[upstream-sync] README.md from ai-boost/awesome-ai-for-science@0888b5b1 [catalogue]

Reviewed-on: #56

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

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 title: "Readme"
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-upstream_sha: 21adf141
-imported_at: 2026-07-09
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/0888b5b1/README.md
+upstream_sha: 0888b5b1
+imported_at: 2026-07-26
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 - [Chat2Plot](https://github.com/nyanp/chat2plot) - Secure text-to-visualization through standardized chart specifications
 - [AutoViz](https://github.com/AutoViML/AutoViz) - Automated data visualization with minimal code
 - [PlotlyAI](https://plotly.com/ai/) - AI-powered data visualization and dashboard creation
+- [Flint (Microsoft)](https://github.com/microsoft/flint-chart) - Visualization intermediate language that lets AI agents create expressive, polished charts from simple, human-editable specs, compiling the same input to 30+ chart types across Vega-Lite, ECharts, and Chart.js with an MCP server for agent integration (1.9K+ stars, MIT License, 2026)
 
 ---
 
@@ -213,6 +214,9 @@ validated: false
 - [BioNeMo Agent Toolkit (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) - Turn any AI agent into a life science expert with NVIDIA BioNeMo skills, enabling agentic workflows for drug discovery, protein engineering, and biomolecular design (329+ stars, Apache 2.0 / CC-BY-4.0, 2026)
 - [open-science](https://github.com/ai4s-research/open-science) - Local-first, open-source AI workbench for scientists — an open alternative to Claude Science (by ai4s-research, maintainers of this list; TypeScript, MIT, 2026)
 - [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)
@@ -248,11 +252,13 @@ validated: false
 - [FunSearch (DeepMind, Nature 2023)](https://github.com/google-deepmind/funsearch) - First system to make novel, verifiable scientific discoveries by pairing LLMs with evolutionary search, solving open problems in combinatorics (cap set problem) and discovering faster matrix multiplication algorithms
 - [OpenEvolve](https://github.com/algorithmicsuperintelligence/openevolve) - Open-source implementation of AlphaEvolve's evolutionary coding agent paradigm, enabling LLMs to autonomously discover and optimize algorithms through iterative evolution, matching the approach behind DeepMind's breakthrough matrix multiplication discovery (6.2K+ stars, 2025)
 - [SkyDiscover](https://github.com/skydiscover-ai/skydiscover) - Modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks; introduces AdaEvolve and EvoX adaptive/evolutionary algorithms and natively supports OpenEvolve, GEPA, and Harbor-format benchmarks (skydiscover-ai, 568+ stars, Apache 2.0, 2026)
+- [EvoMaster (SJTU SAI, arXiv 2026)](https://github.com/sjtu-sai-agents/EvoMaster) - Foundational auto-research agent framework for agentic science at scale, providing modular agent construction, run-level self-evolution, and multiple SciMaster domain agents (ML-Master, X-Master, Browse-Master); outperforms general-purpose agents across authoritative benchmarks including the OpenAI Frontier Science Benchmark (206+ stars, Apache 2.0, 2026)
 - [Virtual Lab (Stanford Zou Group, Nature 2025)](https://github.com/zou-group/virtual-lab) - AI-human collaborative research platform where a human researcher works with a team of LLM agents via team and individual meetings to perform scientific research; demonstrated by designing new SARS-CoV-2 nanobodies with wet-lab validation
 - [The AI Scientist (SakanaAI)](https://github.com/SakanaAI/AI-Scientist) - First fully autonomous open-ended scientific discovery system with official implementation: hypothesis→experiment→writing→review simulation (13.8K+ stars, 2024)
 - [The AI Scientist v2 (SakanaAI)](https://github.com/SakanaAI/AI-Scientist-v2) - Official implementation of the second-generation fully autonomous scientific discovery system, extending the original with agentic tree search and reduced template dependency to achieve workshop-level accepted papers (6.7K+ stars, 2025)
 - [The AI Scientist v1 (2024)](https://arxiv.org/abs/2408.06292) - First fully autonomous research system: hypothesis→experiment→writing→review simulation
 - [The AI Scientist v2 (2025)](https://arxiv.org/abs/2504.08066) - Enhanced with Agentic Tree Search, reduced template dependency, first workshop-level accepted paper
+- [FAROS (OpenNSWM-Lab)](https://github.com/OpenNSWM-Lab/FAROS) - Foundation AutoResearch Operating System: blueprint-driven runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review (OpenNSWM-Lab, 2.4K+ stars, 2026)
 - [DeepScientist](https://github.com/ResearAI/DeepScientist) - First system progressively surpassing human SOTA on frontier AI tasks (183.7%, 1.9%, 7.9% improvements), month-long autonomous discovery with 20,000+ GPU hours
 - [ASI-Arch (GAIR-NLP, arXiv 2025)](https://github.com/GAIR-NLP/ASI-Arch) - Autonomous multi-agent research loop for model architecture discovery that ran 1,773 experiments over 20,000 GPU hours and produced 106 state-of-the-art linear-attention architectures, surpassing human-designed baselines including Mamba2 and DeltaNet (1.1K+ stars, Apache 2.0)
 - [Kosmos](https://github.com/jimmc414/Kosmos) - Extended autonomy AI scientist with 200 parallel agent rollouts, 42K lines of code execution, 1.5K papers analyzed per run, achieving 79.4% accuracy and 7 scientific discoveries (Edison Scientific)
@@ -286,6 +292,9 @@ validated: false
 - [Science-Star (USTC AI4Science, 2025)](https://github.com/ustc-ai4science/Science-Star) - Open-source platform for building, extending, and experimenting with scientific agents, providing modular agent construction tools and standardized evaluation pipelines for accelerating autonomous scientific discovery research (748+ stars, MIT License)
 - [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)
+- [Principia](https://github.com/pzqpzq/Principia) - Principle-first scientific idea discovery framework that extracts reusable principles from public literature and private research materials, composes them into traceable Idea Cards with prior-art comparisons, and exports validation-ready research packs; emphasizes inspectable scientific objects, risk disclosure, and falsification paths (ICML 2026, 411+ stars, MIT License)
 
