[upstream-sync] README.md from ai-boost/awesome-ai-for-science@86c5f147 [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
-upstream_source: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery/blob/b39b55ac/README.md
-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/e92baf5c/README.md
-upstream_sha: e92baf5c
-imported_at: 2026-07-02
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/86c5f147/README.md
+upstream_sha: 86c5f147
+imported_at: 2026-07-03
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -167,6 +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
@@ -245,6 +246,7 @@ validated: false
 - [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)
 - [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
@@ -610,6 +612,7 @@ 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)