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README.md

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----
-license: mit
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+---
+license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+---
+
+# AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents
+
+This repository contains the model weights for **AtlasVA**, as presented in the paper [AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents](https://huggingface.co/papers/2605.17933).
+
+**AtlasVA** is a teacher-free visual skill memory framework designed for Vision-Language Model (VLM) agents. It organizes memory into three complementary layers: spatial heatmaps, visual exemplars, and symbolic text skills. By evolving danger and affinity atlases directly from trajectory statistics, AtlasVA provides dense, coordinate-aware guidance for reinforcement learning, unifying perception, memory, and optimization without external LLM supervision.
+
+- **Project Page:** [https://wangpan-ustc.github.io/AtlasvaWeb/](https://wangpan-ustc.github.io/AtlasvaWeb/)
+- **Repository:** [https://github.com/wangpan-ustc/AtlasVA](https://github.com/wangpan-ustc/AtlasVA)
+- **Paper:** [https://huggingface.co/papers/2605.17933](https://huggingface.co/papers/2605.17933)
+
+## Model Details
+- **Base Model**: Qwen2.5-VL-3B-Instruct
+- **Task**: Multimodal agentic decision making (Sokoban, FrozenLake, 3D navigation, robotic manipulation).
+- **Memory Layers**: Spatial heatmaps, visual exemplars, and symbolic text skills.
+
+## Citation
+
+```bibtex
+@article{wang2026atlasva,
+  title={AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents},
+  author={Wang, Pan and Hu, Yihao and Liu, Xiujin and Yang, Jingchu and Wang, Hang and Wen, Zhihao},
+  journal={arXiv preprint arXiv:2605.17933},
+  year={2026}
+}
+```