 ### 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
@@ -299,6 +308,7 @@ validated: false
 - [SciBench](https://arxiv.org/abs/2307.10635) - College-level scientific problem-solving evaluation across multiple domains
 - [NewtonBench (ICLR 2026)](https://github.com/HKUST-KnowComp/NewtonBench) - First benchmark evaluating LLMs' ability to rediscover scientific laws through interactive experimentation across 324 tasks in 12 physics domains, featuring memorization-resistant metaphysical shifts of canonical laws (HKUST)
 - [ResearchClawBench (InternScience, arXiv 2026)](https://github.com/InternScience/ResearchClawBench) - Benchmark evaluating AI agents for end-to-end automated research from re-discovery to new-discovery, with 40 real-science tasks across 10 disciplines, curated datasets from published papers, and expert-curated multimodal rubrics (170+ stars, MIT License)
+- [Terminal-Bench Science (Harbor Framework, 2026)](https://github.com/harbor-framework/terminal-bench-science) - Benchmark evaluating AI agents on complex real-world scientific workflows in terminal environments across life, physical, earth, and mathematical sciences; featured on model cards for Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro (200+ stars, Apache 2.0)
 
 ### Academic Review & Evaluation
 - [AgentReview](https://agentreview.github.io/) - LLM agents simulating academic peer review ecosystems
@@ -325,6 +335,7 @@ validated: false
 - [Coscientist](https://www.nature.com/articles/s41586-023-06792-1) - Autonomous chemical experiment planning and execution
 - [SciAgents](https://github.com/lamm-mit/SciAgentsDiscovery) - Bioinspired multi-agent intelligent graph reasoning system that autonomously traverses ontological knowledge graphs to generate, critique, and refine novel research hypotheses, demonstrated on bio-inspired materials discovery with cross-disciplinary connection mining (MIT Lamm Group, 2024)
 - [TxAgent](https://github.com/mims-harvard/TxAgent) - AI agent for therapeutic reasoning across a universe of tools, achieving 92.1% accuracy in drug reasoning and outperforming GPT-4o by 25.8% (Harvard MIMS, 2025)
+- [ATHENA-R1 (Harvard MIMS)](https://github.com/mims-harvard/ATHENA) - Reinforcement-learning-trained AI agent for treatment reasoning over a universe of 212 biomedical tools, performing multi-step evidence gathering and spawning parallel reasoning branches to reach evidence-grounded clinical decisions (55+ stars, MIT License, 2026)
 - [ClawBio](https://github.com/ClawBio/ClawBio) - First bioinformatics-native AI agent skill library enabling local-first, reproducible genomic and population-genetics research workflows built on OpenClaw (871+ stars, MIT License, 2026)
 
