Excited to introduce our recent work #SurgVLM, a universal vision-language foundation model for surgical intelligence, aiming to support cognitive assistance, facilitate surgical education, task automation. Welcome to check!😀 🔗arxiv.org/pdf/2506.02555 🔗jinlab-imvr.github.io/SurgVL…

Introducing OneContext. I built it for myself but now I can’t work without it, so it felt wrong not to share. OneContext is an Agent Self-Managed Context Layer across different sessions, devices, and coding agents (Codex / Claude Code). How it works: 1. Open Claude Code/Codex inside OneContext as usual, it automatically manages your context and history into a persistent context layer. 2. Start a new agent under the same context, it remembers everything about your project. 3. Share the context via link, anyone can continue building on the exact same shared context. Install with: npm i -g onecontext-ai And open with: onecontext Give it a try!

Welcome to submit your papers to #IPCAI2026, alongside the CARS conference, endorsed by @MICCAI_Society 😄

Thanks @omarsar0 for the great summarization of our work "KNOWLEDGE or REASONING ? A Close Look at How LLMs Think Across Domains"! How can we build more reliable LLMs? 🤔 We focus on ensuring not just accurate final answers, but also high-quality reasoning 💡 & knowledge utilization 🧠 at every single step! Here’s what we found & advocate for: ➡️ Evaluate step-wise knowledge (Knowledge Index) & reasoning (InfoGain), not just the final output. ➡️ SFT boosts knowledge (vital for domains like medicine!) but can make reasoning verbose, sometimes reducing its quality. ➡️ RL enhances reasoning quality & can prune incorrect knowledge from reasoning paths, improving overall knowledge correctness. Paper: arxiv.org/abs/2506.02126 Project: ucsc-vlaa.github.io/Reasonin… Code: github.com/UCSC-VLAA/Reasoni…

We are excited to share our latest work: "ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking", early accepted at MICCAI 2025 (top 9%)🚀 arXiv: arxiv.org/abs/2505.08581 Huggingface: huggingface.co/papers/2505.0… #SurgicalAI #SugicalDataScience #SAM2

Excited to share our work: "MedAgent-Pro: Towards Evidence-Based Multi-Modal Medical Diagnosis via Reasoning Agentic Workflow": Paper: arxiv.org/abs/2503.18968 Code: github.com/jinlab-imvr/MedAg… Huge thanks to @JinYueming , @JundeMorsenWu , and @lowchanghan99 for their effort!🎉🎉

I am thrilled to share our latest work: “SurgRAW: Multi-Agent Chain-of-Thought Reasoning for Surgical Intelligence” Arxiv: arxiv.org/abs/2503.10265 Special thanks to Prof @JinYueming and @AI_Ziyue and all collaborators who made this work possible.

🔥Introducing Open-Janus: a fully open-source and reproducible training pipeline for Janus. Janus—@DeepSeek_AI’s powerful vision-language model—unifies multimodal understanding and generation in one framework. We’re strong believers that this is a promising path toward truly generalized vision-language intelligence. But while the model weights were released, the training code wasn’t.🫠. So we rebuilt it from scratch. And now it’s open for everyone!🥳 Check it out 👉 github.com/jinlab-imvr/Open-… We've already achieved image understanding performance very close to DeepSeek’s Janus, and rapidly closing the gap on the others. Contributions welcome! Grateful for the work by Fang Zheng @meaw041524005 and the guidance of @JinYueming, which made it possible. #MultimodalAI #VisionLanguage #OpenSource #LLM #Janus #DeepLearning

📢Happy to share our MedSAM-2, a novel way for segmenting medical images as videos based on SAM-2. STRONGEST model for promptable medical image segmentation for both 2D and 3D images ( 500 GH stars) joint work with collaborators from @UniofOxford and NUS a thread 🧵 1/n

Excited to share our work: "Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning" at the AIM-FM workshop, NeurIPS 2024. We look forward to continuing our exploration and contributions to the research community. #NeurIPS2024 #SurgicalAI