[upstream-sync] README.md from ai-boost/awesome-ai-for-science@cf74319a [catalogue]

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

upstream/HKUST-KnowComp-Awesome-LLM-Scientific-Discovery/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome LLM Scientific Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
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-upstream_sha: b39b55ac
-imported_at: 2026-06-26
+upstream_source: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery/blob/7fcb8811/README.md
+upstream_sha: 7fcb8811
+imported_at: 2026-07-03
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -104,6 +104,7 @@ LLMs assisting in experimental protocol planning, workflow design, and scientifi
 *   **Natural Language to Code Generation in Interactive Data Science Notebooks** [![arXiv](https://img.shields.io/badge/arXiv-2212.09248-B31B1B.svg)](https://arxiv.org/pdf/2212.09248) - *Yin et al. (2022.12)*
 *   **DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation** [![arXiv](https://img.shields.io/badge/arXiv-2211.11501-B31B1B.svg)](https://arxiv.org/pdf/2211.11501) - *Lai et al. (2022.11)*
 *   **Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents**, [![arXiv](https://img.shields.io/badge/arXiv-2502.16069-B31B1B.svg)](https://arxiv.org/pdf/2502.16069) - *Kon et al. (2025.02)*
+*   **AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing** [![arXiv](https://img.shields.io/badge/arXiv-2602.17607-B31B1B.svg)](https://arxiv.org/pdf/2602.17607) - *Du et al. (2026.02)*
 
 ### Data Analysis and Organization
 
@@ -117,12 +118,14 @@ LLMs assisting in data-driven analysis, tabular/chart reasoning, statistical rea
 *   **Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding** [![arXiv](https://img.shields.io/badge/arXiv-2401.04398-B31B1B.svg)](https://arxiv.org/pdf/2401.04398) - *Wang et al. (2024.01)*
 *   **TableBench: A Comprehensive and Complex Benchmark for Table Question Answering** [![arXiv](https://img.shields.io/badge/arXiv-2408.09174-B31B1B.svg)](https://arxiv.org/pdf/2408.09174) - *Wu et al. (2024.08)*
 *   **Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs** [![arXiv](https://img.shields.io/badge/arXiv-2402.12424-B31B1B.svg)](https://arxiv.org/pdf/2402.12424) - *Deng et al. (2024.02)*
+*   **ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics** [![DOI](https://img.shields.io/badge/DOI-10.64898/2026.02.26.708361-blue.svg)](https://doi.org/10.64898/2026.02.26.708361) - *Yang et al. (2026.02)* [Code](https://github.com/cafferychen777/ChatSpatial)
 
 ### Conclusion and Hypothesis Validation
 
 LLMs providing feedback, verifying claims, replicating results, and generating reviews.
 
