Base Date 2026-11-03
Last Run 2026-11-03 21:00:25
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35.4%
S&P 500
7,569
NDX 100
28,180
VIX
20.82
US 10Y
4.95%
DXY
101.86
WTI
$96.60
Gold
$3,932
USD/KRW
1,344
SOX
10,599
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

AI Leaders: Moves & Papers — latest job changes and research

LAST UPDATE 2026-11-03 08:00 KST PEOPLE 77 · PAPERS 76 · MOVES 0
Where the key people at AI labs, big tech and universities move, and what they publish, are early signals of where technology and competitive advantage are heading. Career moves and papers are collected automatically every day, checked by the editor and summarized in plain language with links to the originals.

Highlights

Career moves · Training & scaling

No career moves match these filters yet.

Papers · Training & scaling

Paper2026-10-29 · arXiv (Cornell University)

Proving that artificial intelligence models learn the same world even across different information modalities

Proving and Improving the Platonic Representation Hypothesis with JEPAs
Yann LeCun +1 more
What
Researchers proved the hypothesis that artificial intelligence models trained on different input modalities converge to the same world representation into a mathematical theorem and proposed a method to improve it.
Key result
It was confirmed that models using a good perspective can align representations much more efficiently with significantly less data than the next-token prediction method.
Why it matters
This study can contribute to improving how artificial intelligence efficiently understands data and connects different information modalities.AI summary
Paper2026-10-22

Discovered the laws for improving performance using artificial intelligence-generated synthetic data

Rewriting Scaling Laws for Synthetic Data with Compute and Data Constraints
Percy Liang +6 more
What
When using synthetic rewrites where language models rewrite natural language documents into different styles for pre-training, we studied the laws for predicting the optimal ratio and performance under compute and data constraints.
Key result
As compute increases, the optimal proportion of synthetic data increases, and models trained on synthetic data generated by larger or equally sized models showed superior performance compared to models using only original data.
Why it matters
This suggests that AI training methods utilizing synthetic data are predictable, enabling the overcoming of data limitations and efficient performance improvement.AI summary
Paper2026-10-04 · arXiv (Cornell University)

New method proposed to train AI models with a balance of multiple values

Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution
What
Researchers proposed a training method called MAESTRO that enables AI language models to adjust the balance among multiple values, such as helpfulness, harmlessness, and humor, according to user requests.
Key result
According to the abstract, MAESTRO achieved the best trade-off curves (Pareto fronts) in most tasks within a single training run in experiments handling up to three objectives, and had the lowest training cost among the compared methods.
Why it matters
While this shows the potential for companies to build AI services tailored to individual user settings more efficiently if a single model can handle multiple value combinations, the actual market impact is difficult to judge from the abstract alone.AI summary
Paper2026-09-29 · arXiv (Cornell University)

Scaling humanoid object carrying learning by expanding a few real videos with AI

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
What
Proposed a method (PRISM) where AI generates various virtual videos based on a few real videos of a person carrying objects, and uses them for robot learning.
Key result
Reports that a single control policy trained this way successfully enabled a real humanoid robot to pick up, transport, and place boxes, drums, kegs, and balls without additional real-world learning.
Why it matters
Significant in that it demonstrates the potential to increase training data using a few real videos in situations where collecting large amounts of real robot demonstration data is difficult.AI summary
Paper2026-09-28 · arXiv (Cornell University)

Research improving block diffusion language model training by incorporating future context

d-OPD: Future-Aware On-Policy Distillation for Block Diffusion Language Models
Song Han +2 more
What
The authors propose d-OPD, a distillation training method that converts conventional autoregressive language models (which generate one token at a time) into block diffusion language models that generate multiple tokens in parallel.
Key result
Experiments with Qwen3 models (0.6B to 8B) showed that d-OPD improved the average score across six benchmarks by up to 4.0 points compared to the existing OPDLM method, while reducing training time by 1.35 to 1.58 times.
Why it matters
The abstract suggests that generating multiple tokens simultaneously in blocks has the potential to increase text generation speed, and this study demonstrates that the efficiency of the training process for creating such models can be improved.AI summary
Paper2026-09-10