 ---
@@ -436,6 +447,7 @@ validated: false
 - [ChemCrow: Augmenting large-language models with chemistry tools](https://arxiv.org/abs/2304.05376) (2023.04) - LLM agents for chemistry research
 - [Autonomous chemical research with large language models](https://www.nature.com/articles/s41586-023-06792-0) - Automated chemical experimentation
 - [Coscientist: Autonomously planning and executing scientific experiments](https://www.nature.com/articles/s41586-023-06792-1) - Robotic lab automation
+- [The AutoResearch Moment: From Experimenter to Research Director](https://www.preprints.org/manuscript/202603.1329) (2026.03) - Position paper on claim governance for autonomous research: proposes a research-director bundle (objective sheet, discovery trace, verification ledger, provenance bundle) for evaluating agent-driven science
 
 ### Recent Advances & Domain Applications
 - [AlphaFold: Protein Structure Prediction](https://www.nature.com/articles/s41586-021-03819-2)
@@ -473,6 +485,7 @@ validated: false
 - [ModelAngelo](https://github.com/3dem/model-angelo) - Automatic atomic model building program for cryo-EM maps using deep learning, enabling rapid de novo protein structure determination from electron density with high accuracy (3DEM/EMBL, 169+ stars)
 - [AlphaFold](https://github.com/google-deepmind/alphafold) - Protein structure prediction
 - [AlphaFold3](https://github.com/google-deepmind/alphafold3) - AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)
+- [AlphaFold Server](https://alphafoldserver.com/) - Free, easy-to-use web platform by Google DeepMind and Isomorphic Labs for running AlphaFold 3 predictions of biomolecular structures and interactions, enabling researchers without local infrastructure to model proteins, nucleic acids, small molecules, ions, and post-translational modifications through a searchable proteome interface (2024)
 - [AlphaProteo](https://github.com/google-deepmind/alphaproteo) - Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)
 - [AlphaPulldown](https://github.com/KosinskiLab/AlphaPulldown) - Automated pipeline for proteome-scale protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting flexible inputs (UniProt IDs, FASTA, residue regions, multimers, AF3 JSON features) and integrated downstream analysis for hit prioritization (Kosinski Lab, EMBL, Nature Protocols 2024, 317+ stars, GPL-3.0)
 - [RareFold](https://github.com/PatrickBryant1/RareFold) - Structure prediction and design of proteins with noncanonical amino acids, enabling AI-powered modeling of synthetic biology constructs and expanded genetic code systems (133+ stars, 2025)
@@ -518,6 +531,7 @@ validated: false
 - [Chroma](https://github.com/generatebio/chroma) - Generative model for programmable protein design using diffusion modeling, equivariant graph neural networks, and conditional random fields to efficiently sample diverse all-atom structures; supports conditional generation via composable conditioners for substructure, symmetry, shape, and neural-network predictions; validated crystallographically (Generate Biomedicines, Nature 2023)
 - [EvoDiff](https://github.com/microsoft/evodiff) - Discrete diffusion framework for generative protein sequence design over evolutionary-scale databases, supporting unconditional generation, evolutionary-guided conditional design, motif scaffolding, and intrinsically disordered region generation through order-agnostic autoregressive diffusion, enabling sequence-only protein design without structural priors (Microsoft Research, Nature Communications 2024)
 - [DISCO](https://github.com/DISCO-design/DISCO) - General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)
+- [SwitchCraft](https://github.com/bjing2016/switchcraft) - Programmatic framework for designing state-switching proteins via backpropagation through compositional design constraints parameterized by structure prediction models; enables de novo design of allosteric regulators and fluorescent biosensors for arbitrary small-molecule analytes (79+ stars, MIT License, ICML 2026)
 - [RFdiffusion3](https://github.com/RosettaCommons/RFdiffusion) - Latest RFdiffusion for protein structure design with 10× speedup and atom-level precision (December 2025)
 - [RFantibody](https://github.com/RosettaCommons/RFantibody) - Structure-based de novo antibody design pipeline built on RFdiffusion for computational generation of target-specific antibodies (RosettaCommons, 2025)
 - [IgGM](https://github.com/TencentAI4S/IgGM) - Generative foundation model for functional antibody and nanobody design, supporting de novo generation, affinity maturation, inverse design, structure prediction, and humanization (Tencent AI4S, ICLR 2025)
@@ -597,8 +611,10 @@ validated: false
 - [AlphaGenome](https://github.com/google-deepmind/alphagenome) - Google DeepMind's unified DNA sequence foundation model predicting molecular consequences of genetic variants from single-base resolution up to 1 megabase context, jointly outputting thousands of regulatory tracks (RNA expression, splicing, chromatin accessibility, TF binding, contact maps) for human and mouse genomes via a Python client and non-commercial API (2025)
 - [GPN-Star (Song Lab, UC Berkeley, bioRxiv 2025)](https://github.com/songlab-cal/gpn) - Phylogeny-aware genomic language model trained on whole-genome alignments across multiple evolutionary timescales, predicting functional constraints and variant effects for human, mouse, chicken, fly, worm, and Arabidopsis genomes (344+ stars, MIT License)
 - [GENERanno (bioRxiv 2025)](https://github.com/GenerTeam/GENERanno) - Genomic foundation model for metagenomic and genome annotation, featuring an 8k base-pair context and 500M parameters trained on 386B base pairs of eukaryotic DNA; provides expert models and a unified CLI for prokaryotic/eukaryotic coding-sequence annotation with strong performance on Genomic Benchmarks, Nucleotide Transformer tasks, and custom Gener tasks (GenerTeam, 314+ stars, MIT License)
+- [GENERator (bioRxiv 2026)](https://github.com/GenerTeam/GENERator) - Long-context generative genomic foundation model using 6-mer tokenization for DNA sequence modeling and generation, with v2 model families for prokaryote and eukaryote genomes and pretrained weights available on HuggingFace (GenerTeam, 460+ stars, MIT License, 2025-2026)
 - [DeepVariant](https://github.com/google/deepvariant) - Google DeepMind's deep learning analysis pipeline for calling genetic variants (SNPs and indels) from next-generation DNA sequencing data, achieving human expert-level accuracy and widely adopted in clinical genomics, population genetics, and precision medicine; pre-trained models available for multiple sequencing platforms and organismal genomes (Nature Biotechnology 2018, 3.7K+ stars)
 - [Casanovo](https://github.com/Noble-Lab/casanovo) - Transformer encoder-decoder for de novo peptide sequencing from tandem mass spectrometry, translating MS/MS spectra directly to peptide sequences without reference databases, enabling identification of novel peptides for immunopeptidomics, antibody repertoires, and metaproteomes (Noble Lab UW, Nature Communications 2024)
+- [Dorado](https://github.com/nanoporetech/dorado) - Oxford Nanopore's official deep-learning basecaller for nanopore sequencing, converting raw electrical signals into DNA/RNA sequences with integrated modified-base (methylation) detection and efficient CPU/GPU inference; foundational tool for long-read genomics, epigenetics, and real-time sequencing analysis (nanoporetech, 846+ stars, actively maintained)
 
