Remove data-efficiency claims from model card

README.md

@@ -40,7 +40,6 @@ On top of this pair, a **System 1 & System 2 dual-process design** adds proactiv
 
 - **Codec-native & from scratch.** The entire visual stack is trained from scratch — no billion-scale image-text ViT initialization. The bio-inspired predictive-patch mechanism (I/P frames at `16×16`) cuts visual-token consumption by **over 75%** (**~1/8 or less** of dense frame sampling), letting the model train on videos **8× longer** under the same budget.
 - **Codec-native speedup.** Codec tokenization sets a superior accuracy–efficiency frontier — **up to 3.5× wall-clock inference speedup** over uniform frame sampling at matched accuracy, and the fastest of all compared models on most video benchmarks (single 8×B200 node).
-- **Data-efficient tokenizer.** Trained on only **~100M unlabeled images/videos**, Mage-ViT matches or beats frontier encoders trained on billions of image-text pairs (SigLIP2 @ 10B, MoonViT @ 2B) — e.g. **99.33% on CIFAR-10** and **85.69% on ImageNet** with 256 tokens, showing web-scale pretraining is *not* essential for a strong VLM front-end.
 - **Native-resolution scaling.** Variable-resolution pretraining lets Mage-ViT improve *monotonically* with the token budget (peaking **>96.1% Food-101 / >86.3% ImageNet** at 676 tokens) where fixed-resolution encoders saturate or degrade.
 - **Matched-LLM video gains.** With the 4B Qwen3 backbone held fixed and only the ViT swapped, Mage-VL improves over Qwen3-VL-4B on **every** reported video and temporal-grounding benchmark — largest on localization-heavy tasks (**+22.5 QVHighlight**, +17.1 ActivityNet, +11.0 VSI-Bench, +24.5 VideoEval-Pro).
 - **Strong for its size.** On par with Qwen3-VL-4B on static images, and clearly ahead on video understanding and spatial intelligence (**+11.0** VSI-Bench, **+53.1** CrossPoint, **+5.2** EmbSpatial, **+22.5** QVHighlight).
@@ -50,7 +49,7 @@ On top of this pair, a **System 1 & System 2 dual-process design** adds proactiv
 
 A **single checkpoint**, `microsoft/Mage-VL`, is one unified model that **simultaneously** provides image & video understanding **and** the proactive streaming gate — the same weights answer offline image/video questions and drive event-gated commentary. It covers every Mage-VL capability: image understanding, frame-sampled video, traditional H.264/HEVC codec video, neural DCVC-RT codec video, and event-gated streaming. The repository bundles the codec processor, the neural codec package, and the proactive gate weights — no separate understanding, NVC, or streaming checkpoint is required.
 
-We additionally release **`microsoft/Mage-ViT`** — the standalone visual encoder from the two-stage, from-scratch ViT pre-training (cluster-discrimination on ~100M unlabeled image/video frames). This is the **ViT-pre-trained checkpoint only**: it has **not** gone through the joint VLM training with the language model. Use it as a data-efficient, codec-native visual encoder or as a drop-in ViT for your own multimodal training.
+We additionally release **`microsoft/Mage-ViT`** — the standalone visual encoder from the two-stage, from-scratch ViT pre-training. This is the **ViT-pre-trained checkpoint only**: it has **not** gone through the joint VLM training with the language model. Use it as a data-efficient, codec-native visual encoder or as a drop-in ViT for your own multimodal training.
 
 | Model | Task | Backbone | Hugging Face |
 | :--- | :--- | :--- | :--- |
@@ -186,15 +185,14 @@ JoyAI's high TriggerAcc comes from predicting silence almost everywhere under So
 
 ## 🔬 Key Findings
 
-Beyond the model, the report distills **seven empirical findings** for efficient multimodal training:
+Beyond the model, the report distills **six empirical findings** for efficient multimodal training:
 
-1. **Web-scale pretraining is not essential.** A from-scratch backbone on ~100M unlabeled frames matches encoders trained on billions of image-text pairs.
-2. **Variable-resolution pretraining scales monotonically.** Quality keeps improving with the visual-token budget instead of saturating/degrading like fixed-resolution encoders.
-3. **Codec-native tokenization sets a better accuracy–efficiency frontier** — up to **3.5× wall-clock inference speedup** over uniform frame sampling.
-4. **Explicit VideoQA SFT is redundant.** Dense video *captions* + standard image SFT are sufficient for strong zero-shot VideoQA.
-5. **Motion–spatial synergy.** Dynamic video training substantially improves static 2D/3D spatial reasoning.
-6. **AI4AI data pipeline.** Agentic closed-loop feedback + prompt/code co-design systematically lift caption quality and downstream scores (inspired SkillOpt-Lite).
-7. **Zero-Vision SFT for multimodal RL.** Bypassing visual SFT in favor of pure-text reasoning SFT unlocks stronger multimodal RL — a compute-efficient path.
+1. **Variable-resolution pretraining scales monotonically.** Quality keeps improving with the visual-token budget instead of saturating/degrading like fixed-resolution encoders.
+2. **Codec-native tokenization sets a better accuracy–efficiency frontier** �� up to **3.5× wall-clock inference speedup** over uniform frame sampling.
+3. **Explicit VideoQA SFT is redundant.** Dense video *captions* + standard image SFT are sufficient for strong zero-shot VideoQA.
+4. **Motion–spatial synergy.** Dynamic video training substantially improves static 2D/3D spatial reasoning.
+5. **AI4AI data pipeline.** Agentic closed-loop feedback + prompt/code co-design systematically lift caption quality and downstream scores (inspired SkillOpt-Lite).
+6. **Zero-Vision SFT for multimodal RL.** Bypassing visual SFT in favor of pure-text reasoning SFT unlocks stronger multimodal RL — a compute-efficient path.
 
 ## 🚀 Quick Start