From 40172d7f6938f8cbbe72c68d942dd11e84ed66af Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 17 Jul 2026 03:10:44 +0000 Subject: [PATCH] [upstream-sync] README.md from ai-boost/awesome-ai-for-science@e5103bf4 [catalogue] --- .../ai-boost-awesome-ai-for-science/catalogue/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md b/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md index 1b4f73c..3e87a84 100644 --- a/upstream/ai-boost-awesome-ai-for-science/catalogue/README.md +++ b/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/709cee28/README.md -upstream_sha: 709cee28 -imported_at: 2026-07-15 +upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/e5103bf4/README.md +upstream_sha: e5103bf4 +imported_at: 2026-07-17 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -290,6 +290,7 @@ validated: false - [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) ### 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 @@ -798,6 +799,7 @@ validated: false - [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) - [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) +- [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 -- 2.54.0