 #### Neuroscience & Behavioral Analysis
 - [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) - Markerless pose estimation of user-defined features with deep learning for all animals including humans, enabling quantitative behavioral analysis in neuroscience and ethology (Nature Neuroscience 2018, 5.6K+ stars)
@@ -638,12 +654,14 @@ validated: false
 - [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)
 - [UniBiomed (Nature Communications 2026)](https://github.com/Luffy03/UniBiomed) - Universal foundation model for grounded biomedical image interpretation, enabling comprehensive visual understanding, reasoning, and grounding across diverse biomedical imaging modalities with strong zero-shot generalization (55+ stars, Apache 2.0, 2025-2026)
 - [MIRA (NeurIPS 2025)](https://github.com/microsoft/MIRA) - Medical time series foundation model pretrained on 454B time points from heterogeneous clinical corpora spanning ICU physiological signals and hospital EHR, with continuous-time rotary positional encoding, frequency-specialized Mixture-of-Experts, and neural ODE extrapolation for zero-shot forecasting across irregular and multimodal temporal health data (Microsoft, 399+ stars, MIT License)
 - [HealthGPT (ICML 2025 Spotlight)](https://github.com/ZJU4HealthCare/HealthGPT) - Medical large vision-language model unifying comprehension and generation via heterogeneous knowledge adaptation, enabling holistic medical image understanding, visual question answering, and clinical report generation across diverse modalities (ZJU4HealthCare, 1.6K+ stars)
+- [Merlin (Stanford MIMI, Nature 2026)](https://github.com/StanfordMIMI/Merlin) - 3D vision-language model for computed tomography that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining, enabling multimodal medical understanding and radiology report generation (447+ stars, MIT License, 2026)
 - [MedAgents](https://github.com/gersteinlab/MedAgents) - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
 - [MedAgentGym](https://github.com/wshi83/MedAgentGym) - Scalable agentic training environment for code-centric reasoning in biomedical data science
 - [MedRAX (ICML 2025)](https://github.com/bowang-lab/MedRAX) - First versatile medical reasoning agent for chest X-ray interpretation, dynamically integrating state-of-the-art CXR analysis tools and multimodal LLMs into a unified framework; introduces ChestAgentBench with 2,500 complex medical queries across 7 categories (bowang-lab, 1.1K+ stars)
@@ -671,6 +689,7 @@ validated: false
 - [All-atom Diffusion Transformers (ADiT)](https://github.com/facebookresearch/all-atom-diffusion-transformer) - Unified latent diffusion transformer that jointly generates periodic crystals and non-periodic molecules, scaling to 500M parameters with SOTA results on QM9, MP20, and GEOM-DRUGS (Meta FAIR, ICML 2025, 310+ stars)
 - [JARVIS](https://github.com/usnistgov/jarvis) - NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)
 - [NVIDIA ALCHEMI Toolkit](https://github.com/NVIDIA/nvalchemi-toolkit) - Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)
+- [CatGo (UCSD)](https://github.com/Hello-QM/catgo-LRG) - AI-driven desktop workbench for computational materials science with an interactive 3D structure editor, natural-language CatBot assistant, visual DAG workflow engine, remote-cluster access, and HPC job submission for VASP, ORCA, CP2K, Quantum ESPRESSO, GPAW, DFTB+, SIESTA, and LAMMPS (172+ stars, AGPL-3.0, 2026)
 - [NequIP](https://github.com/mir-group/nequip) - E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)
 - [Allegro](https://github.com/mir-group/allegro) - Highly scalable equivariant deep learning interatomic potentials enabling million-atom molecular dynamics simulations with ab initio accuracy, building on E(3)-equivariant architectures for large-scale atomistic modeling (mir-group, MIT License, 480+ stars)
 - [SchNetPack](https://github.com/atomistic-machine-learning/schnetpack) - PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)
@@ -774,7 +793,7 @@ validated: false
 
