arXiv 2610.28887 · cs.CL · 2026-10-20
Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures
Yujian Liu, Li An, Zelin He, Jacob Helwig, Jiabao Ji, Rohit Jain, Siyu Zhu, Shiyu Chang
University of California, Santa Barbara
在最新榜 #325 本站收录于 2026-10-24
一句话要点
UCSB 用多个专长不同的 AI 研究员智能体组队，自动探索线性注意力架构：靠不同背景促成正交探索、跳出局部最优，并在实验昂贵、只能测少量想法时平衡探索与利用，挑选由谁提出和执行下一轮实验。
UCSB builds teams of AI researcher agents with varied expertise to autonomously discover linear-attention architectures, using diversity for orthogonal exploration and balancing exploration and exploitation when choosing which researchers propose and run the next costly experiments.
阅读 arXiv 原文 PDF Hugging Face 讨论
主题与学科
同主题：智能体
- Raven: The Harness of Harnesses for Composable Agentic Intelligence ▲748
- False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents ▲642
- Omni-IO Skills: Harnessing Your Agent Omni-Native ▲485
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business ▲388
- Player-1: Learning to Play from Synthetic Experience ▲347
同主题：AI for Science
- Scaling Parameter-Efficient Fine-Tuning for Brain Encoding 2026-10-19 · ▲247
- Into the Unknown: Benchmarking AI agents on open scientific research problems 2026-10-13 · ▲144
- EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery 2026-09-30 · ▲112
- Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts 2026-09-29 · ▲103
- X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning 2026-10-12 · ▲79
同机构：University of California
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-26。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。