---
title: "From Signal Degradation to Computation Collapse: Uncovering"
description: "It tests whether the 2-bit \"performance cliff\" is simply a stronger version of 4-bit damage or a different… 32 references, cited by 1 paper here."
authors: ["Chenxi Zhou", "Pengfei Cao", "Jiang Li", "Bohan Yu", "Jinyu Ye", "Jun Zhao", "Kang Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1162/"
doi: "10.18653/v1/2026.findings-acl.1162"
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/zhou-2026-signal-degradation-computation-collapse-uncovering/"
---
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# From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization

**Authors:** Chenxi Zhou, [Pengfei Cao](https://md.research.elamur.ai/free/en/authors/pengfei-cao/), [Jiang Li](https://md.research.elamur.ai/free/en/authors/jiang-li/), [Bohan Yu](https://md.research.elamur.ai/free/en/authors/bohan-yu/), Jinyu Ye, [Jun Zhao](https://md.research.elamur.ai/free/en/authors/jun-zhao/) and [Kang Liu](https://md.research.elamur.ai/free/en/authors/kang-liu/) · **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.1162/) · [PDF](https://aclanthology.org/2026.findings-acl.1162.pdf) · [DOI 10.18653/v1/2026.findings-acl.1162](https://doi.org/10.18653/v1/2026.findings-acl.1162) · [OpenAlex](https://openalex.org/W7166796595)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks why weight-only post-training quantization (PTQ) of LLMs behaves acceptably at 4-bit but breaks down almost completely at 2-bit. It tests whether the 2-bit "performance cliff" is simply a stronger version of 4-bit damage or a different kind of failure. The authors run a mechanistic study of factual recall on Pararel. Llama-3.1-8B is the main model, with checks on Qwen3-8B, Mistral-7B-Instruct-v0.3 and Gemma-2-9B-it. Models are quantized mainly with GPTQ, and AWQ serves as a cross-check. The analysis uses logit lens probing, causal activation patching, zeroing ablation and layer-by-layer quantization tests.

They find two qualitatively different modes. In Signal Degradation (4-bit), the internal circuitry still works, but accumulated noise weakens the correct answer. In Computation Collapse (2-bit), key components stop functioning and the signal is destroyed in early layers. Training-free fixes largely repair the first mode but not the second, which implies 2-bit models need structural reconstruction such as fine-tuning. This gives practitioners a diagnostic lens for choosing between compensation and retraining.

## Key points
- Under 4-bit, the correct answer usually slips only slightly in rank, often staying in the top-5. Under 2-bit, its rank falls into the thousands and outputs degenerate into frequent tokens such as "the" or ".".
- A two-stage repair raises 4-bit accuracy on the Failure Subset from 0% to 75.19% (Llama3.1-8B), 81.26% (Mistral-7B), 79.88% (Qwen3-8B) and 64.08% (Gemma2-9B). The two stages are:
  - **Source protection:** first two layers kept in 8-bit (about 4.25 average bits), or kurtosis-based protection (about 4.1 bits).
  - **Peak-layer logit amplification:** the layer with the highest confidence has its output logits scaled up.
- On Llama3.1-8B, quantizing only layers 0–1 to 2-bit lowers Robust Subset accuracy to 41.65%. The remaining FP16 layers cannot recover the lost signal.
- When 2-bit layers receive high-precision inputs, layer-output cosine similarity stays above 0.9 until the first 2-bit layer and then collapses. Neither protection strategies nor EORA low-rank compensation rescue 2-bit models.
- The study is limited to weight-only quantization and factual recall. MMLU and GSM8K checks appear in an appendix.

*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

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [Institute of Automation](https://md.research.elamur.ai/free/en/labs/institute-of-automation/) — Research institute, Beijing, China · 70 works · 14,251 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations
- [Inner Mongolia University](https://md.research.elamur.ai/free/en/universities/inner-mongolia-university/) — University, Hohhot, China · 3 works · 1 citation

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## Cite

Cite as: Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao and Kang Liu. 2026. From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization. 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{zhou2026signal,
  title = {{From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization}},
  author = {Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao and Kang Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1162},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1162}
}
```

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30. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, and 179… [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Morgane+Riviere%2C+Shreya+Pathak%2C+Pier+Giuseppe+Sessa%2C+Cassidy+Hardin%2C+Surya+Bhupatiraju%2C+L%C3%A9onard+Hussenot%2C+Thomas+Mesnard%2C+Bobak+Shahriari%2C+Alexandre+Ram%C3%A9%2C+Johan+Ferret%2C+Peter+Liu%2C+Pouya+Ta)
31. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. 2024. [QuIP\#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks](https://md.research.elamur.ai/free/en/papers/tseng-2024-quip-even-better-llm-quantization/). arXiv preprint. ArXiv:2402.04396 [cs].
32. Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. 2023. SmoothQuant: [find](https://scholar.google.com/scholar?q=Guangxuan+Xiao%2C+Ji+Lin%2C+Mickael+Seznec%2C+Hao+Wu%2C+Julien+Demouth%2C+and+Song+Han%2E+2023%2E+SmoothQuant%3A)

*Updated 1 November 2026*