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20202026
most citedThe Generative AI Paradox: "What It Can Create, It May Not Understand"

10 citations · 28 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Efficient Test-Time Adaptation through Human-AI Interaction

Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao +22

AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely meet the personal bar profes…

cs.AI2026

ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment

Hongjue Zhao, Haosen Sun, Jiangtao Kong +8

Activation steering, or representation engineering, offers a lightweight approach to align large language models (LLMs) by manipulating their internal activations at inference time…

cs.AI2024

HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions

Xuhui Zhou, Hyunwoo Kim, Faeze Brahman +9

AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examini…

cs.AI2024

A Roadmap to Pluralistic Alignment

Taylor Sorensen, Jared Moore, Jillian Fisher +9

With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, a…

cs.AI202310 cited

The Generative AI Paradox: "What It Can Create, It May Not Understand"

Peter West, Ximing Lu, Nouha Dziri +11

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…