most citedFantastic Copyrighted Beasts and How (Not) to Generate Them

2 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds

Shannon Zejiang Shen, Yapei Chang, Qiyao Liang +13

As teams of AI agents take on larger projects and long-standing open problems, agent collaboration offers another dimension for scaling inference-time compute. To understand what m…

cs.AI2026

Scaling Participation in Modular AI Systems

Shangbin Feng, Yike Wang, Weijia Shi +3

Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized mark…

cs.CL2024★ 1 cited

Evaluating Copyright Takedown Methods for Language Models

Boyi Wei, Weijia Shi, Yangsibo Huang +5

Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. These models can memorize and generate content…

cs.CV2024★ 2 cited

Fantastic Copyrighted Beasts and How (Not) to Generate Them

Luxi He, Yangsibo Huang, Weijia Shi +7

Recent studies show that image and video generation models can be prompted to reproduce copyrighted content from their training data, raising serious legal concerns about copyright…

cs.CR2024★ 2 cited

AI Risk Management Should Incorporate Both Safety and Security

Xiangyu Qi, Yangsibo Huang, Yi Zeng +22

The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety…