## Paper 1: Addressing Activation Outliers in LLMs: A Systematic Review of Post-Training Quantization Techniques
**Authors:** Czakó, Kertész, Szénási
**Year:** 2025
**DOI:** [10.1109/access.2025.3568702](https://doi.org/10.1109/access.2025.3568702)
**Citations:** (New Publication)

### Research Question
How can Post-Training Quantization (PTQ) techniques effectively manage "activation outliers"—extreme values in neural network activations that degrade quantization performance—without requiring expensive retraining?

### Methodology
- **Design:** Systematic Literature Review.
- **Approach:** Taxonomy creation of existing PTQ methods specifically targeting activation outliers.
- **Scope:** Large Language Models (LLMs) where retraining (QAT) is computationally prohibitive.

### Key Findings
1. **Outlier Impact:** Activation outliers are identified as the primary bottleneck preventing low-bit quantization (e.g., INT4) in LLMs, as they skew the quantization range.
2. **Technique Categorization:** The paper categorizes solutions into weight-only quantization modifications, activation smoothing, and mixed-precision strategies.
3. **PTQ Dominance:** Confirms that for billion-scale parameter models, PTQ is the only viable path compared to Quantization-Aware Training (QAT).

### Implications
This paper serves as a critical map for researchers, establishing that future quantization work must treat "outlier suppression" as a first-class citizen rather than a side effect. It likely standardizes terminology for "outlier-aware" quantization.

### Limitations
- As a review, it does not propose a new algorithm but rather synthesizes existing ones.
- **[VERIFY]** Does it cover the specific hardware implications of these outlier-handling techniques (e.g., latency overhead)?

### Notable Citations
- **[Paper 9 (AWQ)]** - Likely cited as a primary example of activation-aware methodologies.

### Relevance to Your Research
**Score:** ⭐⭐⭐⭐⭐ (5/5)
**Why:** Essential for understanding the "Why" behind modern quantization failures. Provides the theoretical landscape for why methods like AWQ work.

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