[upstream-sync] README.md from ai-boost/awesome-ai-for-science@a45b1cc3 [catalogue]

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

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 - [OpenScience (Synthetic Sciences)](https://github.com/synthetic-sciences/openscience) - Open-source AI workbench for scientific research that automates the full research loop — literature review, hypothesis generation, code writing, experiment execution, database querying, and report writing — with 290+ skills, specialized research agents, and a browser-based workspace (1453+ stars, Apache 2.0, 2026)
 - [Claude Scholar](https://github.com/Galaxy-Dawn/claude-scholar) - Semi-automated research assistant for academic research and software development, supporting Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication (Galaxy-Dawn, 4.5K+ stars, MIT License, 2026)
 - [K-Dense BYOK](https://github.com/K-Dense-AI/k-dense-byok) - Free, open-source desktop AI research assistant that runs locally and turns natural-language requests into real data analysis, literature search, figure generation, and manuscript review; ships with 149 scientific skills, 326 workflow templates, and 229 databases across genomics, proteomics, drug discovery, and materials science, plus a living lab notebook, 60+ scientific file previews, and LaTeX editing (K-Dense-AI, 908+ stars, MIT License, 2026)
+- [Academic Research Skills (ARS)](https://github.com/Imbad0202/academic-research-skills) - Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)
 
 ### Literature Management Plugins
 - [llm-for-zotero](https://github.com/yilewang/llm-for-zotero) - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
@@ -290,6 +291,7 @@ validated: false
 - [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, elevating LLMs from equation proposers to autonomous scientists that write code, analyze data, implement equations, and optimize based on experimental feedback; outperforms baselines by 6-35% across four science disciplines with robustness to noise and out-of-domain generalization (GAIR-NLP / SJTU, 49+ stars, Apache 2.0)
 - [ARA (Agent-Native Research Artifact)](https://github.com/ARA-Labs/Agent-Native-Research-Artifact) - Research ecosystem for rigorous and trustworthy AI scientists — a protocol and skill bundle that makes autonomous research verifiable, crystallized, and observable through structured, machine-executable research artifacts and five agent skills for research management, compilation, verification, visualization, and publication (ARA-Labs, 447+ stars, MIT License, 2026)
 - [Scholar Loop](https://github.com/renee-jia/scholar-loop) - Autonomous multi-agent AI scientist that mirrors a PhD workflow: literature review → grounded hypothesis → real ML experiments → self-critique → write-up; features a deterministic harness with frozen-metric scoring, edit allowlists, and a verified registry to make reward-hacking and hallucination impossible, plus 108 unit tests runnable without API keys or GPUs (461+ stars, MIT License, 2026)
+- [ResearchStudio (Microsoft)](https://github.com/microsoft/ResearchStudio) - AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)
 
 ### Evaluation & Benchmarking
 - [ScienceAgentBench (ICLR 2025)](https://github.com/OSU-NLP-Group/ScienceAgentBench) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
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 - [micro-sam](https://github.com/computational-cell-analytics/micro-sam) - Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)
 - [MedSAM](https://github.com/bowang-lab/MedSAM) - Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)
 - [MedSAM2](https://github.com/bowang-lab/MedSAM2) - Segment Anything in 3D medical images and videos, extending SAM2 to volumetric and temporal medical imaging with state-of-the-art zero-shot segmentation performance across CT, MRI, and surgical video (arXiv 2025)
+- [Medical SAM3 (AIM Research Lab, arXiv 2026)](https://github.com/AIM-Research-Lab/Medical-SAM3) - Foundation model for universal prompt-driven medical image segmentation extending SAM3 to clinical imaging, supporting 2D public benchmarks and 3D training/evaluation with text and box prompts; pretrained weights available on HuggingFace (189+ stars)
 - [MedSegX](https://github.com/MedSegX/MedSegX-code) - Generalist foundation model and database for open-world medical image segmentation, enabling universal segmentation of diverse anatomical structures and pathologies with zero-shot generalization to unseen tasks and modalities (Nature Biomedical Engineering 2025)
 - [VoxTell (MIC-DKFZ, 2025)](https://github.com/MIC-DKFZ/VoxTell) - Free-text promptable universal 3D medical image segmentation foundation model enabling zero-shot segmentation of diverse anatomical structures and pathologies via natural language prompts across CT, MRI, and other volumetric imaging modalities (DKFZ, 195+ stars, Apache 2.0)
 - [BiomedParse](https://github.com/microsoft/BiomedParse) - Foundation model for joint segmentation, detection, and recognition of biomedical objects across nine imaging modalities, with v2 introducing BoltzFormer architecture for end-to-end 3D inference (Microsoft, Nature Methods 2025)
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 - [Chronos (Amazon Science, NeurIPS 2024)](https://github.com/amazon-science/chronos-forecasting) - Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)
 - [TabPFN (Prior Labs, Nature 2025)](https://github.com/PriorLabs/tabpfn) - Foundation model for tabular data that predicts on unseen real-world tables in a single forward pass, achieving accurate small-data classification and regression without task-specific training; widely applicable to scientific datasets with limited samples (7.4K+ stars, 2022-2026)
 - [DeepInnovator (HKUDS, arXiv 2026)](https://github.com/HKUDS/DeepInnovator) - Scientific foundation model and AI research copilot for idea generation, cross-disciplinary connection discovery, and hypothesis formation; trained with a decoupled reward-comment RL architecture and achieves GPT-4o-competitive novelty/rationale on STEM and social-science idea-generation benchmarks (270+ stars, MIT License, 2026)
+- [LOGOS (arXiv 2026)](https://github.com/LOGOS-Hub/LOGOS) - First multi-domain generative foundation model for the natural sciences built on a unified scientific grammar, encoding proteins, antibodies, small molecules, chemical reactions, materials, and their spatial interactions into a shared token vocabulary; enables unified generation, prediction, and design across domains under a purely autoregressive paradigm (134+ stars, Apache 2.0, 2026)
 - [MinervaAI](https://github.com/google-research/minerva) - Mathematical reasoning
 - [PaLM-2](https://ai.google/discover/palm2) - Scientific reasoning capabilities