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Data Party THU
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Data Party THU

Official platform of Tsinghua Big Data Research Center, sharing the team's latest research, teaching updates, and big data news.

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Recent Articles

Latest from Data Party THU

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Data Party THU
Data Party THU
Jul 10, 2026 · Artificial Intelligence

2026 Big Data Challenge: Award Winners Revealed with In‑Depth Competition Experience Shares (Phase 2)

The article announces the winning teams of the 2026 China University Computer Competition Big Data Challenge and provides detailed, step‑by‑step experience reports covering data processing, feature engineering, model design, training strategies, and post‑processing for a cross‑sectional stock ranking task.

LightGBMTime SeriesXGBoost
0 likes · 10 min read
2026 Big Data Challenge: Award Winners Revealed with In‑Depth Competition Experience Shares (Phase 2)
Data Party THU
Data Party THU
Jul 9, 2026 · Artificial Intelligence

Major Embodied Intelligence Datasets Shaping Robot Engineering Development

The article surveys the leading embodied‑intelligence datasets—including ALOHA, Open X‑Embodiment, AGIBOT WORLD, RH20T and RoboMIND—detailing their technical features, scale, multimodal content, and how they support model training, benchmark evaluation, and practical robot development pipelines.

ALOHABenchmarkingMultimodal Data
0 likes · 14 min read
Major Embodied Intelligence Datasets Shaping Robot Engineering Development
Data Party THU
Data Party THU
Jul 9, 2026 · Big Data

Big Data Challenge 2026: Monthly Star Winners Announced with Winning Teams’ Experience Shares (Third Edition)

The 2026 China University Computer Competition Big Data Challenge announced its Monthly Star winners, and the top teams detailed their data preparation, StockTransformer and LightGBM modeling pipelines, feature engineering, validation strategies, ensemble techniques, and key lessons learned from the competition.

Big Data CompetitionCross-Stock AttentionEnsemble Modeling
0 likes · 8 min read
Big Data Challenge 2026: Monthly Star Winners Announced with Winning Teams’ Experience Shares (Third Edition)
Data Party THU
Data Party THU
Jul 8, 2026 · Artificial Intelligence

Closing the Legal Judgment Gap: Evidence‑Based Fact Prediction (LFP) & LFPBench

This article surveys the shortcomings of current legal judgment prediction, proposes the evidence‑driven Legal Fact Prediction (LFP) paradigm, details the construction of the LFPBench benchmark, and presents extensive experiments with GPT‑4o, Claude 3.5 Sonnet and domain‑specific LLMs that cut average accuracy loss by 38.5%, while also examining ethical concerns, bias, and future research challenges.

EthicsLFPBenchLLM evaluation
0 likes · 11 min read
Closing the Legal Judgment Gap: Evidence‑Based Fact Prediction (LFP) & LFPBench
Data Party THU
Data Party THU
Jul 8, 2026 · Artificial Intelligence

How AI‑CURA Uses Large Language Models to Automate ACMG Variant Classification

AI‑CURA, an LLM‑driven workflow developed by the Hong Kong Genome Institute, automates 13 ACMG rules without literature and leverages DeepSeek‑R1 and o3‑mini‑high to interpret the remaining seven literature‑dependent rules, achieving up to 99.3% diagnostic agreement and markedly speeding rare‑disease genetic analysis.

ACMGAI-CURADeepSeek
0 likes · 7 min read
How AI‑CURA Uses Large Language Models to Automate ACMG Variant Classification
Data Party THU
Data Party THU
Jul 7, 2026 · Artificial Intelligence

Parallel Decoding for Large Language Models: Balancing Inference Speed and Sampling Diversity in E‑GRM

The article presents an engineering analysis of the E‑GRM framework, detailing how parallel decoding, a temperature‑ladder sampling strategy, and batch‑parallel KV‑Cache sharing achieve low‑latency, high‑diversity inference while preserving consensus‑driven routing accuracy.

Batch ParallelismConsensus RoutingE‑GRM
0 likes · 14 min read
Parallel Decoding for Large Language Models: Balancing Inference Speed and Sampling Diversity in E‑GRM
Data Party THU
Data Party THU
Jul 7, 2026 · Artificial Intelligence

Beyond Vector Retrieval: Building a Multi‑Strategy RAG Agent with LangGraph

This article explains how to use LangGraph to create a hybrid RAG agent that dynamically selects between vector, graph, web, or direct LLM retrieval, detailing the router, grader, rewriter, generator, and hallucination‑checking components along with a complete Python implementation.

Hybrid AgentLLMLangGraph
0 likes · 16 min read
Beyond Vector Retrieval: Building a Multi‑Strategy RAG Agent with LangGraph
Data Party THU
Data Party THU
Jul 6, 2026 · Artificial Intelligence

3D Scene Graphs: Open Challenges and Future Directions

This review systematically surveys 3D Scene Graph research from 2019‑2026, defining their structure, construction pipelines, applications, evaluation protocols, and highlighting open challenges such as unified definitions, dynamic modeling, functional affordances, and fragmented benchmarks that hinder real‑world deployment.

3D Scene GraphsDynamic ModelingRepresentation Learning
0 likes · 15 min read
3D Scene Graphs: Open Challenges and Future Directions
Data Party THU
Data Party THU
Jul 6, 2026 · Artificial Intelligence

Why Even a 10× Smarter AI Scientist Won’t Speed Up Science: The 300‑Year‑Old Paper Bottleneck

The article argues that despite rapid advances in AI scientists—automating literature review, hypothesis generation, experimentation, and writing—their impact on scientific speed is limited by a three‑century‑old research protocol, peer‑review bottlenecks, and incentive misalignments, which can only be overcome by redesigning the research artifact itself.

AI for ScienceAgent‑Native Research ArtifactArtificial Intelligence
0 likes · 12 min read
Why Even a 10× Smarter AI Scientist Won’t Speed Up Science: The 300‑Year‑Old Paper Bottleneck