TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training
Paper • 2603.01714 • Published
Zhejiang University
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Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video
InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
yangjinluan
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yangjinluan
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Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
Paper • 2601.22642 • Published • 9
Can Tool-Integrated Reinforcement Learning Generalize Across Diverse Domains?
Paper • 2510.11184 • Published • 1
LRAS: Advanced Legal Reasoning with Agentic Search
Paper • 2601.07296 • Published • 3
LongCat-Flash-Thinking-2601 Technical Report
Paper • 2601.16725 • Published • 178
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yangjinluan
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🔥 We're thrilled to share some exciting news about ICEdit! Currently, ICEdit app ( RiverZ/ICEdit) has soared to the second place on the weekly trend list of Hugging Face Space, just trailing behind Qwen3. What's more, it also holds the second position on the overall space trend list. This achievement wouldn't have been possible without your incredible support and love. A huge thank you to each and every one of you❤!
🎉 The ICEdit community has been incredibly active, and we've seen a plethora of amazing ComfyUI workflows being shared. For instance, with the help of ComfyUI - nunchaku, you can run ICEdit locally with just 4GB of VRAM. This makes it much more accessible for those with limited hardware resources.
🎇 If you're interested in the detailed information, please head over to our repository. We highly encourage you to give these workflows a try and explore the creative possibilities that ICEdit offers.
Github Repo: https://github.com/River-Zhang/ICEdit
Hugging Face Space: RiverZ/ICEdit
🎉 The ICEdit community has been incredibly active, and we've seen a plethora of amazing ComfyUI workflows being shared. For instance, with the help of ComfyUI - nunchaku, you can run ICEdit locally with just 4GB of VRAM. This makes it much more accessible for those with limited hardware resources.
🎇 If you're interested in the detailed information, please head over to our repository. We highly encourage you to give these workflows a try and explore the creative possibilities that ICEdit offers.
Github Repo: https://github.com/River-Zhang/ICEdit
Hugging Face Space: RiverZ/ICEdit
yangjinluan
authored 2
papers 11 months ago
Post
3732
🚀 Excited to Share Our Latest Work: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer～
🎨 Daily Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
🔓 Code is now open source!
🔥 Huggingface DEMO:
RiverZ/ICEdit
🌐 Project Website: https://river-zhang.github.io/ICEdit-gh-pages/
🏠 GitHub Repository: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
🤗 Huggingface:
sanaka87/ICEdit-MoE-LoRA
📄 arxiv Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
🔥 Why it’s cool:
- Achieves high-quality, multi-task image editing.
- Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods — extremely efficient
- Beats several commercial models on background preservation, ID control, and consistency
- Open-source, low-cost, faster, and stronger — think of it as the “DeepSeek of image editing” 👀
We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video — happy to send it your way!
🎨 Daily Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
🔓 Code is now open source!
🔥 Huggingface DEMO:
RiverZ/ICEdit
🌐 Project Website: https://river-zhang.github.io/ICEdit-gh-pages/
🏠 GitHub Repository: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
🤗 Huggingface:
sanaka87/ICEdit-MoE-LoRA
📄 arxiv Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
🔥 Why it’s cool:
- Achieves high-quality, multi-task image editing.
- Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods — extremely efficient
- Beats several commercial models on background preservation, ID control, and consistency
- Open-source, low-cost, faster, and stronger — think of it as the “DeepSeek of image editing” 👀
We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video — happy to send it your way!
h12345678
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yangjinluan
authored a
paper about 1 year ago