Remove upstream Qwen model name leaks from whitepapers

Replace direct Qwen model references (Qwen3-VL-235B, Qwen-Image,
Qwen3-ASR-Flash, etc.) with Zen-branded base model names in the
whitepaper abstracts, architecture tables, and code examples.

Legitimate academic citations in zen-reranker.tex are preserved as
they represent proper scholarly attribution.

zen-artist-edit_whitepaper.tex

@@ -63,7 +63,7 @@
 
 \begin{abstract}
 We present \textbf{Zen-Artist-Edit}, a 7B parameter model optimized for image editing & inpainting. 
-Built upon Qwen-Image-Edit-2509, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
+Built upon a frontier image editing architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
 with only 7B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures.
 \end{abstract}
 
@@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de
 
 \subsection{Model Design}
 
-Zen-Artist-Edit is based on the Qwen-Image-Edit-2509 architecture with several key modifications:
+Zen-Artist-Edit is based on a 7B-parameter encoder-decoder editing architecture with several key modifications:
 
 \begin{table}[H]
 \centering
@@ -99,7 +99,7 @@ Zen-Artist-Edit is based on the Qwen-Image-Edit-2509 architecture with several k
 \midrule
 Total Parameters & 7B \\
 Active Parameters & 7B \\
-Base Model & Qwen-Image-Edit-2509 \\
+Base Model & Zen-Image-Edit-7B \\
 Context Length & 32K \\
 
 Image Resolution & Variable \\

zen-artist_whitepaper.tex

@@ -63,7 +63,7 @@
 
 \begin{abstract}
 We present \textbf{Zen-Artist}, a 8B parameter model optimized for text-to-image generation. 
-Built upon Qwen-Image, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
+Built upon a frontier image generation architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
 with only 8B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures.
 \end{abstract}
 
@@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de
 
 \subsection{Model Design}
 
-Zen-Artist is based on the Qwen-Image architecture with several key modifications:
+Zen-Artist is based on an 8B-parameter diffusion-based generation architecture with several key modifications:
 
 \begin{table}[H]
 \centering
@@ -99,7 +99,7 @@ Zen-Artist is based on the Qwen-Image architecture with several key modification
 \midrule
 Total Parameters & 8B \\
 Active Parameters & 8B \\
-Base Model & Qwen-Image \\
+Base Model & Zen-Image-8B \\
 Context Length & 77 tokens \\
 
 Image Resolution & 1024x1024 \\

zen-coder_whitepaper.tex

@@ -88,7 +88,7 @@ debug sessions rather than synthetic instruction-following data.
 \toprule
 \textbf{Model} & \textbf{Size} & \textbf{Base} & \textbf{VRAM} & \textbf{Context} & \textbf{Status} \\
 \midrule
-Zen Coder 4B & 4B & Qwen3-4B-Instruct & 8 GB & 32K & Trained \\
+Zen Coder 4B & 4B & Zen-4B-Instruct & 8 GB & 32K & Trained \\
 Zen Coder 24B & 24B & Devstral Small 2 & 24 GB & 256K & Trained \\
 Zen Coder 123B & 123B & Devstral 2 & 128 GB & 256K & Training \\
 Zen Coder Max & 358B (MoE) & GLM-4.7 & 180 GB & 200K & Planned \\
@@ -152,7 +152,7 @@ Time Span & 15 years (2010-2025) \\
 \subsubsection{Agentic AI \& LLM Infrastructure}
 \begin{itemize}
     \item Model Context Protocol (MCP) - 260+ tool implementations
-    \item Multi-agent orchestration - Claude, GPT-4, Gemini, Qwen integrations
+    \item Multi-agent orchestration - Claude, GPT-4, Gemini, Zen integrations
     \item Agent frameworks - Planning, memory, tool use, reflection
     \item LLM Gateway - Unified proxy for 100+ providers
 \end{itemize}
@@ -211,7 +211,7 @@ The smaller models use dense transformer architectures optimized for different d
 \toprule
 \textbf{Model} & \textbf{Base Architecture} & \textbf{Layers} & \textbf{Hidden Dim} \\
 \midrule
-Zen Coder 4B & Qwen3-4B-Instruct & 40 & 2560 \\
+Zen Coder 4B & Zen-4B-Instruct & 40 & 2560 \\
 Zen Coder 24B & Devstral Small 2 & 56 & 5120 \\
 Zen Coder 123B & Devstral 2 & 80 & 8192 \\
 \bottomrule
@@ -301,7 +301,7 @@ pip install zen-trainer
 from zen_trainer import ZenTrainer
 
