most citedThe AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

30 citations · 30 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2025

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

SIMA team, Adrian Bolton, Alexander Lerchner +63

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…

cs.LG2025

Foundation Model Self-Play: Open-Ended Strategy Innovation via Foundation Models

Aaron Dharna, Cong Lu, Jeff Clune

Multi-agent interactions have long fueled innovation, from natural predator-prey dynamics to the space race. Self-play (SP) algorithms try to harness these dynamics by pitting agen…

cs.AI202530 cited

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Yutaro Yamada, Robert Tjarko Lange, Cong Lu +5

AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing t…

cs.LG2025

Automated Capability Discovery via Foundation Model Self-Exploration

Cong Lu, Shengran Hu, Jeff Clune

Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to prec…

cs.RO2024

IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving

Clémence Grislain, Risto Vuorio, Cong Lu +1

Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achiev…