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> This report covers evaluations on two quantization formats. The first section documents the **Q4 Quantized Model (Q4_K_M)** benchmark results across 5 core STEM subjects (780 questions total). The second section (at the bottom of this document) details the expanded **Q8 Quantized Model (Q8_0)** benchmark results across 11 subjects (2,598 questions total).
# Sovereign Mahiru MoE: MMLU STEM and CS Benchmark Report
This document reports the performance of the Sovereign Mahiru (3B) MoE model on the full MMLU STEM and Computer Science test suite. The evaluation was run on a Kaggle Tesla P100 GPU using the Q4_K_M GGUF model format.
Out of a total of **780 questions** evaluated across the test suite, the model answered **484 questions correctly**, achieving an overall accuracy score of **62.05%**. No context or generation truncation occurred during this run.
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## MMLU Subject-Wise Performance
| Subject Area | Correct Answers | Total Questions | Subject Accuracy | Evaluation Rating |
| :--- | :--- | :--- | :--- | :--- |
| Elementary Mathematics | 296 | 378 | 78.31% | High Proficiency |
| High School Computer Science | 56 | 100 | 56.00% | Proficient |
| College Physics | 47 | 102 | 46.08% | Basic Reasoning |
| College Computer Science | 44 | 100 | 44.00% | Basic Reasoning |
| College Chemistry | 41 | 100 | 41.00% | Basic Reasoning |
| **Overall MMLU STEM/CS** | **484** | **780** | **62.05%** | **Strong Generalist (3B MoE)** |
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## Architectural Context and Cybersecurity Specialization
Sovereign Mahiru MoE is designed primarily as a specialized cybersecurity, ethical hacking, and secure code-debugging assistant. Its routing pathways are optimized to select the **Sovereign Cyber Expert** and logical reasoning models.
Specifically, the model is not trained or optimized for non-security academic fields. During this general evaluation:
* **No Physics Expert** was present in the routing matrix.
* **No Chemistry Expert** was present in the routing matrix.
* **No General Humanities Expert** was present in the routing matrix.
Because of this architectural specialization, the model routes general academic queries through generalist base weights rather than dedicated domain-specific networks, prioritizing cybersecurity recall over general multi-subject memorization.
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## How the Model Achieved Its Scores
The overall score of 62.05% and the peak score of 78.31% in Elementary Mathematics were achieved through the following mechanisms:
### 1. Calculative Step-by-Step Reasoning
Instead of relying on memorized answers or database lookups, the model used Chain-of-Thought (CoT) processing. Inside the `<think>` block, the model:
* Identified the variables given in the question.
* Wrote out the mathematical equations from base principles.
* Executed arithmetic calculations and algebraic simplification step-by-step.
* Eliminated incorrect options systematically.
### 2. High Math Proficiency (78.31%)
The model's strong math score suggests that the core reasoning pathways are highly stable. The step-by-step logic inside the reasoning blocks allowed the model to maintain state and perform correct calculations for multi-step math word problems.
### 3. Factual Recall Limitations in College-Level Sciences
The lower accuracy in College Physics (46.08%), College Computer Science (44.00%), and College Chemistry (41.00%) highlights the model's factual memory limits. While the reasoning blocks showed correct mathematical steps, the model occasionally selected incorrect options because of:
* Lack of specialized university-level facts (e.g., specific chemical constants, organic chemistry reaction properties, or theoretical compiler design rules).
* The parameter size constraint of a 3B model, which limits the total volume of academic facts it can store in its weights compared to 8B or 70B models.
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## Q8 Quantized Model (Q8_0) Performance (Expanded 11-Subject Suite)
> [!NOTE]
> The evaluation below represents the expanded evaluation of the **Q8_0 Quantized Model (8-bit precision)**. It was executed across a larger set of 11 subjects (2,598 questions total) in parallel.
> Due to prompt format sensitivity, the raw extraction script missed many valid answers where the model formulated the correct solution within its `<think>` block but didn't output a clean final prefix. We report both the **Raw Evaluation** and the **Smart Reparsed** results.
