most citedAI Assistance Reduces Persistence and Hurts Independent Performance

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

A Roadmap to Impactful Pluralistic Alignment Research

Elinor Poole-Dayan, Jillian Fisher, Atoosa Kasirzadeh +3

Pluralistic value alignment---the goal of building AI systems that represent and serve diverse human values and perspectives---has emerged as an active research agenda. Yet, there'…

cs.CY2026

Europe and the Geopolitics of AGI: The Need for a Preparedness Plan

Maximilian Negele, Daan Juijn, Afek Shamir +8

Artificial general intelligence (AGI)--defined here as AI systems that match or exceed humans at most economically useful cognitive work--has moved from speculation to the centre o…

cs.CL2026

Multi-User Large Language Model Agents

Shu Yang, Shenzhe Zhu, Hao Zhu +5

Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a…

cs.HC2026

AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent

Suyash Fulay, Prerna Ravi, Emily Kubin +3

AI is increasingly used to scale collective decision-making, but far less attention has been paid to how such systems can support procedural legitimacy, particularly the conditions…

cs.HC2026

Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice

Prerna Ravi, Om Gokhale, Suyash Fulay +3

Deliberative democratic theory suggests that civic competence: the capacity to navigate disagreement, weigh competing values, and arrive at collective decisions is not innate but d…

cs.AI2026★ 2 cited

AI Assistance Reduces Persistence and Hurts Independent Performance

Grace Liu, Brian Christian, Tsvetomira Dumbalska +2

People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the o…

cs.CY2026

Evaluating Human-AI Safety: A Framework for Measuring Harmful Capability Uplift

Michelle Vaccaro, Jaeyoon Song, Abdullah Almaatouq +1

Current frontier AI safety evaluations emphasize static benchmarks, third-party annotations, and red-teaming. In this position paper, we argue that AI safety research should focus…