 *   **CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?** [![arXiv](https://img.shields.io/badge/arXiv-2503.21717-B31B1B.svg)](https://arxiv.org/pdf/2503.21717) - *Ou et al. (2025.03)*
+*   **REFUTE: Reasoning Over Evidence - Falsification, Uncertainty, Truth-grounding & Epistemics** [![HF Dataset](https://img.shields.io/badge/HuggingFace-dataset-yellow.svg)](https://huggingface.co/datasets/BGPT-OFFICIAL/refute) - *BGPT (2026.06)*. Open benchmark for scientific critique and epistemic calibration on recent science paper summaries, covering falsification, limitations, overclaims, missing-evidence refusal, calibration, and planted-flaw detection.
 *   **LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing**  [![arXiv](https://img.shields.io/badge/arXiv-2406.16253-B31B1B.svg)](https://arxiv.org/pdf/2406.16253) - *Du et al. (2024.06)*
 *   **AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews**  [![arXiv](https://img.shields.io/badge/arXiv-2408.10365-B31B1B.svg)](https://arxiv.org/pdf/2408.10365) - *Tyser et al. (2024.08)*
 *   **Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks**  [![Link](https://img.shields.io/badge/Link-LREC--COLING_2024-blue.svg)](https://aclanthology.org/2024.lrec-main.816.pdf) - *Zhou et al. (2024.05)*
@@ -164,7 +167,7 @@ Automated modeling of machine learning tasks, experiment design, and execution.
 *   **MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?** [![arXiv](https://img.shields.io/badge/arXiv-2504.09702-B31B1B.svg)](https://arxiv.org/pdf/2504.09702) - *Zhang et al. (2025.04)*
 *   **RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts** [![arXiv](https://img.shields.io/badge/arXiv-2411.15114-B31B1B.svg)](https://arxiv.org/pdf/2411.15114) - *Wijk et al. (2024.11)*
 *   **MLZero: A Multi-Agent System for End-to-end Machine Learning Automation** [![arXiv](https://img.shields.io/badge/arXiv-2505.13941-B31B1B.svg)](https://arxiv.org/pdf/2505.13941) - *Fang et al. (2025.05)*
-*   **AIDE: AI-Driven Exploration in the Space of Code** [![arXiv](https://img.shields.io/badge/arXiv-2502.13138-B31B1B.svg)](https://arxiv.org/pdf/2502.13138) - *Jiang et al. (2025.02)*
+*   **AIDE: AI-Driven Exploration in the Space of Code** [![GitHub](https://img.shields.io/badge/GitHub-WecoAI/aideml-blue.svg)](https://github.com/WecoAI/aideml) [![arXiv](https://img.shields.io/badge/arXiv-2502.13138-B31B1B.svg)](https://arxiv.org/abs/2502.13138) - *Jiang et al. (2025.02)*
 *   **Language Modeling by Language Models** [![arXiv](https://img.shields.io/badge/arXiv-2506.20249-B31B1B.svg)](https://arxiv.org/pdf/2506.20249) - *Cheng et al. (2025.06)*
 *   **MLGym: A New Framework and Benchmark for Advancing AI Research Agents** [![arXiv](https://img.shields.io/badge/arXiv-2502.14499-B31B1B.svg)](https://arxiv.org/pdf/2502.14499) - *Nathani et al. (2025.02)*
 
@@ -181,6 +184,7 @@ Automated data-driven analysis, statistical data modeling, and hypothesis valida
 *   **Large Language Models for Scientific Synthesis, Inference and Explanation**  [![arXiv](https://img.shields.io/badge/arXiv-2310.07984-B31B1B.svg)](https://arxiv.org/pdf/2310.07984) - *Zheng et al. (2023.10)*
 *   **MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem**  [![arXiv](https://img.shields.io/badge/arXiv-2505.14148-B31B1B.svg)](https://arxiv.org/pdf/2505.14148) - *Liu et al. (2025.05)*
 *   **DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?**  [![arXiv](https://img.shields.io/badge/arXiv-2409.07703-B31B1B.svg)](https://arxiv.org/pdf/2409.07703) - *Jing et al. (2024.09)*
+*   **OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents** [![arXiv](https://img.shields.io/badge/arXiv-2504.16918-B31B1B.svg)](https://arxiv.org/pdf/2504.16918) - *Thind et al. (2025.04)*
 
 
 ### Function Discovery
@@ -210,6 +214,8 @@ Autonomous research workflows for natural science discovery (e.g., chemistry, bi
 *   **Towards an AI co-scientist** [![arXiv](https://img.shields.io/badge/arXiv-2502.18864-B31B1B.svg)](https://arxiv.org/pdf/2502.18864) - *Gottweis et al. (2025.02)*
 *   **GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis** [![arXiv](https://img.shields.io/badge/arXiv-2507.21035-B31B1B.svg)](https://arxiv.org/pdf/2507.21035) - *Liu et al. (2025.07)*
 *   **Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search** [![arXiv](https://img.shields.io/badge/arXiv-2508.03661-B31B1B.svg)](https://arxiv.org/pdf/2508.03661) - *Wang and Zeng (2025.08)*
+*   **AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software** [![bioRxiv](https://img.shields.io/badge/bioRxiv-2026.06-b31b1b.svg)](https://www.biorxiv.org/content/10.64898/2026.06.12.731250v1) - *Xie et al. (2026.06)*
+*   **CASSIA: a multi-agent large language model for automated and interpretable cell annotation** [![DOI](https://img.shields.io/badge/DOI-10.1038/s41467--025--67084--x-blue.svg)](https://www.nature.com/articles/s41467-025-67084-x) - *Xie et al. (2025.12)*
 
