# Narrative Consistency Report

**Sections Reviewed:**
- Chapter 1: Introduction
- Chapter 2: Main Body (2.1 Lit Review, 2.2 Methodology, 2.3 Analysis & Results, 2.4 Discussion)
- Chapter 3: Conclusion
- Chapter 4: Appendices

**Overall Coherence:** ⭐⭐⭐⭐½ (4.5/5)

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## Summary

**Strengths:**
- **Strong Structural Linking:** The draft excels at cross-referencing (e.g., Discussion 2.4 explicitly references the "lens established in 2.1" and "synthesis in 2.3").
- **Consistent Methodology:** The paper clearly defines itself as a "Narrative Review" and "Theoretical Synthesis" in Section 2.2, and the Results section (2.3) faithfully adheres to this by analyzing *literature* rather than presenting new experimental data.
- **Unified Vocabulary:** Key terms like "Activation Outliers," "Hardware-Native," and "Co-design" are used consistently across chapters.

**Issues Found:** 2 moderate, 3 minor

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## Issues Identified

### CRITICAL (Must Fix)
None found ✓

### MODERATE (Should Fix)

**Issue 1: Ambiguity of "Proposed Strategy" vs. Lack of Empirical Testing**
- **Location:** Section 2.1 (End) vs. Section 2.3 (End)
- **Problem:** Section 2.1 concludes by stating the thesis will "propose a unified strategy for hardware-native quantization." In engineering contexts, this often implies a new algorithm benchmarked against others. However, Section 2.2 clarifies this is a "narrative review." The reader might expect a performance table for this "unified strategy" in Section 2.3, but instead finds a list of principles.
- **Fix:** Slight adjustment in language to manage expectations. Change "propose a unified strategy" to "synthesize a theoretical framework for..." or "propose a conceptual model for..."

**Issue 2: Verification of Internal References**
- **Location:** Section 2.2 (Methodology)
- **Problem:** The text references **"Section 2.2.4"** ("Theoretical Synthesis phase"). However, based on the provided text, it is unclear if this subsection exists or was cut.
- **Fix:** Verify Section 2.2.4 exists in the full document. If the Methodology is short, ensure the "Theoretical Synthesis" header is present.

### MINOR (Nice to Fix)

**Issue 3: Repetitive "Problem Statement" Transition**
- **Location:** End of 1.1 → Start of 2.1.1
- **Problem:** Section 1.1 ends by contrasting edge hardware with data centers. Section 2.1.1 immediately restarts with "The exponential growth... has created a significant divergence..." essentially repeating the intro's hook.
- **Fix:** Modify the start of 2.1.1 to be less introductory and more technical immediately. E.g., "To address the resource constraints identified in Chapter 1, quantization alters..."

**Issue 4: Specificity Mismatch**
- **Location:** Section 2.1.7.4
- **Problem:** References the "solid-state circuit and systems community." This is very specific compared to the broader "Edge AI" audience addressed in the Introduction.
- **Fix:** Broaden to "hardware architecture and systems community" to match the Introduction's scope.

**Issue 5: Cut-off Text**
- **Location:** End of Chapter 1 (Introduction)
- **Problem:** The text ends mid-sentence: "...capabilities of the data c".
- **Fix:** Complete the sentence (likely "data centers").

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## Transition Quality

### Introduction → Literature Review
**Quality:** ⚠️ Repetitive
**Note:** The start of Lit Review retreads the "LLMs are big/expensive" ground covered in Intro.

### Literature Review → Methodology
**Quality:** ✅ Smooth
**Note:** Good shift from "what is known" to "how we will analyze it."

### Methodology → Results
**Quality:** ✅ Excellent
**Note:** The definition of the Hessian metric in Methodology pays off immediately in Results 2.3.1.1.

### Results → Discussion
**Quality:** ✅ Excellent
**Note:** Discussion explicitly synthesizes findings from 2.3 using theory from 2.1. High coherence.

### Discussion → Conclusion
**Quality:** ✅ Smooth
**Note:** Natural progression from implications to final summary.

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## Narrative Arc Check

**Act 1 (Introduction):** LLMs are too big for Edge AI; we need a solution. ✓
**Act 2 (Literature):** Quantization is the math solution, but standard methods fail on outliers. ✓
**Act 3 (Methods):** We will perform a theoretical synthesis (not a coding experiment) using Hessian metrics to evaluate strategies. ✓
**Act 4 (Results):** Literature shows that "Hardware-Native" quantization (combining 4 specific techniques) is the answer. ✓
**Act 5 (Discussion):** This shifts the paradigm from weight-focus to activation-focus. ✓
**Conclusion:** Integer-only inference is viable if we handle outliers; this democratizes AI. ✓

**Overall:** Coherent story ✅

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## Recommended Fixes (Priority Order)

1.  **[HIGH]** Fix **Issue 5** (Complete the cut-off sentence in Intro).
2.  **[MEDIUM]** Resolve **Issue 1** (Clarify that the "proposed strategy" is a theoretical synthesis, not a benchmarked algorithm, to avoid disappointing readers looking for code/charts).
3.  **[MEDIUM]** Verify **Issue 2** (Ensure Section 2.2.4 exists).
4.  **[LOW]** Smooth **Issue 3** (Remove repetitive hook in 2.1.1).

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## Before/After Examples

**Issue 1 Fix (Managing Expectations):**

❌ **Before (Section 2.1 End):**
"...to propose a unified strategy for hardware-native quantization."

✅ **After:**
"...to propose a **theoretical framework** for hardware-native quantization **derived from best-in-class methodologies.**"

**Issue 3 Fix (Transition Repetition):**

❌ **Before (Section 2.1.1 Start):**
"The exponential growth in the parameter count of Large Language Models (LLMs)... has created a significant divergence..."

✅ **After:**
"**Given the computational divergence established in Chapter 1**, quantization has emerged as the critical technique to bridge this gap..."