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新论文 · v1arXiv:2611.12434

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance

Zeng Yi, Seán Ó hÉigeartaigh, Cao Gongce, Lu Enmeng, Chen Yu, Han Kaiyu, Li Kai, Wang Zhengqi 等 110 位作者

原文摘要

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance is a key outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on AI in 2026. Drawing on the latest developments in frontier AI capabilities and applications, the report systematically examines the evolving risk landscape, analyzing key risk areas, their severity and response priorities. Building on a review of existing global governance practices and an assessment of frontier AI governance readiness, it identifies key governance gaps and weaknesses and proposes directions and practical mechanisms for coordinated responses. The report provides a reference for government departments, international organizations, businesses, research institutions and other stakeholders. It aims to strengthen the international community's understanding of and consensus on frontier AI risks, enhance preparedness and collective response capacity, and advance the safe, reliable and responsible development of frontier AI to better serve the common well-being of humanity.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12139

BioMARS 2.0: a robotic biologist for formulating and adapting biological experiments

Yibo Qiu, Zan Huang, Yilin Qiao, Ziyang Ling, Yi Wang, Zhengyang Lu, Hangke Peng, Haoliang Ye 等 22 位作者

原文摘要

Biological experimentation is not reducible to a single optimization problem, but requires decisions at multiple levels of abstraction: translating scientific knowledge into experimental procedures, adapting culture conditions to measured phenotypes and selecting interventions that test biological hypotheses. Automating experimental execution does not, by itself, determine which experiments should be performed next or how they should change in response to biological feedback. Here we introduce BioMARS, a human-in-the-loop multi-agent robotic system that connects knowledge-guided experimental decision-making with robotic execution and biological feedback. At the procedural level, BioMARS generated literature-grounded protocols and traceable action plans, improving expert-reviewed protocol quality scores across four model backends. At the parametric level, BioMARS used image-derived phenotypes from robotic retinal organoid culture to select subsequent experimental conditions, with higher reported scores in later rounds of the three-round experiment. At the intervention level, BioMARS nominated drug-repurposing candidates from biomedical evidence and advanced six candidates to wet-lab testing by researchers. Together, these studies demonstrate feedback-driven robotic biological experimentation within a framework that supports decisions about experimental procedures, culture conditions and biological interventions.

cs.RO · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06442

TwinBridge: Digital Twin-Driven Policy and Reward Transfer for Real-World Reinforcement Learning in Laboratory Robotics

Shu'ang Sun, Yibo Qiu, Haoliang Ye, Zan Huang, Ao Tian, Kan Ding, Ronald Xu, Mingzhai Sun

原文摘要

Online reinforcement learning offers an effective approach to precise laboratory manipulation, yet costly real-world demonstrations and limited access to dense process rewards hinder its practical application. We present TwinBridge, a digital twin-driven framework that supplies policy initialization and dense visual rewards for real-world online learning. We construct digital twins by combining 3D Gaussian splatting with physics simulation. Here, simulator states provide task events and geometric progress under physical prerequisites and ordering constraints, yielding rewards for policy pretraining with simulated demonstrations and reinforcement learning. We distill this supervision into a visual reward model. During real-world interaction, its predictions are combined with execution history to compute dense rewards for human-in-the-loop policy refinement. Across five real-world laboratory tasks, TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task. Experiments show that dense rewards improve interaction efficiency over sparse rewards, and simulation-based policy initialization further accelerates learning.

cs.RO

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arXiv ↗PDF ↗