 ### General Research
 
@@ -236,6 +242,7 @@ LLM-based systems operating as active agents capable of orchestrating and naviga
 *   **Zochi Technical Report** [![Link](https://img.shields.io/badge/Link-Intology.AI-blue.svg)](https://www.intology.ai/blog/zochi-tech-report) - *Intology AI (2025.03)*
 *   **Meet Carl: The First AI System To Produce Academically Peer-Reviewed Research** [![Link](https://img.shields.io/badge/Link-AutoScience.AI-blue.svg)](https://www.autoscience.ai/blog/meet-carl-the-first-ai-system-to-produce-academically-peer-reviewed-research) - *Autoscience Institute (2025.03)*
 *   **DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively** [![arXiv](https://img.shields.io/badge/arXiv-2509.26603-B31B1B.svg)](https://arxiv.org/pdf/2509.26603) - *Weng et al. (2025.09)*
+*   **Accelerating Social Science Research via Agentic Hypothesization and Experimentation** [![arXiv](https://img.shields.io/badge/arXiv-2602.07983-B31B1B.svg)](https://arxiv.org/pdf/2602.07983) - *Gupta et al. (2026.02)*
 
 ---
 

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

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/d3eb7394/README.md
-upstream_sha: d3eb7394
-imported_at: 2026-06-29
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/cf74319a/README.md
+upstream_sha: cf74319a
+imported_at: 2026-07-04
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -99,6 +99,7 @@ validated: false
 - [Scientific Agent Skills](https://github.com/K-Dense-AI/scientific-agent-skills) - Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science with 140+ ready-to-use skills and 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Antigravity, and the open Agent Skills standard (K-Dense-AI, 26K+ stars, 2025)
 - [SciAgent-Skills](https://github.com/jaechang-hits/SciAgent-Skills) - 197 bioinformatics and life science skills for Claude Code and AI agents, achieving 92.0% accuracy on BixBench. Covers RNA-seq, single-cell analysis, drug discovery, proteomics, and more. Powers OmicsHorizon (195+ stars, 2026)
 - [Medical Research Skills](https://github.com/aipoch/medical-research-skills) - Curated library of 550+ medical research agent skills spanning evidence insights, protocol design, omics/clinical data analysis, and academic writing; each skill is reviewed through MedSkillAudit and compatible with Claude Code, Codex, Open Code, OpenClaw, and SKILL.md-compatible agents (AIPOCH, 1.2K+ stars, MIT License, 2026)
+- [bioSkills](https://github.com/GPTomics/bioSkills) - Collection of SKILLS.md guiding AI coding agents (Claude Code, OpenAI Codex, Google Gemini, OpenCode, OpenClaw) through common bioinformatics workflows from basic sequence manipulation to advanced analyses such as single-cell RNA-seq and population genetics; evaluated on the Bio-Task Bench dataset (GPTomics, 969+ stars, MIT License, 2026)
 
