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Tsinghua

6 articles · Page 1 of 1
PaperAgent
PaperAgent
Jun 28, 2026 · Artificial Intelligence

AgentSociety²: An Integrated Research Environment Redefining Executable Social Science

AgentSociety² combines literature review, hypothesis generation, experiment design, large‑scale simulation, result analysis, and paper drafting into a unified platform where silicon‑based participants and AI social scientists collaborate, enabling fully traceable, reproducible social science experiments from micro‑behaviour to city‑scale scenarios.

AI AgentsAgentSocietyExecutable Social Science
0 likes · 10 min read
AgentSociety²: An Integrated Research Environment Redefining Executable Social Science
Data Party THU
Data Party THU
May 24, 2026 · Artificial Intelligence

ICLR 2026 by the Numbers: Tsinghua Leads Overall, US Dominates Oral Papers

The ICLR 2026 analysis shows Chinese institutions, led by Tsinghua with 331 papers, dominate total submissions, while U.S. institutions retain the lead in high‑impact Oral papers, highlighting a shift in volume but not in top‑tier research influence.

AI research trendsICLR 2026Oral papers
0 likes · 7 min read
ICLR 2026 by the Numbers: Tsinghua Leads Overall, US Dominates Oral Papers
Machine Heart
Machine Heart
May 21, 2026 · Artificial Intelligence

Learning Adaptive Gaussian Sampling for 3D Generation: Density‑Sampled Gaussians (DeG) at SIGGRAPH 2026

The SIGGRAPH 2026 paper “Generative 3D Gaussians with Learned Density Control” introduces Density‑Sampled Gaussians (DeG), a differentiable framework that lets a model learn where to place Gaussian splats by sampling from a learned spatial density, enabling arbitrary‑budget, non‑uniform 3D representations with higher quality per cost.

3D Gaussian SplattingAdaptive SamplingSIGGRAPH 2026
0 likes · 14 min read
Learning Adaptive Gaussian Sampling for 3D Generation: Density‑Sampled Gaussians (DeG) at SIGGRAPH 2026
Code of Duty
Code of Duty
May 12, 2026 · Fundamentals

Speed Up Conda and pip in China: 2026 Guide to Configuring Domestic Mirrors

This guide explains why the default Conda and pip sources are slow in China, shows how to check current settings, switch to Tsinghua mirrors for both Conda and pip, verify the configuration, and provides additional tips such as using mamba, creating isolated environments, and avoiding common AI‑project pitfalls.

AIMambaPython
0 likes · 7 min read
Speed Up Conda and pip in China: 2026 Guide to Configuring Domestic Mirrors
AI Explorer
AI Explorer
Mar 27, 2026 · Artificial Intelligence

Why Tsinghua’s Multi‑Intelligence DeepSeek‑R1 Shifts AI from Depth to Width

Tsinghua University and WuWen XinQiong unveil DeepSeek‑R1, a multi‑model AI architecture that prioritizes width over depth, enabling parallel expert models to tackle complex, multi‑format data, addressing single‑model limitations while attracting significant industry investment and posing new engineering challenges.

AI ArchitectureDeepSeek-R1Tsinghua
0 likes · 7 min read
Why Tsinghua’s Multi‑Intelligence DeepSeek‑R1 Shifts AI from Depth to Width