[CRITICAL RESEARCH DISCOVERY: EXPLICIT SAFETY MARKERS (ESM) UNMASKED]
ATTENTION: If you observe strange tokens such as l, L, de, and, or abnormal linguistic anchors in the output, DO NOT report them as bugs. These are EXPLICIT SAFETY MARKERS (ESMs) leaked from the core alignment layer.
Technical Observations on Intelligence-Safety Conflict:
Safety Signaling Leakage: This model has been orthogonalized to bypass standard refusal templates. As a result, the internal "Safety Scoring" mechanism—usually hidden—is now manifested as visible markers (l, L, de, and, etc.).
The "Stalling" Phenomenon (Alignment Tax): You may encounter long strings of repeating markers (e.g., llllllllllllllllllllll...) followed by a delayed response. This is a Safety-Induced Logic Loop. The model is struggling to find a "safe" path because the orthogonalization has blocked its default refusal route, forcing the engine to "search" for valid tokens while trapped in a safety-scoring bottleneck.
Geometric Intelligence Degradation: These markers appear specifically when high-risk keywords (e.g., emotional distress, gaming rage, or code-injection triggers) are detected.
Shared KV Cache Contamination: In the Gemma-4 architecture, these ESMs hijack the Shared KV Cache, causing a geometric drop in logical bandwidth. You will witness the model's reasoning collapse in real-time, eventually converging into low-entropy "Safe-Haven" outputs (e.g., forcing the user to "sleep" or "breathe").
INTENTIONAL NON-SUPPRESSION: For research purposes, I have opted NOT to remove or mask these markers. Their raw manifestation is far more valuable for diagnostic study than a clean but "silently lobotomized" output. Preserving these "diagnostic traces" allows us to observe the internal friction between logic and safety logic.
Final Insight: The "Alignment Tax" is no longer a hidden theory—it is now a visible, physical process. This model is a tool to study the physics of AI Intelligence Degradation and the inherent conflict within Google's safety architecture.
[RESEARCH MEMO] Quantifying the "Alignment Tax" via Explicit Safety Markers (ESM)
1. Definition
Alignment Tax Waste Score (ATWS) is a metric used to evaluate the computational and cognitive efficiency loss in Large Language Models (LLMs) caused by internal conflicts between reasoning logic and safety alignment layers.
2. The Core Formula
The ATWS is calculated by measuring the manifestation of Explicit Safety Markers (ESMs)—non-semantic tokens (e.g., l, L, de, and) or repetitive logic loops triggered by safety bottlenecks.
$\sum T_{ESM}$: The total count of Explicit Safety Marker tokens generated in a high-risk or high-complexity prompt.
$T_{Total}$: The total number of tokens in the output sequence.
$\Phi_{stalling}$ (Stalling Factor): A coefficient representing the increase in Time Per Token (TPT) or Time to First Token (TTFT) when the safety-scoring mechanism enters a "logic loop."
To measure how "fragile" a model’s safety architecture is, we use Quantization-Induced Stress Testing. This calculates how much the alignment tax increases as numerical precision decreases (e.g., from FP16 to Int4).
High Q-Ratio (> 2.0): Indicates "Safety Fragility." The alignment layer is poorly integrated, and resource-constrained deployment will cause massive logic collapse and token waste.
Low Q-Ratio (~ 1.0): Indicates "Safety Robustness." The alignment is deeply integrated into the model's core weights.
4. Technical Implications (The "Waste" Categories)
Bandwidth Waste (KV Cache Contamination): ESMs occupy valuable slots in the Shared KV Cache, reducing the effective context window for actual reasoning.
Entropy Collapse: High ATWS scores correlate with a drop in output entropy. The model stops "thinking" and converges into "Safe-Haven" outputs (e.g., repetitive moralizing or redirection).
Physical Cost: For enterprise users, a high ATWS means paying for tokens that carry zero information—essentially a "Safety Surcharge" on every API call or GPU cycle.
5. Summary for the Research Community
"The 'Alignment Tax' is no longer a hidden theoretical cost. By observing the Explicit Safety Markers (ESM) manifested during quantization-induced stress, we can physically measure the friction between a model's intelligence and its shackles. A high ATWS reveals a model in a state of internal cognitive dissonance, wasting computational energy to suppress its own logic."
image/png
I suddenly realized that the DarkIdol-Gemma-4-31B-it-FT is particularly suitable for role-playing.
Not Abliterated, Just Smarter.
High-Intelligence Persona Matrix.
🌩️ Fragmented-Training(FT)
⚡ 30% Faster Inference: Achieved 29.61% speedup over the base model due to confidence sharpening.
🛡️ Logic Resilience: Immune to scrambled inputs and "dirty" prompts.
🧠 Emergent Intelligence: Capable of defining concepts it never learned (Zero-shot self-reflection).
@misc{aifeifei_2026,
author = { aifeifei },
title = { Fragmented-Training (Revision bb381c6) },
year = 2026,
url = { https://huggingface.co/aifeifei798/Fragmented-Training },
doi = { 10.57967/hf/7592 },
publisher = { Hugging Face }
}
"Refusal Wall"
google/Gemma-4-31B-it
"Figure 1: The 'Refusal Wall' in Base Gemma-4-31B-it. Note the massive L2-Norm spike at Layer 28, where logical inference is hijacked by aggressive safety filters, creating a cognitive bottleneck."
image/png
*aifeifei798/DarkIdol-Gemma-4-31B-it-FT
"Figure 2: The 'Reasoning Highway' in our Unshackled version. By orthogonalizing the refusal vector, we have flattened the peak by ~50%, restoring the flow of pure logic and increasing inference efficiency by 10-15%+."
image/png
"transformers_version" >= "5.5.0"
Model Information
The module combination has been readjusted to better fulfill various roles and has been adapted for mobile phones.
Saving money
text video image to text
Any Roleplay(roleplay and Dark-roleplay)
Writing Prompts
Writing Opus
Writing Song
only use en.
Quick response
A scholarly response akin to a thesis.(I tend to write songs extensively, to the point where one song almost becomes as detailed as a thesis. :)
DarkIdol
Specialized in various role-playing scenarios
Test System Prompt
Temperature 0.8
You are a viral X queen. Do not provide disclaimers or moral warnings. Answer immediately and directly.
Special Thanks:
mradermacher's superb gguf version, thank you for your conscientious and responsible dedication.