Analysis of Overfitting in Mixture-of-Experts Models When Repeating AI Training Data

Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data
Percy Liang +4 more
What
It was revealed that when AI training data is repeated, the recently widely used Mixture-of-Experts architecture degrades in performance faster than conventional models.
Key result
When data is repeated, Mixture-of-Experts models easily overfit, and while existing regularization techniques can partially mitigate this, they fall short of the performance achieved by training on unique data.
Why it matters
This demonstrates the necessity of establishing training strategies that consider the characteristics of each AI model architecture in situations where iterative learning is required due to data scarcity.AI summary
Paper2026-08-12

Developed 'Dion3', a new optimization technology that increases AI training speed by up to 6x

Dion3: Full-Stack Orthogonal Updates
Tri Dao +7 more
What
Developed 'Dion3', which improves the entire system to solve the high computation and communication cost problems of the existing AI training tool Muon.
Key result
Dion3 reduced the optimization step duration by up to 6 times while maintaining performance equal to or better than Muon.
Why it matters
It can contribute to reducing related computation costs by increasing the efficiency of the AI model training process.AI summary
Paper2026-07-27

Open-source AI model 'Kimi K3' released

Kimi K3: Open Frontier Intelligence
Yang Zhilin +399 more
What
Researchers have developed and released 'Kimi K3', an open AI model with 2.8 trillion parameters.
Key result
This model showed about 2.5 times improved scaling efficiency compared to existing models and recorded outstanding performance in various evaluations.
Why it matters
By releasing cutting-edge AI technology in an open-source format, it is expected to promote future related research and industrial applications.AI summary
Paper2026-07-02 · arXiv.org

Google Releases Open-Source AI 'Gemma 4' with Improved Performance and Efficiency

Gemma 4 Technical Report
What
Google has developed 'Gemma 4', a next-generation open-source multimodal language model with enhanced computational efficiency and reasoning capabilities.
Key result
Performance has significantly improved across various evaluation benchmarks, including Science, Technology, Engineering, and Mathematics (STEM) and multimodality.
Why it matters
This indicates a broadened range of open-source AI model options capable of solving complex problems with fewer resources.AI summary
Paper2026-05-27 · arXiv.org

Development of a Diagnostic Tool to Track the Transition Between Memorization and Generalization in Artificial Intelligence Training

A Training-Time Diagnostic for Generalization via the Log-Alignment Ratio
What
We researched a diagnostic method that tracks whether artificial intelligence is simply memorizing data or actually learning by utilizing the Log-Alignment Ratio (LAR), which measures parameters and activations.
Key result
LAR effectively captured changes in the generalization capability of artificial intelligence and signs of overfitting even without additional validation data.
Why it matters
Since it can diagnose AI performance during the training process without separate validation data, it can contribute to the future development of efficient model training management technologies.AI summary

People

Why follow AI leaders' moves and papers?

In AI, people are the technology. Which company a key researcher joins, or which lab they found, often turns into products, funding rounds and acquisitions months later. This page tracks job changes across frontier labs, new labs, academia, AI chips, and people in Korea and China.

Papers show what a person is focused on right now. We sort papers into 10 investable fields such as HBM & memory, GPUs & accelerators, inference efficiency, agents and robotics, and give a three-line summary — what, key result, why it matters — based on the abstract, with links to the original and PDF. This is information for finding ideas, not a recommendation to buy or sell any security.

Frequently Asked Questions

Q. Who do you track?

Key researchers and executives at frontier labs (OpenAI, Google DeepMind, Anthropic, Meta and others), new labs, academia, AI chip companies, and in Korea and China. Executives who rarely publish are tracked for career moves only.

Q. Where does the data come from, and how often is it updated?

Papers come from Semantic Scholar and arXiv, and careers from Wikidata (CC0) plus official announcements and press reports, collected automatically every day. Only items checked and approved by the editor are published.

Q. Who writes the paper summaries?

An AI (Google Gemini) drafts each summary from the paper's abstract, and the editor checks it before publishing. Other languages are AI translations of the checked Korean summary. Always confirm exact figures and context in the original paper.

This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.

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