Update README.md (SGT paper info)

README.md

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+---
+license: apache-2.0
+pipeline_tag: any-to-any
+library_name: bagel-mot
+tags:
+  - sgt
+  - semantic-generative-tuning
+  - unified-multimodal
+  - image-segmentation
+  - visual-understanding
+  - visual-generation
+---
+
+# SGT: Semantic Generative Tuning for Unified Multimodal Models
+
+This repository hosts checkpoints fine-tuned with **Semantic Generative Tuning (SGT)** — a training
+paradigm that couples visual *understanding* and *generation* in Unified Multimodal Models (UMMs)
+by using **image segmentation as a generative proxy**.
+
+> Unified multimodal models typically optimize understanding and generation with *misaligned*
+> objectives (sparse text tokens vs. dense pixel targets), which isolates the two capabilities.
+> SGT introduces segmentation — a **high-level semantic task** — as a unified generative objective
+> that aligns the two branches, improves feature linear separability, and optimizes visual-textual
+> attention allocation.
+
+## 🧠 Method Overview
+
+SGT reformulates classical visual tasks as generative proxies and establishes a **hierarchical
+taxonomy** (low-/mid-/high-level). Extensive experiments show that **high-level semantic tasks
+(e.g. image segmentation) are the optimal proxy**, outperforming depth, edge, reconstruction and
+MAE/inpainting for synergizing understanding and generation.
+
+Key findings:
+
+1. **High-level > low-level**: segmentation gives larger gains in both understanding and generation
+   than depth / edge / pixel reconstruction.
+2. **Perception, not reasoning**: visual supervision mainly strengthens vision-centric perception
+   (spatial, hallucination, OCR), rather than abstract reasoning.
+3. **Architecture-agnostic**: the gains hold for both **BAGEL** and **OmniGen2**.
+
+## 📦 Released Artifacts
+
+| Repo | Type | Base Model | Content |
+|---|---|---|---|
+| [`Two-hot/SGT-BAGEL`](https://huggingface.co/Two-hot/SGT-BAGEL)   | model   | BAGEL-7B-MoT   | SGT fine-tuned BAGEL checkpoint |
+| [`Two-hot/SGT-Gen2`](https://huggingface.co/Two-hot/SGT-Gen2)     | model   | OmniGen2       | SGT fine-tuned OmniGen2 checkpoint (transformer/ only) |
+| [`Two-hot/SAM-SGT`](https://huggingface.co/datasets/Two-hot/SAM-SGT) | dataset | —            | Segmentation training data (tar-sharded) used by SGT |
+
+### Use the SAM-SGT dataset
+
+See [`Two-hot/SAM-SGT`](https://huggingface.co/datasets/Two-hot/SAM-SGT) for the data
+layout and the extraction instructions (files are stored as 5GB tar shards to fit HF limits).
+
+## 📊 Highlights
+
+- **+6.02%** average gain over BAGEL on the **CV-Bench** evaluation.
+- Consistent improvements in **spatial reasoning**, **hallucination resistance**, and **OCR**.
+- Generation: gains across **GenEval** dimensions (Position / Color / Counting / Single-Object / etc.).
+- Verified on two representative UMM architectures (**BAGEL**, **OmniGen2**).
+
+## 📝 License
+
+Apache-2.0. Base models remain under their original licenses:
+BAGEL (Apache-2.0, based on Qwen2.5-7B + SigLIP + FLUX VAE) and
+OmniGen2 (based on Qwen2.5-VL + diffusion transformer).
+
+## ✍️ Citation
+
+If you find this work useful, please cite our paper (anonymous ECCV 2026 submission, paper ID #3064):
+
+```bibtex
+@article{sgt2026,
+  title   = {Semantic Generative Tuning for Unified Multimodal Models},
+  author  = {Songsong Yu, Yuxin Chen, Ying Shan, and Yanwei Li},
+  journal = {arxiv},
+  year    = {2026}
+}
+```