Hierarchical Experimentalist Agents
提出分层实验者代理框架,通过层级结构提升AI自主实验与决策能力,为智能体研究开辟新路径。
arXiv:2606.29315v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support hum…
提出分层实验者代理框架,通过层级结构提升AI自主实验与决策能力,为智能体研究开辟新路径。
arXiv:2606.29315v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support hum…
AI助力经济学研究:用开放问题测试AI工作流,探索公共品菜单稳定性的新实验方法。
arXiv:2606.16989v1 Announce Type: cross Abstract: Using an open problem from the EC 2025 paper "Stable Menus of Public Goods" as a testbed, we conduct…
预算受限下如何高效筛选微预训练配方?分阶段因子筛选方法或可稳定识别早期效应结构
arXiv:2606.05186v1 Announce Type: cross Abstract: Budget-constrained micro-pretraining often requires triaging many candidate recipes on a shared acce…
LLM在抽象问卷中左倾,但在具体政策投票中却未必,双工具方法颠覆认知。
arXiv:2606.00048v1 Announce Type: cross Abstract: Prior research has established that instruction-tuned large language models exhibit left-of-center p…
大语言模型在交互场景中如何主动提问降低不确定性?这篇论文提出对话感知贝叶斯实验设计方法。
arXiv:2606.01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades i…
ICML 2026 提出用在线规划解决约束贝叶斯实验设计,理论结合实践,方法新颖。
arXiv:2605.26990v1 Announce Type: cross Abstract: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential…
机器人实验室能自动处理液体、培养细胞、操作仪器,让科学家专注实验设计与结果解读,大幅提升科研效率。
Nature, Published online: 27 May 2026; doi:10.1038/d41586-026-01713-3 A new AI tool has generated an atlas of more than one billion predicted protein …
大语言模型模拟实验只是观察性研究,干预错觉被无情拆穿
arXiv:2605.20767v1 Announce Type: cross Abstract: Large language models (LLMs) show potential as simulators of human behavior, offering a scalable way…