Imagen de Portada

CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Junlin, Zhang, Dylan, Song, Xiangchen, Dai, Qirun, Liu, Xiao, Chen, Yuen, Vashishtha, Aniket, Shi, Jing, Tan, Chenhao, Peng, Hao
Formato:	Preprint
Publicado:	2026
Materias:	Artificial Intelligence Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Executable Counterfactuals: Improving LLMs' Causal Reasoning Through Code
por: Vashishtha, Aniket, et al.
Publicado: (2025)

The Best Instruction-Tuning Data are Those That Fit
por: Zhang, Dylan, et al.
Publicado: (2025)

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
por: Dai, Qirun, et al.
Publicado: (2025)

CauScientist: Teaching LLMs to Respect Data for Causal Discovery
por: Peng, Bo, et al.
Publicado: (2026)

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
por: Vashishtha, Aniket, et al.
Publicado: (2023)

A Note on Dynamic Bidirected Dyck-Reachability with Cycles
por: Zhang, Qirun
Publicado: (2024)

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
por: Lyu, Yougang, et al.
Publicado: (2026)

Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
por: Srivastava, Ashutosh, et al.
Publicado: (2025)

Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery
por: Weidener, Lukas, et al.
Publicado: (2026)

Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
por: Wainrib, Gilles, et al.
Publicado: (2026)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
por: Lu, Chris, et al.
Publicado: (2024)

Kosmos: An AI Scientist for Autonomous Discovery
por: Mitchener, Ludovico, et al.
Publicado: (2025)

LabOS: The AI-XR Co-Scientist That Sees and Works With Humans
por: Cong, Le, et al.
Publicado: (2025)

Towards a Medical AI Scientist
por: Wu, Hongtao, et al.
Publicado: (2026)

Teaching Transformers Causal Reasoning through Axiomatic Training
por: Vashishtha, Aniket, et al.
Publicado: (2024)

Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
por: Feng, Haodong, et al.
Publicado: (2025)

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
por: Sauter, Andreas W. M., et al.
Publicado: (2024)

Scaling Laws in Scientific Discovery with AI and Robot Scientists
por: Zhang, Pengsong, et al.
Publicado: (2025)

SR-Scientist: Scientific Equation Discovery With Agentic AI
por: Xia, Shijie, et al.
Publicado: (2025)

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
por: Bisht, Harshit, et al.
Publicado: (2026)

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
por: Liu, Haoyang, et al.
Publicado: (2024)

AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
por: Somasekharan, Nithin, et al.
Publicado: (2026)

AutoVeriFix+: High-Correctness RTL Generation via Trace-Aware Causal Fix and Semantic Redundancy Pruning
por: Tan, Yan, et al.
Publicado: (2026)

CRYPTONITE: Scalable Accelerator Design for Cryptographic Primitives and Algorithms
por: Maheswaran, Karthikeya Sharma, et al.
Publicado: (2025)

OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
por: Shao, Chenyang, et al.
Publicado: (2025)

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
por: Zhang, Pengsong, et al.
Publicado: (2025)

IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
por: Sheth, Ivaxi, et al.
Publicado: (2026)

ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI
por: Maturo, Fabrizio, et al.
Publicado: (2025)

SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
por: Zhu, Kunlun, et al.
Publicado: (2025)

Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
por: Dong, Xinshuai, et al.
Publicado: (2025)

Scalable Time-Series Causal Discovery with Approximate Causal Ordering
por: Jiao, Ziyang, et al.
Publicado: (2024)

Social Learning through Interactions with Other Agents: A Survey
por: Hillier, Dylan, et al.
Publicado: (2024)

AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists
por: Li, Yifei, et al.
Publicado: (2025)

Scalable Variational Causal Discovery Unconstrained by Acyclicity
por: Hoang, Nu, et al.
Publicado: (2024)

UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
por: Bi, Tingzhu, et al.
Publicado: (2025)

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
por: Kıcıman, Emre, et al.
Publicado: (2023)

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
por: Zhou, Lianhao, et al.
Publicado: (2025)

LabMed Discovery
Publicado: (2025)

From AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents
por: Xia, Sirui, et al.
Publicado: (2026)

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
por: Amin, Alan Nawzad, et al.
Publicado: (2024)