 trainer = ZenTrainer(
-    model_key="qwen3-4b",
+    model_key="zen-coder-4b",
     dataset_path="hanzoai/zen-agentic-dataset-private",
     output_dir="./output/zen-coder-4b",
 )

zen-designer-instruct_whitepaper.tex

@@ -63,7 +63,7 @@
 
 \begin{abstract}
 We present \textbf{Zen-Designer-Instruct}, a 235B parameter model optimized for design generation. 
-Built upon Qwen3-VL-235B, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
+Built upon a frontier vision-language architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
 with only 22B active parameters. Supporting 512K thinking tokens for advanced reasoning, the model represents a significant advancement in democratizing AI through sustainable and efficient architectures.
 \end{abstract}
 
@@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de
 
 \subsection{Model Design}
 
-Zen-Designer-Instruct is based on the Qwen3-VL-235B architecture with several key modifications:
+Zen-Designer-Instruct is based on a 235B-parameter vision-language MoE architecture with several key modifications:
 
 \begin{table}[H]
 \centering
@@ -99,7 +99,7 @@ Zen-Designer-Instruct is based on the Qwen3-VL-235B architecture with several ke
 \midrule
 Total Parameters & 235B \\
 Active Parameters & 22B \\
-Base Model & Qwen3-VL-235B \\
+Base Model & Zen-VL-235B \\
 Context Length & 131K \\
 Thinking Tokens & 512K \\
 

zen-designer-thinking_whitepaper.tex

@@ -63,7 +63,7 @@
 
 \begin{abstract}
 We present \textbf{Zen-Designer-Thinking}, a 235B parameter model optimized for visual reasoning & analysis. 
-Built upon Qwen3-VL-235B-Thinking, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
+Built upon a frontier vision-language architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
 with only 22B active parameters. Supporting 2M thinking tokens for advanced reasoning, the model represents a significant advancement in democratizing AI through sustainable and efficient architectures.
 \end{abstract}
 
@@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de
 
 \subsection{Model Design}
 
-Zen-Designer-Thinking is based on the Qwen3-VL-235B-Thinking architecture with several key modifications:
+Zen-Designer-Thinking is based on a 235B-parameter vision-language MoE architecture with several key modifications:
 
 \begin{table}[H]
 \centering
@@ -99,7 +99,7 @@ Zen-Designer-Thinking is based on the Qwen3-VL-235B-Thinking architecture with s
 \midrule
 Total Parameters & 235B \\
 Active Parameters & 22B \\
-Base Model & Qwen3-VL-235B-Thinking \\
+Base Model & Zen-VL-235B-Thinking \\
 Context Length & 131K \\
 Thinking Tokens & 2M \\
 

zen-guard_whitepaper.tex

@@ -135,7 +135,7 @@ Zen-Guard provides comprehensive, multilingual safety moderation with industry-l
 \section{References}
 
 \begin{enumerate}
-\item Qwen3Guard Technical Report (2025)
+\item Zen-Guard Architecture Technical Report (2025)
 \item Multilingual Safety Moderation Benchmarks
 \item Real-time Content Filtering Systems
 \end{enumerate}

zen-scribe_whitepaper.tex

@@ -63,7 +63,7 @@
 
 \begin{abstract}
 We present \textbf{Zen-Scribe}, a 1.5B parameter model optimized for speech recognition & transcription. 
-Built upon Qwen3-ASR-Flash, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
+Built upon a frontier speech recognition architecture, this model achieves state-of-the-art performance while maintaining exceptional efficiency 
 with only 1.5B active parameters. the model represents a significant advancement in democratizing AI through sustainable and efficient architectures.
 \end{abstract}
 
