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Last Run 2026-11-03 22:30:03
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S&P 500
7,569
NDX 100
28,180
VIX
20.83
US 10Y
4.95%
DXY
101.87
WTI
$96.76
Gold
$3,940
USD/KRW
1,344
SOX
10,600
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

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

LAST UPDATE 2026-11-04 07:41 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 · New labs · Agents & coding

No career moves match these filters yet.

Papers · New labs · Agents & coding

Paper2026-10-30

Development of a Hierarchical Model Surpassing Limitations in Complex Robot Control

Hierarchical World Models with Implicit Dynamics
Yann LeCun +6 more
What
Researchers developed ID-WM, a hierarchical world model capable of performing complex and long-horizon tasks by separating state accessibility and local control.
Key result
It recorded a 38.5 percent performance improvement compared to the previous state-of-the-art method, and a 135 percent performance improvement compared to single-level world models.
Why it matters
This research demonstrates the possibility for robots to plan long-term while reducing errors, even in complex and novel environments.AI summary
Paper2026-10-28

Development of High-Efficiency AI Models Capable of Complex Tasks

Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI
Ashish Vaswani +499 more
What
Researchers have developed Nemotron 3.5 Super and Lightning, open AI models well-suited for reasoning, long-context understanding, and autonomous task execution.
Key result
The Super model achieved 51.78% on software engineering evaluations while using fewer active parameters, outperforming previous models, and demonstrated high throughput performance through various optimizations.
Why it matters
This research demonstrates the possibility of simultaneously increasing the efficiency and performance of large-scale models so they can be practically utilized in diverse environments.AI summary
Paper2026-10-07

Method developed to predict coding agent performance of base AI models in advance

Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models
What
Developed three evaluation screens utilizing the success paths of post-trained coding agents to evaluate the potential of base AI models.
Key result
All three developed evaluation methods closely predicted the rankings of the open model group to actual performance.
Why it matters
Presents the possibility of utilizing future coding agent benchmarks for base model evaluation.AI summary
Paper2026-10-06 · arXiv (Cornell University)

Clarifying the Relationship between Environmental Control and Structural Centrality in AI Agents

The Geometry of Empowerment
What
Researchers revealed the relationship between empowerment, which refers to an AI agent's ability to control its surrounding environment, and structural centralization, which grants broad access to future outcomes.
Key result
They proposed a new geometric structure capable of interpreting and analyzing this by connecting empowerment maximization with skill-learning methods.
Why it matters
Provides theoretical implications necessary for building scalable empowerment maximization methods.AI summary
Paper2026-10-04 · arXiv (Cornell University)

AI Model 'SheetSage2' Released for Converting Music to Sheet Music

SheetSage2: Coherent Lead-Sheet Transcription with Synthetic Supervision
Yann LeCun +6 more
What
Presented SheetSage2, an integrated music transcription model that automatically converts audio recordings into human-readable sheet music (lead-sheets).
Key result
The single model SheetSage2-AR outperformed existing listed systems in 12 out of 15 benchmark and metric pairs evaluated, showed significant improvements over its predecessor SheetSage1, and even surpassed task-specific dedicated models in some benchmarks.
Why it matters
This research improves the accuracy of music transcription technology, and the model weights and inference code have been released to be utilized for follow-up research or applications. However, it is difficult to judge commercialization potential or market size based on the abstract alone.AI summary
Paper2026-10-01 · arXiv (Cornell University)

A 'Finding Inspiring Papers' Benchmark Created by 184 Researchers

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
Chelsea Finn +13 more
What
We created ScholarCatalyst, a dataset where first authors of 207 recent computer science papers indicated which prior papers actually helped their research, and presented a retrieval task to find them.
Key result
Agentic search (AI that iteratively performs tasks on its own) did not show better performance than basic search methods (recall of 0.42 versus 0.48 based on the top 20 results). Agents based on Claude Fable 5.1 only reached 0.51.
Why it matters
This indicates that current AI still lacks the expert intuition needed to identify necessary prior ideas from vast research literature, pointing to the need for new training methods. While this can be viewed as a step forward for AI assisting scientific research, actual commercialization or industrial effects cannot be confirmed from the abstract.AI summary
Paper2026-09-28 · arXiv (Cornell University)

DexAgent automatically generates robot hand motion training data from human demonstration videos

DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
Fei-Fei Li +4 more
What
We propose an agentic framework that takes a single first-person human video and a task description to generate physically feasible robot motion trajectories for robot learning data.
Key result
Policies trained on data generated by DexAgent across 11 real-world tasks showed a 3.5 times higher success rate compared to baseline methods.
Why it matters
It demonstrates the potential to automate the process of converting human demonstration videos into robot learning data, though it is difficult to judge the industrial application level or commercialization timeline from the abstract alone.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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