Official platform of Tsinghua Big Data Research Center, sharing the team's latest research, teaching updates, and big data news.
The article surveys recent observations from leading mathematicians who report that AI breakthroughs—ranging from solving most IMO problems in 2025 to accelerating research with systems like AlphaEvolve—signal a decisive turning point in how mathematics is explored, proved, and taught.
The article analyzes MiniMax’s strategy of co‑evolving large language models with a Harness framework, contrasting product philosophies, detailing a live MaxHermes demo that creates and refines reusable Skills, and explaining how this dual evolution reshapes the competitive focus from single‑turn Q&A to sustained, self‑improving agent workflows.
The article analyzes silent failures in Retrieval‑Augmented Generation pipelines, identifies three gaps—retrieval relevance, LLM confidence masking uncertainty, and missing fault signals—and presents a practical feedback‑loop architecture with relevance gating, post‑generation evaluation, session tracing, and user‑signal logging to make production RAG systems trustworthy.
Researchers from Shanghai Jiao Tong University introduce the Meta‑Encoder, a unified integration framework that dynamically combines multiple pathological foundation models, achieving superior cancer detection performance across diverse tasks and datasets while maintaining low computational cost.
The 9th Tsinghua Big Data Intelligent Lecture gathered leading scholars and industry experts to showcase cutting‑edge research on multimodal perception, embodied intelligence, spatial AI, large‑model multimodal systems, and industrial time‑series databases, emphasizing their technical depth and real‑world impact.
These two recent papers—Microsoft’s M⋆, which evolves task‑specific memory harnesses, and Google’s AutoHarness, which automatically generates code‑level constraints—demonstrate reflective code evolution and tree‑search synthesis, achieving state‑of‑the‑art performance across diverse benchmarks and outlining LLM post‑training directions for 2025‑2026.
OpenAI introduced GPT‑Rosalind, a purpose‑built reasoning model for biology, drug discovery and translational medicine that streamlines evidence synthesis, hypothesis generation and experiment planning, and demonstrates leading performance on benchmarks such as BixBench and LABBench2 while offering free plugins that connect to over fifty scientific tools and data sources.
This guide presents a step‑by‑step roadmap for becoming an Agentic AI engineer in 2026, covering Python fundamentals, LLM concepts, framework selection, advanced memory management, tool integration, production deployment, and interview preparation with concrete examples and best‑practice recommendations.
LARYBench, the first large‑scale benchmark for embodied intelligence, quantifies implicit action representations across 1.2 million video clips, evaluates vision‑only and robot‑specific models, and reveals how general visual encoders can close the vision‑action modality gap.
The 2026 AI Index report shows a dramatic shift toward industry‑driven AI breakthroughs, widening US‑China gaps, soaring carbon footprints of large models, narrowing performance gaps among top systems, booming AI investment, and growing societal concerns about responsible AI and its impact on jobs, education, and public perception.