 ---
 
@@ -166,6 +167,7 @@ validated: false
 ### High-Performance Document Processing
 - [MinerU (2024/2025)](https://github.com/opendatalab/MinerU) - SOTA multimodal document parsing with 1.2B parameters outperforming GPT-4o, converts PDFs to LLM-ready Markdown/JSON
 - [MinerU-Diffusion (OpenDataLab, ECCV 2026)](https://github.com/opendatalab/MinerU-Diffusion) - Diffusion-based document OCR framework replacing autoregressive decoding with block-level parallel diffusion decoding, enabling high-accuracy text recognition in scientific PDFs (613+ stars, MIT License)
+- [OpenDataLoader PDF (OpenDataLoader, 2025)](https://github.com/opendataloader-project/opendataloader-pdf) - Open-source PDF parser for AI-ready data, converting PDFs into Markdown/JSON/HTML/Tagged PDF with layout analysis and reading-order detection; ranks #1 overall on extraction benchmarks with deterministic bounding boxes and hybrid AI mode (26K+ stars, Apache 2.0)
 - [PDF-Extract-Kit (2024)](https://github.com/opendatalab/PDF-Extract-Kit) - Comprehensive toolkit for high-quality PDF content extraction with layout detection, formula recognition, and OCR
 - [Docling (IBM, AAAI 2025)](https://research.ibm.com/publications/docling-an-efficient-open-source-toolkit-for-ai-driven-document-conversion) - Multi-format (PDF/DOCX/PPTX/HTML/Images) → structured data (Markdown/JSON) with layout reconstruction, table/formula recovery
 - [Nougat (Meta AI)](https://github.com/facebookresearch/nougat) - Neural optical understanding for academic documents, transforms scientific PDFs to Markdown with mathematical formula support
@@ -208,6 +210,7 @@ validated: false
 - [ScholarAIO](https://github.com/ZimoLiao/scholaraio) - Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)
 - [BioMCP](https://github.com/genomoncology/biomcp) - Biomedical Model Context Protocol (MCP) server unifying literature search across PubMed/Europe PMC, entity pivoting across genes/variants/drugs/diseases/pathways/proteins, local study analytics, and Claude Code/Codex integration for agentic biomedical research (531+ stars, MIT License, 2025-2026)
 - [MATLAB Agentic Toolkit](https://github.com/matlab/matlab-agentic-toolkit) - Official MathWorks toolkit connecting AI agents to MATLAB via the MATLAB MCP Server and curated skills, enabling trusted engineering and scientific computing workflows with idiomatic code generation, testing, and error diagnosis in Claude Code, GitHub Copilot, OpenAI Codex, and Gemini CLI (686+ stars, BSD-3-Clause, 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)
 
