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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

Paper2026-10-30
Development of a Hierarchical Model Surpassing Limitations in Complex Robot Control
Yann LeCun
Paper2026-10-29
A technology for jointly training robot data using diverse controllers
Chelsea Finn
Paper2026-10-29
Proving that artificial intelligence models learn the same world even across different information modalities
Yann LeCun
Paper2026-10-29
Improving Psychiatric Intake AI Performance through Simulated Environment Training
Mark Chen
People All Frontier labsNew labsAcademiaAI hardwareKoreaChinaExecutives
Field All HBM & memoryGPUs & AI acceleratorsInference efficiencyTraining & scalingAgents & codingRobotics & physical AIWorld models & multimodalData centers & powerAI for scienceSafety & policy

Career moves · Academia

Recent career records (from Wikidata; may include unverified information)
  • Kaiming He — Massachusetts Institute of Technology joined 2024-01
  • Kaiming He — Facebook joined 2016 · ended 2024-01
  • Geoffrey Hinton — Google joined 2013 · ended 2023-05-01

Papers · Academia

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
Training & scaling
Full summary Read paper ↗ PDF ↗
Paper2026-10-12 · arXiv (Cornell University)

Development of a Technology for Efficiently Discovering Diverse Antibody Candidates Using Artificial Intelligence

Multi-Objective Amortized CDRH3 Sequence Sampling Diversifies Promising Antibody Candidates
Yoshua Bengio +5 more
What
Researchers have developed a new artificial intelligence-based antibody sequence generation technology that can simultaneously consider multiple conditions and diversity during the antibody design process.
Key result
Compared to existing methods, it generated 10 times more diverse and acceptable antibody candidates at a 50 percent sequence identity.
Why it matters
This research can help reduce the probability of failure in the early stages of new drug development and explore various potential antibody candidates.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-08 · Code Ocean

Technology to Interpret the Causes of Uncertainty in Artificial Intelligence Revealed

pce-uncertainty-attribution: code for "Interpretable attribution of decision-relevant uncertainty in AI"
Yoshua Bengio +6 more
What
Released code and methods to interpret which factors contribute to the decision-making uncertainty of trained artificial intelligence models.
Key result
Utilized polynomial surrogates to uncover the causes of model uncertainty in the fields of chemistry, experimental design, and language model alignment.
Why it matters
Can be utilized to better understand the decision-making process of artificial intelligence models across various fields and increase reliability.AI summary
AI for science Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-10-06 · arXiv (Cornell University)

Development of an algorithm that accelerates AI robot behavior learning

QF3: Fast Flow RL with Filtered Q-Gradients
Pieter Abbeel +9 more
What
Researchers have developed QF3, a new reinforcement learning algorithm that efficiently learns robot behavior patterns.
Key result
It has been demonstrated that it can learn humanoid robot locomotion and motion tracking policies about 10 times faster than existing methods.
Why it matters
It shows the potential to learn and improve robot behavior control technology faster and more efficiently.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-10-06 · arXiv (Cornell University)

Whole-Body Humanoid Loco-Manipulation Technology Utilizing Human Demonstrations

Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data
Pieter Abbeel +6 more
What
Developed a technology that enables humanoid robots to manipulate objects and move by utilizing human demonstration videos through a visual planner and a reinforcement learning tracker.
Key result
Successfully performed complex whole-body manipulation tasks such as box carrying, box grasping, and stepping over a suitcase on a real Unitree G1 robot.
Why it matters
Demonstrates that robots can improve their ability to handle objects while maintaining balance in complex environments based on human behavior data.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-10-04 · arXiv (Cornell University)

Measuring how easily AI comes up with future research ideas

Priced Guidance: Can Language Models Generate Future Research Ideas?
Percy Liang +2 more
What
The study proposes a method to indirectly estimate the likelihood that a language model generates novel research ideas without hints, using the amount of information (compression cost) required to reveal the correct answer.
Key result
Five models were evaluated on the core ideas of 87 recent deep learning papers, and the median compression cost of the best single model was 69.9 bits. This is significantly lower than the median of 5,712 bits for the general compression tool gzip.
Why it matters
This research demonstrates a new evaluation framework that can calculate the lower bound of the probability that a model generates ideas. However, the abstract does not address research productivity or market effects.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-03 · arXiv (Cornell University)

Researchers Propose a Framework for Evaluating AI Answer Oversight via Code Execution

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
Yoshua Bengio +7 more
What
Created the PyINE framework, which utilizes program execution results as ground truth, to examine whether supervisors can trust AI answers that feature plausible yet incomplete reasoning.
Key result
Models trained to follow shortcuts significantly improved their ability to predict execution outcomes, but still made systematic errors when misleading cues conflicted with actual behavior, and cheap learned supervisors frequently missed rare shortcut errors.
Why it matters
Significant for presenting an experimental environment that compares and reuses AI answer verification oversight methods under the same conditions, although the abstract does not mention actual service application effects.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-10-02 · arXiv (Cornell University)

