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@@ -2,9 +2,9 @@
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title: "Awesome LLM Scientific Discovery [](https://awesome.re)"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery/blob/b39b55ac/README.md
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upstream_sha: b39b55ac
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imported_at: 2026-06-26
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upstream_source: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery/blob/7fcb8811/README.md
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upstream_sha: 7fcb8811
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imported_at: 2026-07-03
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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@@ -104,6 +104,7 @@ LLMs assisting in experimental protocol planning, workflow design, and scientifi
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* **Natural Language to Code Generation in Interactive Data Science Notebooks** [](https://arxiv.org/pdf/2212.09248) - *Yin et al. (2022.12)*
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* **DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation** [](https://arxiv.org/pdf/2211.11501) - *Lai et al. (2022.11)*
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* **Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents**, [](https://arxiv.org/pdf/2502.16069) - *Kon et al. (2025.02)*
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* **AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing** [](https://arxiv.org/pdf/2602.17607) - *Du et al. (2026.02)*
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### Data Analysis and Organization
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@@ -117,12 +118,14 @@ LLMs assisting in data-driven analysis, tabular/chart reasoning, statistical rea
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* **Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding** [](https://arxiv.org/pdf/2401.04398) - *Wang et al. (2024.01)*
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* **TableBench: A Comprehensive and Complex Benchmark for Table Question Answering** [](https://arxiv.org/pdf/2408.09174) - *Wu et al. (2024.08)*
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* **Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs** [](https://arxiv.org/pdf/2402.12424) - *Deng et al. (2024.02)*
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* **ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics** [](https://doi.org/10.64898/2026.02.26.708361) - *Yang et al. (2026.02)* [Code](https://github.com/cafferychen777/ChatSpatial)
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### Conclusion and Hypothesis Validation
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LLMs providing feedback, verifying claims, replicating results, and generating reviews.
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* **CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?** [](https://arxiv.org/pdf/2503.21717) - *Ou et al. (2025.03)*
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* **REFUTE: Reasoning Over Evidence - Falsification, Uncertainty, Truth-grounding & Epistemics** [](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.
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* **LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing** [](https://arxiv.org/pdf/2406.16253) - *Du et al. (2024.06)*
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* **AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews** [](https://arxiv.org/pdf/2408.10365) - *Tyser et al. (2024.08)*
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* **Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks** [](https://aclanthology.org/2024.lrec-main.816.pdf) - *Zhou et al. (2024.05)*
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@@ -164,7 +167,7 @@ Automated modeling of machine learning tasks, experiment design, and execution.
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* **MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?** [](https://arxiv.org/pdf/2504.09702) - *Zhang et al. (2025.04)*
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* **RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts** [](https://arxiv.org/pdf/2411.15114) - *Wijk et al. (2024.11)*
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* **MLZero: A Multi-Agent System for End-to-end Machine Learning Automation** [](https://arxiv.org/pdf/2505.13941) - *Fang et al. (2025.05)*
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* **AIDE: AI-Driven Exploration in the Space of Code** [](https://arxiv.org/pdf/2502.13138) - *Jiang et al. (2025.02)*
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* **AIDE: AI-Driven Exploration in the Space of Code** [](https://github.com/WecoAI/aideml) [](https://arxiv.org/abs/2502.13138) - *Jiang et al. (2025.02)*
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* **Language Modeling by Language Models** [](https://arxiv.org/pdf/2506.20249) - *Cheng et al. (2025.06)*
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* **MLGym: A New Framework and Benchmark for Advancing AI Research Agents** [](https://arxiv.org/pdf/2502.14499) - *Nathani et al. (2025.02)*
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@@ -181,6 +184,7 @@ Automated data-driven analysis, statistical data modeling, and hypothesis valida
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* **Large Language Models for Scientific Synthesis, Inference and Explanation** [](https://arxiv.org/pdf/2310.07984) - *Zheng et al. (2023.10)*
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* **MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem** [](https://arxiv.org/pdf/2505.14148) - *Liu et al. (2025.05)*
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* **DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?** [](https://arxiv.org/pdf/2409.07703) - *Jing et al. (2024.09)*
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* **OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents** [](https://arxiv.org/pdf/2504.16918) - *Thind et al. (2025.04)*
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### Function Discovery
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@@ -210,6 +214,8 @@ Autonomous research workflows for natural science discovery (e.g., chemistry, bi
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* **Towards an AI co-scientist** [](https://arxiv.org/pdf/2502.18864) - *Gottweis et al. (2025.02)*
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* **GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis** [](https://arxiv.org/pdf/2507.21035) - *Liu et al. (2025.07)*
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* **Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search** [](https://arxiv.org/pdf/2508.03661) - *Wang and Zeng (2025.08)*
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* **AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software** [](https://www.biorxiv.org/content/10.64898/2026.06.12.731250v1) - *Xie et al. (2026.06)*
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* **CASSIA: a multi-agent large language model for automated and interpretable cell annotation** [](https://www.nature.com/articles/s41467-025-67084-x) - *Xie et al. (2025.12)*
