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Last Run 2026-11-03 21:00:25
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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 · World models & multimodal

No career moves match these filters yet.

Papers · World models & multimodal

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
Paper2026-10-11 · arXiv (Cornell University)

Reducing Prediction Errors in AI World Models by Applying Physical Laws

LeHami: Symplectic Latent Dynamics for World Models
Yann LeCun +2 more
What
Researchers developed LeHami, a new world model training method that combines the geometric properties of physical systems with artificial intelligence.
Key result
Unlike conventional approaches, this method prevents information from compressing and disappearing during long-term prediction and significantly improves velocity prediction performance.
Why it matters
This research can contribute to AI understanding and simulating physical spaces more accurately.AI summary
Paper2026-10-07 · arXiv (Cornell University)

Development of a World Model Integrating Various Robot Behaviors into One

Cross-Embodiment Robot Foundation World Models with Latent Actions
Fei-Fei Li +11 more
What
Developed a robot world model utilizing an integrated latent action space that can be shared by various types of robots.
Key result
When applied to new robots, it showed up to a 46.7% improvement in precise manipulation tasks and an 11.7% improvement in LIBERO benchmark performance compared to conventional methods.
Why it matters
Enables efficient learning across diverse robots, contributing to solving major challenges in robotics.AI summary
Paper2026-10-06 · arXiv (Cornell University)

Open Framework Combining Robot Control and Future Prediction Released

OpenWAM: An Open Framework for Composable World-Action Models
Fei-Fei Li +9 more
What
Developed OpenWAM, an open-source framework for comparing and researching world-action models that handle robot control and future video prediction together.
Key result
The proposed method significantly increased the success rate of robotic manipulation tasks and improved the performance of dynamics models through counterfactual data training.
Why it matters
It provides a common foundation for comparing various robot control and video prediction methods, thereby increasing the efficiency of related technology research.AI summary
Paper2026-10-05 · arXiv (Cornell University)

Development of H-JEPA, an AI model that learns hierarchical structures for complex visual planning

H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
Yann LeCun +4 more
What
Developed H-JEPA, a hierarchical world model that learns by dividing abstraction levels according to time horizons.
Key result
In simulation environments and real robot video experiments, planning success rates and computational efficiency improved compared to existing single-level models.
Why it matters
It can contribute to increasing efficiency in robot control and visual tasks that require complex long-term planning.AI summary
Paper2026-09-28 · The International Journal of Robotics Research

A comprehensive review summarizing 'world model' research for robot learning

World model for robot learning: A comprehensive survey
Pieter Abbeel +17 more
What
This is a survey paper that systematically organizes 'world models'—models that predict how the environment will change in response to actions—from the perspective of robot learning.
Key result
According to the abstract, it outlines how world models are used in robot policies, simulators, evaluation, and data generation, summarizes the evolutionary trajectory of robot video-based world models, and highlights representative datasets, benchmarks, and evaluation methods.
Why it matters
This serves as a resource that unifies scattered robot learning research approaches into a single framework, helping to grasp the research trends. However, the abstract does not prove the performance or commercial achievements of individual technologies.AI summary
Paper2026-09-28 · arXiv (Cornell University)

JEPA research makes autonomous driving world models 100 times faster

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
Yann LeCun +3 more
What
Evaluated predictive world models (JEPA family) used in autonomous driving and proposed an improved architecture, AD-E2E-JEPA, that significantly reduces computational load.
Key result
The proposed architecture reduced the number of patches used for planning by 1/16 and the embedding dimension by 1/4, increasing inference speed by about 100 times while maintaining planning performance.
Why it matters
This research shows a direction for reducing the computational cost of autonomous driving AI, though the actual in-vehicle application effect and commercialization potential are difficult to judge 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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