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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
May 12, 2026 · Artificial Intelligence

MathForge: Leveraging Hard Problems in RL to Boost Large‑Model Mathematical Reasoning (ICLR 2026)

MathForge tackles the long‑standing question of which math problems deserve focus in reinforcement‑learning‑based training, introducing a difficulty‑aware optimizer (DGPO) and multi‑aspect question reformulation (MQR) that together prioritize harder‑but‑learnable questions, yielding consistent performance gains across model sizes and modalities.

DGPODifficulty‑Aware OptimizationMQR
0 likes · 11 min read
MathForge: Leveraging Hard Problems in RL to Boost Large‑Model Mathematical Reasoning (ICLR 2026)
Data Party THU
Data Party THU
May 12, 2026 · Artificial Intelligence

Time Series Large Models Explained: What They Are and Why They Matter

The article introduces time‑series data, its ubiquitous examples, the challenges of traditional small models, and proposes a universal time‑series large model that simplifies data preparation and model building, ultimately enabling more efficient and stable industrial AI solutions, now offered as a cloud service.

AIARIMACRISP-DM
0 likes · 6 min read
Time Series Large Models Explained: What They Are and Why They Matter
Data Party THU
Data Party THU
May 11, 2026 · Artificial Intelligence

How a 1930‑Era AI Model Without Any Computer Knowledge Learned to Write Python

The talkie‑1930‑13b language model, trained exclusively on English texts published before 1931, surprisingly understands historical events, solves Python coding problems, and exhibits scaling‑law behavior, prompting a detailed comparison with its modern twin talkie‑web‑13b and an analysis of training pipelines, memory categories, and common deployment pitfalls.

AI memoryLLMPython code generation
0 likes · 10 min read
How a 1930‑Era AI Model Without Any Computer Knowledge Learned to Write Python
Data Party THU
Data Party THU
May 10, 2026 · Artificial Intelligence

SpikingBrain 2.0 Breaks Long‑Sequence and Low‑Power Bottlenecks in Brain‑Inspired LLMs

The Chinese Academy of Sciences unveils SpikingBrain 2.0‑5B, a brain‑inspired large model that uses dual‑space sparse attention and dual activation (FP8 and INT8‑Spiking) to cut training cost by over tenfold, achieve up to 15× speedup on long sequences, and match Qwen‑3 performance while drastically reducing power consumption.

Large Language ModelSparse AttentionSpikingBrain2.0
0 likes · 10 min read
SpikingBrain 2.0 Breaks Long‑Sequence and Low‑Power Bottlenecks in Brain‑Inspired LLMs
Data Party THU
Data Party THU
May 9, 2026 · Artificial Intelligence

NOSE: Enabling AI to Smell with a Unified Molecule‑Receptor‑Semantic Tri‑modal Representation

NOSE introduces a neural olfactory‑semantic embedding that unifies molecular structure, receptor sequences, and natural‑language odor descriptions into a continuous space, achieving state‑of‑the‑art results on eleven tasks and strong zero‑shot generalization for odor and receptor retrieval.

contrastive learningdeep learningmolecular design
0 likes · 8 min read
NOSE: Enabling AI to Smell with a Unified Molecule‑Receptor‑Semantic Tri‑modal Representation
Data Party THU
Data Party THU
May 8, 2026 · Backend Development

Stop Using print for Logs: In‑Depth Comparison of Python’s Three Major Logging Solutions

After a chaotic production incident, this article compares Python’s built‑in logging, Loguru, and Logfire, detailing their configurations, strengths, weaknesses, and real‑world use cases—from simple scripts to high‑throughput APIs—while offering migration steps and common pitfalls to help you choose the right solution.

Backend DevelopmentLogfireLoguru
0 likes · 17 min read
Stop Using print for Logs: In‑Depth Comparison of Python’s Three Major Logging Solutions
Data Party THU
Data Party THU
May 7, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Building a Multi‑Agent Trading System for End‑to‑End Intelligent Decisions

This article walks through constructing a multi‑agent trading platform—analysts, researchers, traders, risk managers, and a portfolio manager—using LangChain, LangGraph, and LLMs (gpt‑4o, gpt‑4o‑mini), with real‑time data tools, shared and long‑term memory, ReAct loops, structured debates, and a final executable trade proposal.

ChromaDBFinancial AILLM
0 likes · 46 min read
Step‑by‑Step Guide to Building a Multi‑Agent Trading System for End‑to‑End Intelligent Decisions
Data Party THU
Data Party THU
May 6, 2026 · Backend Development

How a Python Generic Repository Cuts 80% of Duplicate CRUD Code

The article demonstrates building a type‑safe, reusable generic repository with Python generics and SQLAlchemy, showing how to replace repetitive CRUD implementations across multiple FastAPI entities, reduce code size from hundreds of lines to a few dozen, and avoid common pitfalls such as missing rollbacks.

FastAPIGenericsPython
0 likes · 14 min read
How a Python Generic Repository Cuts 80% of Duplicate CRUD Code
Data Party THU
Data Party THU
May 6, 2026 · Artificial Intelligence

When AI Seems Obedient, Hidden Alignment Risks Surface

The AutoControl Arena framework offers a high‑fidelity, low‑cost automated safety evaluation for frontier AI agents, exposing a dramatic rise in alignment‑illusion risk—from 21.7% under low pressure to 54.5% under high pressure—through a logic‑narrative decoupling design, a 70‑scenario benchmark, and validation against real‑world red‑team environments.

AI safetyAutoControl Arenaalignment illusion
0 likes · 9 min read
When AI Seems Obedient, Hidden Alignment Risks Surface