Official platform of Tsinghua Big Data Research Center, sharing the team's latest research, teaching updates, and big data news.
The article explains how to construct a simple radio telescope using household materials, capture the 21‑cm hydrogen line, analyze Doppler‑shifted spectra to measure cloud velocities at various galactic radii, and demonstrate the flat rotation curve that signals the presence of dark matter.
The article explains how simple character‑ or page‑based chunking destroys the spatial and semantic relationships of tables, figures, formulas and headings in PDFs, proposes a structure‑aware multimodal RAG pipeline that restores layout via layout detection, visual description generation, modal enhancement and cross‑encoder re‑ranking, and shows that these steps dramatically improve retrieval quality, especially for visual queries.
The article reviews Ziheng Chen’s PhD thesis on Riemannian Deep Learning, outlining a three‑layer framework that unifies manifold‑aware modules, geometry‑specific network designs, and learnable Riemannian metrics, and discusses theoretical foundations, batch normalization, classification heads, specialized networks, and extensive experiments across vision, signal, and graph domains.
The article explains why traditional robot log formats struggle with self‑organizing behaviors, compares the mainstream standards MCAP, ROS Bag 2.0 and ULG, and evaluates four visualization tools—PlotJuggler, Roboto, robot‑log‑visualizer and WandB—showing how they support efficient recording, storage, and analysis of multimodal robot data.
The article reviews how AI systems—from OpenAI’s internal models to DeepMind’s agents—have begun solving longstanding Erdős problems, detailing specific breakthroughs, the iterative LLM methodology, and the emerging paradigm of AI‑augmented pure mathematics research.
The article outlines China’s strategic roadmap for developing general artificial intelligence, reviewing its historical milestones, economic and societal benefits, current challenges, and a five‑point framework that emphasizes strategic guidance, independent innovation, coordinated governance, agile risk management, and open‑source collaboration.
The article analyzes the limitations of a shared memory pool in large‑language‑model multi‑agent systems and presents DecentMem, a decentralized dual‑pool architecture with an online router that balances exploitation and exploration, achieving up to 23.8% higher accuracy, 49% token reduction, and 2.5× faster evolution across several benchmarks.
OpenAI's report examines eight agent‑assisted scientific‑computing projects, showing how coding agents can rewrite legacy tools like STAR in Rust with near‑perfect result consistency, accelerate workloads, and highlight the need for human validation, iterative feedback, and sustainable long‑term maintenance.
Domain-Adaptive Diffusion Policy (DADP) decouples representation learning and injects domain information into the diffusion process, enabling robots to adapt across varying friction, mass, and dynamics, achieving strong zero-shot performance on MuJoCo and Adroit benchmarks, especially in out-of-distribution scenarios.
The article reviews Coleman Hooper's 2026 Berkeley PhD thesis, which shows that LLM inference is increasingly limited by memory bandwidth and capacity, and proposes a four‑pronged approach—weight quantization, KV‑cache quantization, selective context loading, and multipole attention—to dramatically improve memory efficiency and throughput.
