Cover Image

AI Scientist via Synthetic Task Scaling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cai, Ziyang, Behl, Harkirat
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scaling Synthetic Task Generation for Agents via Exploration
by: Ramrakhya, Ram, et al.
Published: (2025)

A Survey of AI Scientists
by: Tie, Guiyao, et al.
Published: (2025)

"Turing Tests" For An AI Scientist
by: Yin, Xiaoxin
Published: (2024)

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

The AI Data Scientist
by: Akimov, Farkhad, et al.
Published: (2025)

Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Tasks
by: Hou, Irene, et al.
Published: (2026)

CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
by: Kim, Yubin, et al.
Published: (2026)

Towards a Medical AI Scientist
by: Wu, Hongtao, et al.
Published: (2026)

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

Do Two AI Scientists Agree?
by: Fu, Xinghong, et al.
Published: (2025)

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

How Far Are AI Scientists from Changing the World?
by: Xie, Qiujie, et al.
Published: (2025)

Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research
by: Borrett, Thomas, et al.
Published: (2026)

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

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

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
by: Wu, Yujun, et al.
Published: (2026)

MirrorMind: Empowering OmniScientist with the Expert Perspectives and Collective Knowledge of Human Scientists
by: Zeng, Qingbin, et al.
Published: (2025)

SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems
by: Yang, Zonglin, et al.
Published: (2026)

MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
by: Kang, Mingu, et al.
Published: (2024)

When AI Does Science: Evaluating the Autonomous AI Scientist KOSMOS in Radiation Biology
by: Nusrat, Humza, et al.
Published: (2025)

AI Scientists Fail Without Strong Implementation Capability
by: Zhu, Minjun, et al.
Published: (2025)

LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation
by: Wang, Lei, et al.
Published: (2026)

From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
by: Ali, Hyacinth, et al.
Published: (2026)

MIND: AI Co-Scientist for Material Research
by: Ahn, Geonhee, et al.
Published: (2026)

Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
by: Sivaraman, Venkatesh, et al.
Published: (2025)

Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
by: Michel, Thomas, et al.
Published: (2025)

AI Co-Scientist for Knowledge Synthesis in Medical Contexts: A Proof of Concept
by: Rahgozar, Arya, et al.
Published: (2026)

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
by: Pan, Junshu, et al.
Published: (2026)

Circuit-Aware SAT Solving: Guiding CDCL via Conditional Probabilities
by: Zhu, Jiaying, et al.
Published: (2025)

Don’t Give It Away: The Hidden Risks of Generative AI for Scientists
by: Brandão, Anarosa
Published: (2026)

Video Text Preservation with Synthetic Text-Rich Videos
by: Liu, Ziyang, et al.
Published: (2025)

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?
by: Hägele, Alexander, et al.
Published: (2026)

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
by: Yamada, Yutaro, et al.
Published: (2025)

CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists
by: Yang, Junlin, et al.
Published: (2026)

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

The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
by: Luo, Ziming, et al.
Published: (2025)

Enabling AI Scientists to Recognize Innovation: A Domain-Agnostic Algorithm for Assessing Novelty
by: Wang, Yao, et al.
Published: (2025)

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

LinkedIn posts about Data Scientists' displacement by AI
by: Samokhina, Olga, et al.
Published: (2026)

Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
by: Tang, Xiangru, et al.
Published: (2024)