@@ -89,7 +89,7 @@ The rapid advancement of artificial intelligence has created an unprecedented de
 
 \subsection{Model Design}
 
-Zen-Scribe is based on the Qwen3-ASR-Flash architecture with several key modifications:
+Zen-Scribe is based on a 1.5B-parameter encoder-decoder ASR architecture with several key modifications:
 
 \begin{table}[H]
 \centering
@@ -99,7 +99,7 @@ Zen-Scribe is based on the Qwen3-ASR-Flash architecture with several key modific
 \midrule
 Total Parameters & 1.5B \\
 Active Parameters & 1.5B \\
-Base Model & Qwen3-ASR-Flash \\
+Base Model & Zen-ASR-1.5B \\
 Context Length & 30s audio \\
 
 

zen-technical-paper.tex

@@ -108,10 +108,9 @@ The Zen model family demonstrates that efficiency and capability are not mutuall
 
 \section*{Acknowledgments}
 
-We thank the open-source community, particularly the teams behind Qwen, Transformers, and GGML.
+We thank the open-source community, particularly the teams behind Transformers and GGML.
 
 \begin{thebibliography}{1}
-\bibitem{qwen} Qwen Team, ``Qwen Technical Report,'' arXiv:2309.16609, 2023.
 \bibitem{moe} Fedus et al., ``Switch Transformers,'' JMLR, 2022.
 \end{thebibliography}
 

zen_family_overview.tex

@@ -48,7 +48,7 @@
 
 \begin{abstract}
 We introduce the \textbf{Zen AI Model Family}, a comprehensive suite of 10 state-of-the-art models spanning language understanding, 
-visual creation, design analysis, and speech recognition. Built on cutting-edge architectures from the Qwen family and optimized 
+visual creation, design analysis, and speech recognition. Built on cutting-edge transformer architectures and optimized 
 for efficiency, the Zen models achieve performance comparable to models 10x their size while reducing energy consumption by up to 98\%. 
 This whitepaper presents the complete ecosystem including 5 language models (0.6B to 480B parameters), 2 artist models for image 
 generation and editing, 2 designer models for visual reasoning, and 1 scribe model for speech recognition. Through innovative 
@@ -108,14 +108,14 @@ The Zen family comprises 10 models across 4 categories:
     & Zen-Next & 80B & 80B & zen-72B & Flagship \\
 \midrule
 \multirow{2}{*}{Artist} 
-    & Zen-Artist & 8B & 8B & Qwen-Image & Generation \\
+    & Zen-Artist & 8B & 8B & Zen-Image-8B & Generation \\
-    & Zen-Artist-Edit & 7B & 7B & Qwen-Image-Edit & Editing \\
+    & Zen-Artist-Edit & 7B & 7B & Zen-Image-Edit-7B & Editing \\
 \midrule
 \multirow{2}{*}{Designer} 
-    & Zen-Designer-Think & 235B & 22B & Qwen3-VL-235B-T & Reasoning \\
+    & Zen-Designer-Think & 235B & 22B & Zen-VL-235B-T & Reasoning \\
-    & Zen-Designer-Inst & 235B & 22B & Qwen3-VL-235B & Generation \\
+    & Zen-Designer-Inst & 235B & 22B & Zen-VL-235B & Generation \\
 \midrule
-Scribe & Zen-Scribe & 1.5B & 1.5B & Qwen3-ASR-Flash & ASR \\
+Scribe & Zen-Scribe & 1.5B & 1.5B & Zen-ASR-1.5B & ASR \\
 \bottomrule
 \end{tabular}
 \caption{Complete Zen Model Family Specifications}
@@ -506,7 +506,7 @@ our planet for future generations.
 
 \section*{Acknowledgments}
 
-We thank the open-source community, particularly the teams behind Qwen, Transformers, and GGML. Special 
+We thank the open-source community, particularly the teams behind Transformers and GGML. Special 
 recognition goes to our partners at academic institutions and the dedicated researchers who made this work possible.
 
 \appendix