arXiv Machine Learning
Aug 11
Which Decisions Low-Bit Quantization Breaks, and How to Predict Them
arXiv:2608. 06564v2 Announce Type: replace Abstract: Quantization is how large language models are actually deployed, and below four bits it hurts.
Quantization Damage Is Multiplicative, Not Additive
arXiv:2608. 06564v1 Announce Type: new Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt.
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arXiv Machine Learning
Sep 2
The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally
The paper investigates where post‑training quantization (PTQ) harms large language models (LLMs) and how to best allocate a limited precision budget. By causally raising each layer to 8‑bit precision across nine open‑weight models, the authors find that quantization damage is diffuse rather than concentrated in specific task circuits or weight statistics, and that globally refining quantization granularity outperforms selectively protecting the most recoverable layers. They also observe that the residual accuracy loss is budget‑limited and that peak recovery locations correlate with architecture within families but not across families.
arXiv Machine Learning
Jul 31
Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents
arXiv:2607. 27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless.
arXiv Machine Learning
Jul 21
Half the Experts, All the Code: One-Shot Domain Pruning of Mixture-of-Experts LLMs for Coding
arXiv:2607. 16721v1 Announce Type: new Abstract: The strongest open-weight coding models are mixture-of-experts (MoE) networks: most of their size comes from large pools of "expert" subnetworks, of which only a few act on any token.
arXiv AI
Jun 3
How Quantization Changes Interpretable Features: A Sparse Autoencoder Analysis of Language Models
arXiv:2606. 03002v1 Announce Type: cross Abstract: Quantization is a standard path to deploying large language models, and a quantized model is typically judged acceptable when its perplexity or downstream accuracy stays close to the full-precision original.
arXiv Machine Learning
Jul 15
Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate
arXiv:2607. 12266v1 Announce Type: new Abstract: Mixed-precision quantization must decide which parts of a model to keep at higher precision.
arXiv AI
Jun 8
Perplexity Can Miss SAE Feature Damage Under Quantization
arXiv:2606. 03002v2 Announce Type: replace-cross Abstract: Quantization is a standard path to deploying large language models, and quantized models are typically judged acceptable when perplexity or downstream accuracy remains close to the full-precision original.
arXiv AI
Aug 3
When Bits Break Recourse: Counterfactual-Faithful Quantization
arXiv:2605. 17160v2 Announce Type: replace-cross Abstract: Model quantization is widely used to reduce memory, latency, and deployment cost, and is typically judged by whether predictive accuracy is preserved.
arXiv Machine Learning
Sep 22
The Undetected Damage of Quantization on Retrieval and How to Fix It
arXiv:2609.24322v1 Announce Type: new Abstract: We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1 retrieval results, and that aggregate r...
arXiv AI
Jun 30
The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis
arXiv:2606. 29581v1 Announce Type: cross Abstract: Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually treat these choices as fixed implementation details.
arXiv Machine Learning
Sep 17
A Calibrated Instrument for Measuring How Inference Optimizations Affect Output Quality
The paper introduces a calibrated instrument for rigorously measuring how inference optimizations—such as quantization, early‑exit, and speculative decoding—affect the output quality of large language models. It uses a formally calibrated LLM judge that verifies no systematic bias between statistically equivalent outputs and includes a null condition to ensure measured differences are zero. Applying this method, the authors find that a 4‑bit model is indistinguishable from its 16‑bit counterpart, while 3‑bit quantization and early‑exit techniques incur measurable quality losses that vary by language and task, and that token‑certainty‑based acceptance rules cannot reliably identify impactful errors.
Hugging Face Trending Papers
Sep 16
A Calibrated Instrument for Measuring How Inference Optimizations Affect Output Quality
Large language model optimization is an active research area, spanning quantization of model weights, early-exit methods for skipping layers, and speculative decoding. Each track uses its own quality...