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 promptadmin
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4b2bcc5978 | ||
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promptadmin
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c6fd0dfd73 |
@@ -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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@@ -2,8 +2,8 @@
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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/3756bb0f/README.md
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upstream_sha: 3756bb0f
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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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@@ -252,6 +252,7 @@ validated: false
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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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