 ### 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)
@@ -242,8 +245,10 @@ validated: false
 ### Autonomous Research Systems (2023-2025 Breakthroughs)
 - [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)
 - [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
 - [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
@@ -251,7 +256,11 @@ validated: false
 - [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)
 - [AlphaResearch](https://github.com/answers111/alpha-research) - Autonomous algorithm discovery combining evolutionary search with peer-review reward models, achieving best-known performance on circle packing problems
 - [AutoResearchClaw](https://github.com/aiming-lab/AutoResearchClaw) - Fully autonomous research from idea to paper with multi-agent debate, citation verification, and OpenClaw integration (11K+ stars, 2026)
+- [ARIS (Auto-Research-In-Sleep)](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) - Lightweight Markdown-only skills for autonomous ML research with cross-model review loops, idea discovery, and experiment automation; no framework lock-in, works with Claude Code, Codex, OpenClaw, or any LLM agent (12.8K+ stars, MIT License, 2026)
+- [Arbor](https://github.com/RUC-NLPIR/Arbor) - Generalist autonomous research agent that grows a hypothesis tree to optimize any measurable task, beating Claude Code and Codex by 2.5× on the same compute budget across BrowseComp, Terminal-Bench 2.0, math reasoning, and MLE-Bench Lite; supports native CLI, keyless Claude Code/Codex integration, and an MCP tool server (RUC-NLPIR, 866+ stars, Apache 2.0, 2026)
+- [NanoResearch](https://github.com/OpenRaiser/NanoResearch) - End-to-end autonomous AI research engine that turns an idea into a complete LaTeX paper by dispatching real computational experiments to local GPUs or SLURM clusters, collecting actual results, generating figures/tables, and writing a data-grounded manuscript rather than LLM hallucinations (OpenRaiser, 1.5K+ stars, MIT License, 2026)
 - [ScienceClaw](https://github.com/beita6969/ScienceClaw) - Self-evolving AI research colleague built on OpenClaw with 285+ runtime-adaptive skills across 28+ disciplines, persistent cross-session research memory, and zero-hallucination citation protocols; agent autonomously writes new SKILL.md files based on research patterns without redeployment (828+ stars, MIT License, 2026)
+- [ai4s-skills](https://github.com/ai4s-research/ai4s-skills) - Agent skills (SKILL.md + deterministic tools) for the AI4S workflow — topic exploration, literature survey, runnable experiments, publication-grade papers, and integrity audit, with every citation and number traceable to its source (by ai4s-research, maintainers of this list; MIT, 2026)
 - [Denario (AstroPilot-AI, Agents4Science 2025)](https://github.com/AstroPilot-AI/Denario) - Modular multi-agent scientific research assistant that automates idea generation, literature review, methodology design, code execution in Docker, visualization, LaTeX paper writing, and peer-review simulation across 10+ disciplines; winner of the NeurIPS 2025 Fair Universe Competition (573+ stars, GPL-3.0, 2025-2026)
 - [AI-Researcher](https://github.com/HKUDS/AI-Researcher) - Autonomous pipeline from literature review→hypothesis→algorithm implementation→publication-level writing with Scientist-Bench evaluation
 - [Agent Laboratory](https://agentlaboratory.github.io/) - Multi-agent workflows for complete research cycles with AgentRxiv for cumulative discovery
@@ -274,6 +283,7 @@ validated: false
 - [CORAL (arXiv 2026)](https://github.com/Human-Agent-Society/CORAL) - Robust, lightweight infrastructure for multi-agent autonomous self-evolution, built for autoresearch; agents run in isolated git worktrees, share knowledge through a common state directory, and are scored by a grader daemon; natively integrated with Claude Code, Codex, Cursor Agent, OpenCode, and Kiro (672+ stars, Apache 2.0)
 - [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)
 
