Add research directions, comparative analysis, and alternative models

README.md

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 title: Denali AI
-short_description: Vision-Language Models for Structured Garment Attribute Extraction
+short_description: VLMs for Garment Attribute Extraction
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 # Denali AI — Vision-Language Models for Garment Classification
@@ -212,6 +212,49 @@ This multi-metric approach captures semantic similarity rather than requiring ex
 
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+## Research Directions & Future Work
+
+### Near-Term Improvements
+
+| Direction | Expected Impact | Effort |
+|-----------|:--------------:|:------:|
+| **GTPO on Qwen3-VL-2B v9** | +2-4pp weighted (currently SFT+GRPO only) | Low |
+| **QLoRA on Qwen3.5-35B GPTQ** | JSON parse 14% → 100%, weighted 50% → ~80%+ | Low |
+| **OCR pre-processing pipeline** | Fix brand/size for Qwen3.5 models (+30-60pp on those fields) | Medium |
+| **Higher LoRA rank (r=32/64)** | +1-3pp from increased adapter capacity | Low |
+| **Guided JSON decoding** | Force 100% JSON parse on zero-shot models without training | Low |
+
+### Architecture Exploration
+
+Models we haven't tested but are strong candidates:
+
+| Model | Parameters | Why Promising |
+|-------|:----------:|---------------|
+| **[Qwen3-VL-7B](https://huggingface.co/Qwen/Qwen3-VL-7B)** | 7B | Larger Qwen3-VL — our best architecture. Could push past 90% |
+| **[InternVL3-4B](https://huggingface.co/OpenGVLab/InternVL3-4B)** | 4B | Mid-range InternVL — may close gap to Qwen3-VL |
+| **[SmolVLM2-2.2B](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct)** | 2.2B | HuggingFace's efficient VLM — strong structured output |
+| **[PaliGemma2-3B](https://huggingface.co/google/paligemma2-3b-pt-448)** | 3B | Google VLM with excellent OCR — may solve brand/size |
+| **[Phi-4-multimodal](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)** | 5.6B | Microsoft's latest — strong structured output |
+| **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** | 2.8B | Strong small VLM with good OCR capabilities |
+| **[Moondream2](https://huggingface.co/vikhyatk/moondream2)** | 1.6B | Ultra-compact — fastest possible inference |
+
+### Long-Term Research
+
+1. **Ensemble routing:** Use a lightweight classifier to route each field to the best-performing model (e.g., Qwen3-VL for visual attributes, InternVL3 for brand/size)
+2. **Curriculum learning:** Progressive difficulty training — easy garments first, hard edge cases last
+3. **Synthetic data generation:** Use large VLMs (122B) to generate training labels for unlabeled garment images at scale
+4. **Multi-image input:** Leverage front + back + tag images simultaneously for higher accuracy
+5. **Active learning:** Identify samples where models disagree most and prioritize annotation of those
+
+### Key Open Questions
+
+- Why does Qwen3-VL dramatically outperform Qwen3.5-VL at the same scale? Is it the vision encoder, the cross-attention mechanism, or training data?
+- Can RL gains be amplified beyond +1.6pp? Current GRPO/GTPO hyperparameters may be suboptimal
+- Is there a parameter count sweet spot between 2B and 7B where accuracy saturates?
+- Would instruction-tuned base models (vs base models) yield better SFT starting points?
+
+---
+
 ## Datasets
 
 | Dataset | Samples | Purpose | Link |