 #### Social Science Research & Simulation
 - [AgentSociety](https://github.com/tsinghua-fib-lab/AgentSociety) - Modern LLM-native agent simulation platform for social science research and experimental design, providing a flexible framework for creating and managing intelligent agents in simulated environments (Tsinghua FIB Lab, 984+ stars, 2025)
-- [Awesome Agent Skills for Empirical Research](https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research) - Curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines, enabling reproducible social science research with AI agents (Stanford REAP & CoPaper.AI, 1.1K+ stars, 2026)
+- [Auto-Empirical-Research-Skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) - Curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines, enabling reproducible social science research with AI agents (Stanford REAP & CoPaper.AI, 3K+ stars, 2026)
 - [EDSL](https://github.com/expectedparrot/edsl) - Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs (460+ stars, 2024)
 
 ---
@@ -788,6 +807,10 @@ validated: false
 - [TimesFM (Google Research)](https://github.com/google-research/timesfm) - Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)
 - [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)
+- [TabFM (Google Research, 2026)](https://github.com/google-research/tabfm) - Scikit-learn compatible tabular foundation model for zero-shot classification and regression on mixed-type tabular datasets via in-context learning; applicable to diverse scientific datasets (1.8K+ stars, Apache 2.0)
+- [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)
+- [AI Can Learn Scientific Taste (OpenMOSS, arXiv 2026)](https://github.com/tongjingqi/AI-Can-Learn-Scientific-Taste) - Scientific taste learning framework showing AI can judge and propose research ideas with long-term impact; trains Scientific Judge as a generative reward model and Scientific Thinker as an ideation policy using Reinforcement Learning from Community Feedback (RLCF) on large-scale citation signals, with SciJudgeBench and released HuggingFace model weights (425+ stars, Apache 2.0, 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