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### General Research
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@@ -236,6 +242,7 @@ LLM-based systems operating as active agents capable of orchestrating and naviga
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* **Zochi Technical Report** [](https://www.intology.ai/blog/zochi-tech-report) - *Intology AI (2025.03)*
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* **Meet Carl: The First AI System To Produce Academically Peer-Reviewed Research** [](https://www.autoscience.ai/blog/meet-carl-the-first-ai-system-to-produce-academically-peer-reviewed-research) - *Autoscience Institute (2025.03)*
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* **DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively** [](https://arxiv.org/pdf/2509.26603) - *Weng et al. (2025.09)*
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* **Accelerating Social Science Research via Agentic Hypothesization and Experimentation** [](https://arxiv.org/pdf/2602.07983) - *Gupta et al. (2026.02)*
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---
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@@ -2,9 +2,9 @@
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title: "Readme"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/dbd35d0a/README.md
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upstream_sha: dbd35d0a
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imported_at: 2026-06-28
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upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/0cee4659/README.md
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upstream_sha: 0cee4659
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imported_at: 2026-07-01
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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@@ -81,6 +81,7 @@ validated: false
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- [CORE](https://core.ac.uk/) - Aggregator of open access research papers
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- [Connected Papers](https://www.connectedpapers.com/) - AI-powered visual graph for exploring academic papers and discovering connected research through citation networks and semantic similarity
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- [PaSa (ByteDance)](https://github.com/bytedance/pasa) - Advanced paper search agent powered by large language models, autonomously invoking search tools, reading papers, and selecting references to deliver comprehensive and accurate results for complex scholarly queries (1.5K+ stars, Apache 2.0, 2024)
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- [paper-search-mcp](https://github.com/openags/paper-search-mcp) - MCP server, CLI, and agent skills for searching and downloading academic papers from multiple open sources (arXiv, PubMed, bioRxiv, Semantic Scholar, OpenAlex, CORE, Europe PMC, etc.) with unified, deduplicated, LLM-friendly retrieval and an OA-first download fallback chain (OpenAGS, 1.9K+ stars, MIT License, 2025)
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### Data Analysis & Visualization
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- [PandasAI](https://github.com/Sinaptik-AI/pandas-ai) - Conversational data analysis using natural language
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@@ -206,6 +207,7 @@ validated: false
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- [AutoR](https://github.com/AutoX-AI-Labs/AutoR) - Human-centered research OS with terminal-first harness and local browser Studio, turning research work into reproducible artifact-backed runs through a 9-stage workflow with human approval gates, resume/rollback controls, and venue-aware manuscript packaging (1K+ stars, 2026)
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- [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)
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- [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)
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- [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)
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### Literature Management Plugins
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- [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)
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@@ -249,6 +251,9 @@ validated: false
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- [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)
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- [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
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- [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)
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- [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)
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- [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)
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- [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)
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- [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)
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- [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)
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- [AI-Researcher](https://github.com/HKUDS/AI-Researcher) - Autonomous pipeline from literature review→hypothesis→algorithm implementation→publication-level writing with Scientist-Bench evaluation
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@@ -540,6 +545,7 @@ validated: false
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- [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)
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- [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)
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- [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)
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- [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)
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- [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)
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- [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)
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- [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)
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### Physics
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- [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)
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- [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)
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- [LIGO Open Science Center](https://gwosc.org/) - Gravitational wave data
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- [Particle Data Group](https://pdg.lbl.gov/) - Particle physics data
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- [OpenQuantumMaterials](https://www.quantum-materials.org/) - Quantum materials data
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Reference in New Issue
Block a user