peidong Wang", "Wen Zhang", "Shi Feng", "Yifei Zhang", "Daling Wang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1231/"
doi: "10.18653/v1/2026.findings-acl.1231"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-neat-neuron-based-early-exit/"
---
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# NEAT: Neuron-Based Early Exit for Large Reasoning Models

**Authors:** [Kang Liu](https://md.research.elamur.ai/free/en/authors/kang-liu/), [YongKang Liu](https://md.research.elamur.ai/free/en/authors/yongkang-liu/), [Xiaocui Yang](https://md.research.elamur.ai/free/en/authors/xiaocui-yang/), [Peidong Wang](https://md.research.elamur.ai/free/en/authors/peidong-wang/), [Wen Zhang](https://md.research.elamur.ai/free/en/authors/wen-zhang/), [Shi Feng](https://md.research.elamur.ai/free/en/authors/shi-feng/), [Yifei Zhang](https://md.research.elamur.ai/free/en/authors/yifei-zhang/) and [Daling Wang](https://md.research.elamur.ai/free/en/authors/daling-wang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1231/) · [PDF](https://aclanthology.org/2026.findings-acl.1231.pdf) · [DOI 10.18653/v1/2026.findings-acl.1231](https://doi.org/10.18653/v1/2026.findings-acl.1231) · [OpenAlex](https://openalex.org/W7166812650)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/liu-2026-neat-neuron-based-early-exit/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large Reasoning Models frequently keep producing reasoning after they have already reached a correct solution. This "overthinking" wastes compute and can introduce errors late in the output. Earlier early-exit techniques either sample intermediate answers repeatedly, which adds rollouts and latency, or depend on probes trained on labeled hidden-state data. NEAT offers a training-free option that works at the level of individual neurons.

On a small calibration set, it scores FFN neurons by how much each one raises the log probability of the termination token (\</think>). It then keeps the neurons whose activation is concentrated late in the reasoning trace, measured by high relative center of mass and low entropy. During decoding, NEAT compares current activations of these neurons with a reference pattern. A strong match ends reasoning, and a weaker match only suppresses reflection tokens such as "Wait."

The authors tested NEAT on four reasoning benchmarks and six models of varying size and architecture. It reduced generated tokens by 22–28% per model with no loss in accuracy, and without extra rollouts or supervision.

## Key points
- Neuron selection has two steps: log-probability-increase attribution toward \</think>, then temporal filtering by Center of Mass and Activation Entropy over a calibration set.
- NEAT uses two interventions, chosen by the strength of the signal: early exit when it is strong, and reflection suppression when it is weaker.
- Averaged over the four benchmarks, token reduction is 22.0%–28.2% per model. Tested models go up to 14B parameters.
- On MATH500 with DeepSeek-R1-Distill-Qwen-7B, NEAT kept Vanilla accuracy (92.2) while cutting average tokens from 3743 to 2914. Random neurons dropped accuracy to 77.8, and hidden-state probes reached 82.0, so neither worked as a reliable exit signal.
- The method requires access to internal activations, so it cannot be used with API-only models. Code: github.com/KpKqwq/Early_Exist_Circuit.

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

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- [Northeastern University](https://md.research.elamur.ai/free/en/universities/northeastern-university-cn/) — University, Shenyang, China · 42 works · 1,940 citations

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🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Kang Liu, YongKang Liu, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Yifei Zhang and Daling Wang. 2026. NEAT: Neuron-Based Early Exit for Large Reasoning Models. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026neat,
  title = {{NEAT: Neuron-Based Early Exit for Large Reasoning Models}},
  author = {Kang Liu, YongKang Liu, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Yifei Zhang and Daling Wang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1231},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1231}
}
```

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*Updated 31 October 2026*