From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918461001695232 |
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| author | Zhou, Chenxi Cao, Pengfei Li, Jiang Yu, Bohan Ye, Jinyu Zhao, Jun Liu, Kang |
| author_facet | Zhou, Chenxi Cao, Pengfei Li, Jiang Yu, Bohan Ye, Jinyu Zhao, Jun Liu, Kang |
| contents | Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded as an optimal trade-off, reducing the precision to 2-bit usually triggers a catastrophic ``performance cliff.'' It remains unclear whether the underlying mechanisms differ fundamentally. Consequently, we conduct a systematic mechanistic analysis, revealing two qualitatively distinct failure modes: Signal Degradation, where the computational patterns remain intact but information precision is impaired by cumulative error; and Computation Collapse, where key components fail to function, preventing correct information processing and destroying the signal in the early layers. Guided by this diagnosis, we conduct mechanism-aware interventions, demonstrating that targeted, training-free repair can mitigate Signal Degradation, but remains ineffective for Computation Collapse. Our findings provide a systematic diagnostic framework for PTQ failures and suggest that addressing Computation Collapse requires structural reconstruction rather than mere compensation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19884 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization Zhou, Chenxi Cao, Pengfei Li, Jiang Yu, Bohan Ye, Jinyu Zhao, Jun Liu, Kang Computation and Language Artificial Intelligence Machine Learning Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded as an optimal trade-off, reducing the precision to 2-bit usually triggers a catastrophic ``performance cliff.'' It remains unclear whether the underlying mechanisms differ fundamentally. Consequently, we conduct a systematic mechanistic analysis, revealing two qualitatively distinct failure modes: Signal Degradation, where the computational patterns remain intact but information precision is impaired by cumulative error; and Computation Collapse, where key components fail to function, preventing correct information processing and destroying the signal in the early layers. Guided by this diagnosis, we conduct mechanism-aware interventions, demonstrating that targeted, training-free repair can mitigate Signal Degradation, but remains ineffective for Computation Collapse. Our findings provide a systematic diagnostic framework for PTQ failures and suggest that addressing Computation Collapse requires structural reconstruction rather than mere compensation. |
| title | From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.19884 |