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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
Oct 4, 2026 · Artificial Intelligence

Cut Codex Costs: Assign Strong Model for Planning, Cheap Models for Execution & Review

This tutorial explains Codex's internal model call chain—planning, execution, review, and context compression—and shows how to configure separate models for each role via config.toml and worker.toml, using a powerful model for planning while delegating coding and review to cheaper alternatives, permanently reducing per-task API costs.

AI coding assistantCodexOpenAI
0 likes · 6 min read
Cut Codex Costs: Assign Strong Model for Planning, Cheap Models for Execution & Review
Data Party THU
Data Party THU
Oct 3, 2026 · Artificial Intelligence

Agentic Visual Generation: A Five-Level Hierarchy of Controller Decision-Making

This survey introduces a five-level hierarchy (L0–L4) for agentic visual generation systems, classifying them by how far the controller can influence generation decisions—from fixed support to cross-task experience reuse—and proposes a level-conditioned evaluation protocol to isolate causal contributions of control scope.

L0-L4 taxonomyagentic visual generationcontroller hierarchy
0 likes · 35 min read
Agentic Visual Generation: A Five-Level Hierarchy of Controller Decision-Making
Data Party THU
Data Party THU
Oct 2, 2026 · Artificial Intelligence

Recursive Self-Improvement in AI: Survey Distinguishes Evolution Stages and Proposes Unified Framework

This survey paper distinguishes four stages of AI self-improvement—Evolution, Self-Evolution, Meta-Evolution, and Recursive Self-Improvement (RSI)—and introduces a Proposal→Feedback→Optimization framework to analyze how AI systems generate, verify, and retain improvements across cycles, highlighting key challenges like reliability, persistence, and controllability.

AI agentsAI alignmentAI safety
0 likes · 23 min read
Recursive Self-Improvement in AI: Survey Distinguishes Evolution Stages and Proposes Unified Framework
Data Party THU
Data Party THU
Oct 1, 2026 · Artificial Intelligence

Stanford & Tsinghua Find Two Neuron Types Encoding Reward in LLMs

Researchers from Stanford and Tsinghua identify sparse value and dopamine neurons in LLMs that encode state value and temporal difference errors, forming a causal reward subsystem enabling confidence estimation and process reward modeling for improved reasoning.

LLM interpretabilityconfidence estimationdopamine neurons
0 likes · 7 min read
Stanford & Tsinghua Find Two Neuron Types Encoding Reward in LLMs
Data Party THU
Data Party THU
Sep 29, 2026 · Industry Insights

Agility's Digit Humanoids Deploy in Warehouses, Target $2.5B SPAC IPO

Agility Robotics' Digit humanoid robots are already moving boxes in warehouses for customers like Amazon and Spanx, using learning-from-demonstration and large-scale simulation training to achieve adaptive manipulation, with the company claiming 20% lower operating costs than human labor over five years and preparing a $2.5 billion SPAC listing.

Agility RoboticsDigitHumanoid Robots
0 likes · 7 min read
Agility's Digit Humanoids Deploy in Warehouses, Target $2.5B SPAC IPO
Data Party THU
Data Party THU
Sep 27, 2026 · Artificial Intelligence

Diffusion Model Attention Heatmaps: Unlocking Interpretability in Text-to-Image Generation

This article introduces three complementary attention heatmap visualizations — image-to-text, text-to-image, and image-to-image — for interpreting text-to-image diffusion models, detailing aggregated attention computation, bilinear interpolation rendering, and an interactive Flask web service to diagnose prompt adherence and model biases.

Flask web serviceSelf-Attentionattention visualization
0 likes · 12 min read
Diffusion Model Attention Heatmaps: Unlocking Interpretability in Text-to-Image Generation
Data Party THU
Data Party THU
Sep 25, 2026 · Artificial Intelligence

The Last AI Built by Humans? A Five-Level Roadmap to Recursive Self-Improvement

This article analyzes a paper proposing a five-level autonomy framework for recursive self-improvement (RSI) in AI, distinguishing true RSI from mere automation, reviewing applications in science, robotics, software engineering, and healthcare, and surveying industrial practices from Theseus, Lark, and others, while highlighting challenges in evaluation, safety, and governance.

AI Autonomy FrameworkAI agentsAI governance
0 likes · 24 min read
The Last AI Built by Humans? A Five-Level Roadmap to Recursive Self-Improvement