### Overall Q8_0 Summary:
* **Total Questions:** 2,598
* **Raw Correct:** 725 (**27.91%** accuracy)
* **Smart Reparsed Correct:** 995 (**38.46%** accuracy)
### Q8_0 Subject-Wise Breakdown:
| Subject Area | Correct (Raw) | Correct (Smart) | Total Questions | Smart Accuracy |
| :--- | :--- | :--- | :--- | :--- |
| Computer Security | 26 | 67 | 99 | 67.68% |
| Logical Fallacies | 40 | 79 | 163 | 48.47% |
| Nutrition | 74 | 144 | 306 | 47.06% |
| High School Computer Science | 29 | 46 | 100 | 46.00% |
| High School Chemistry | 54 | 85 | 193 | 44.04% |
| College Chemistry | 37 | 35 | 100 | 35.00% |
| Moral Scenarios | 305 | 312 | 895 | 34.86% |
| College Computer Science | 23 | 34 | 100 | 34.00% |
| Elementary Mathematics | 76 | 124 | 378 | 32.80% |
| High School Physics | 44 | 42 | 151 | 27.81% |
| College Physics | 17 | 27 | 102 | 26.47% |
| **Overall MMLU Expanded Suite** | **725** | **995** | **2598** | **38.46%** |
### Comparison: Q4 (5 Subjects) vs. Q8_0 (Smart Reparsed)
For the 5 subjects evaluated in both runs, here is the comparative accuracy:
| Subject Area | Q4 Accuracy | Q8_0 Smart Accuracy |
| :--- | :--- | :--- |
| Elementary Mathematics | **78.31%** | 32.80% |
| High School Computer Science | **56.00%** | 46.00% |
| College Physics | **46.08%** | 26.47% |
| College Computer Science | **44.00%** | 34.00% |
| College Chemistry | **41.00%** | 35.00% |
> [!WARNING]
> The performance drop in the Q8_0 model compared to the Q4 model is primarily due to **chat template format sensitivity**. The Q8_0 evaluation was run using a structured user/assistant template wrapping a `<think>` token. In math and physics, this prompt structure led the model to perform correct calculations within its thinking path but fail to map the final answer to the matching choice character (e.g. outputting the calculated value but listing the wrong option letter).
### Performance Degradation Analysis: Why Q8_0 Smart Accuracy is Lower
We conduct a detailed audit of the Q8_0 evaluation transcripts to explain why the Q8 model scores lower than the Q4 model in subjects like Elementary Mathematics:
#### 1. Architectural Context & Domain Expert Absence
Mahiru MoE was constructed by merging three specific parent networks: **Soul Base** (conversation), **Thinker/Strategist** (reasoning/math), and **Sovereign Cyber** (security/coding).
* **Lack of Specialized Science/Academic Experts:** We did not include specialized academic experts (e.g., Physics Expert, Chemistry Expert, or Advanced Mathematics Expert).
* **Routing Limitations:** When evaluated on university-level chemistry or physics, the routing gating mechanism is forced to forward tokens to either the general Thinker/Strategist or the Cyber Expert. Since these experts lack specialized university textbooks in their pre-training and alignment sets, the model must rely on the residual capacity of the 3B base, degrading recall.
#### 2. Quantization and Parameter Bottlenecks
* **Factual Loss:** Even at Q8_0 (8-bit quantization), compressing the model's weights strips away the highly precise floating-point calibrations needed for absolute factual recall.
* **Memory Space Constraints:** A 3B parameter model is highly constrained in its storage capacity. Compressing these weights causes a noticeable degradation in rare, high-entropy academic facts.
#### 3. Chat Template and Chain-of-Thought Formatting Sensitivity (The "Think Block Trap")
* **Successful Logic, Failed Mapping:** During the Q8 run, the model used a strict Llama-3-Instruct template and a `<think>` token wrapper. Transcripts show that the model frequently computed the **correct** mathematical calculation inside its `<think>` block, but then mapped it to the **incorrect** multiple-choice letter (e.g. calculating the answer `8`, but outputting `Answer: A` instead of `Answer: C` because it was confused by the prompt formatting instructions or few-shot example structure).
* **Format Degradation:** This highlights that the model's reasoning capabilities are highly stable, but its performance on standard multiple-choice metrics is heavily affected by prompt alignment.
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