 ### 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
@@ -542,6 +552,7 @@ validated: false
 - [gRNAde](https://github.com/chaitjo/geometric-rna-design) - Generative AI framework for inverse design of 3D RNA structure and function using geometric deep learning, learning design rules from 3D structures to capture complex tertiary interactions (pseudoknots, non-canonical base pairs) with expert-level accuracy for designing functional RNAs including aptamers and ribozymes (bioRxiv 2025)
 - [AIDO.ModelGenerator](https://github.com/genbio-ai/ModelGenerator) - GenBio AI's software stack for the AI-Driven Digital Organism, supporting adaptation and finetuning of multiscale biological foundation models across DNA, RNA, protein, structure, and single-cell tasks with reproducible CLIs and pretrained model zoo (2025)
 - [Evo 2](https://github.com/ArcInstitute/evo2) - Arc Institute's 40B-parameter genome foundation model trained on 9 trillion nucleotides from all domains of life, supporting 1M base pair context for generalist DNA/RNA/protein prediction and design (Nature 2026)
+- [Carbon (Hugging Face, 2026)](https://github.com/huggingface/carbon) - Family of causal genomic foundation models trained on 1T tokens (~6T DNA base pairs) from the Carbon Pretraining Corpus, combining eukaryote genes, mRNA transcripts, and prokaryote genomes with a hybrid text/6-mer tokenizer; Carbon-3B matches or beats Evo2-7B on zero-shot DNA evaluations including sequence recovery, variant effect prediction, and perturbations (Apache 2.0, 201+ stars)
 - [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) - Foundation models for genomics and transcriptomics pretrained on 3,000+ human genomes and 850+ diverse species, enabling chromatin accessibility prediction, splice site detection, and promoter classification across multiple model scales (InstaDeep, NVIDIA & TUM, Nature Methods 2023)
 - [HyenaDNA](https://github.com/HazyResearch/hyena-dna) - Long-range genomic foundation model using subquadratic Hyena operators instead of Transformer attention, enabling context lengths up to 1 million nucleotides for chromosome-scale DNA sequence modeling and downstream genomics tasks (Stanford Hazy Research, NeurIPS 2023, 784+ stars, Apache 2.0)
 - [Caduceus (ICML 2024)](https://github.com/kuleshov-group/caduceus) - Bi-directional DNA language model based on the Mamba state space architecture, enabling efficient long-range genomic sequence modeling with linear-time complexity and built-in reverse-complement equivariance; achieves strong performance on chromatin accessibility, enhancer, and promoter prediction benchmarks (Stanford & UC Berkeley, 500+ stars)
@@ -605,6 +616,7 @@ validated: false
 - [TITAN (Nature Medicine 2024)](https://github.com/mahmoodlab/TITAN) - Multimodal whole-slide pathology foundation model jointly pretrained on H&E histology and diagnostic text reports, enabling zero-shot cancer subtyping, biomarker prediction, and multimodal reasoning across diverse cancer types (Mahmood Lab, 341+ stars)
 - [Virchow (Nature Medicine 2024)](https://huggingface.co/paige-ai/Virchow) - Self-supervised pathology foundation model (ViT-Huge, 632M parameters) pretrained via DINOv2 on 1.5M whole-slide images from Memorial Sloan Kettering across 17 cancer types, with Virchow2 follow-up scaling to 3.1M slides and mixed magnifications, achieving SOTA on biomarker prediction, mutation classification, and rare cancer detection (Paige AI & MSK)
 - [TRIDENT (2025)](https://github.com/mahmoodlab/TRIDENT) - Toolkit for large-scale whole-slide image processing supporting 22+ patch encoders (UNI, CONCH, Virchow, H-Optimus-0, etc.), slide encoders (TITAN, GigaPath, PRISM, CHIEF, Madeleine, Feather), tissue segmentation, and multi-GPU inference with end-to-end pipeline and smart resume for standardized deployment of computational pathology foundation models (Mahmood Lab, Harvard Medical School, 553+ stars)
+- [Feather (Mahmood Lab, ICML 2025 Spotlight)](https://github.com/mahmoodlab/MIL-Lab) - Lightweight supervised slide foundation model with 0.9M parameters pretrained on 24K whole-slide images for pan-cancer morphological classification, achieving competitive performance with much larger self-supervised models (TITAN, GigaPath) while enabling finetuning on consumer-grade GPUs; includes standardized MIL implementations and benchmarking across 15+ classification tasks (Mahmood Lab, Harvard Medical School, 153+ stars)
 - [PathChat (Nature Medicine 2024)](https://github.com/MahmoodLab/PathChat) - Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars)
 - [HEST (NeurIPS 2024)](https://github.com/mahmoodlab/HEST) - Dataset and benchmarking framework integrating histology and spatial transcriptomics, enabling multimodal analysis of whole-slide images with matched spatial gene expression for advancing computational pathology and tissue microenvironment research (Mahmood Lab, Harvard Medical School, 411+ stars)
 
@@ -804,6 +816,7 @@ validated: false
 
 ### Physics
 - [The Well](https://github.com/PolymathicAI/the_well) - 15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)
+- [RealPDEBench (ICLR 2026 Oral)](https://github.com/AI4Science-WestlakeU/RealPDEBench) - First scientific ML benchmark with paired real-world measurements and matched numerical simulations for complex physical systems, featuring 5 scenarios, 700+ trajectories, 10 baseline models, and 9 evaluation metrics with HuggingFace datasets and model checkpoints (Westlake University, CC BY-NC 4.0)
 - [LIGO Open Science Center](https://gwosc.org/) - Gravitational wave data
 - [Particle Data Group](https://pdg.lbl.gov/) - Particle physics data
 - [OpenQuantumMaterials](https://www.quantum-materials.org/) - Quantum materials data