Proposing GOTT, a reusable object grasping technology for multiple robot hands

GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
Pieter Abbeel +6 more
What
To enable multi-fingered robot hands, similar to human hands, to stably grasp and move objects in desired directions, the authors proposed GOTT, a contact technology that can be commonly used across different robots and hand shapes.
Key result
In simulations and real-world robot experiments, GOTT established stable contacts across various objects, arm-hand platforms, and unseen hand shapes, and consistently improved overall task success rates compared to pre-planned grasping methods.
Why it matters
This study demonstrates the potential to reuse the same technology even when robot hand types change by using a common motion component not dependent on a specific robot. However, it is difficult to judge the commercialization level or real industrial impact from the abstract alone.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-10-01 · arXiv (Cornell University)

VISTA: Research on improving multimodal AI agent performance with visual memory

VISTA: A Visual Harness for Reasoning in an Interactive World
Kaiming He +4 more
What
Researchers proposed 'VISTA', a visual harness designed to help multimodal AI models that handle both images and text solve tasks better in interactive visual environments.
Key result
They reported that applying VISTA on ARC-AGI-3 increased the relative human action efficiency score from 40.68 to 100.00, completing all 25 public games with 57.4% fewer actions than the initial participating human.
Why it matters
This demonstrates that adding visual memory and retrieval functions to existing models was effective in specific visual game tasks. However, since the abstract presents only a few benchmark results, it is premature to conclude that it can be applied broadly across actual industrial settings.AI summary
Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-10-01 · arXiv (Cornell University)

An RPG framework where robots improve their own skills through simulation practice

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Pieter Abbeel +9 more
What
Proposed an RPG framework that autonomously improves robotic task systems without additional human intervention.
Key result
In new initial condition tests across 22 manipulation tasks, the success rate increased from 28.6% after the first practice to 95.0% after 15 rounds, and all 30 real-world robot trials succeeded.
Why it matters
This study demonstrates the potential to improve robot control software with less human effort, although actual industrial application or commercialization levels are difficult to judge from the abstract alone.AI summary
Agents & coding Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-09-30 · arXiv (Cornell University)

Researchers Propose TacEx to Enhance Robot Learning Efficiency Through Tactile Curiosity

Tactile Curiosity Drives Robot Interaction
Pieter Abbeel +6 more
What
The researchers proposed TacEx, an exploration framework that allows robots to develop curiosity about tactile sensations (the sense of touching objects) and learn how to touch and pick up objects on their own.
Key result
According to the abstract, TacEx collects interaction data rich in contact without task rewards or expert demonstrations, enables learning of pick-and-place policies using this data without further environment interactions, and improves the post-training performance of vision-language-action models.
Why it matters
Traditional robot learning spends a lot of time moving randomly in the air, demonstrating that exploring with a focus on contact can improve learning efficiency. However, it is difficult to judge the level of practical application or commercialization potential based on the abstract alone.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Pieter Abbeel +8 more
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
Training & scaling Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-09-28

Analysis of the Impact and Policy Responses Triggered by Automating AI R&D

What if automating AI R&D triggers an intelligence explosion?
Jakub Pachocki · Geoffrey Hinton · Yoshua Bengio +19 more
What
Researchers evaluated the possibility of AI R&D automation triggering an intelligence explosion, along with its potential impacts and policy response measures.
Key result
AI systems are expected to automate most AI R&D tasks within a few years, which could lead to rapid capability growth at a level society struggle to handle.
Why it matters
It warns of the extreme risks such as loss of control alongside the benefits that AI R&D automation may bring, urging policy preparedness.AI summary
Safety & policy
Full summary Read paper ↗
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
Training & scaling
Full summary Read paper ↗ PDF ↗
Paper2026-07-20 · Proceedings of the National Academy of Sciences of the United States of America

Analysis of the Necessity and Institutional Challenges of Legal AI Performance Benchmarking

There is no free benchmark: An institutional view of legal AI benchmarking
Christopher D. Manning +5 more
What
Analyzed the necessity of public benchmarking to verify the performance and risks of legal AI tools, along with institutional design challenges.
Key result
Revealed that the legal AI ecosystem lacks information transparency, and that a careful approach is required when designing benchmarking systems to prevent manipulation or distortion.
Why it matters
Suggests that tailored benchmarking strategies aligned with resources and constraints are crucial for the safe and responsible use of AI in the legal field.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-05-06 · arXiv.org

Development of a Platform for Evaluating Security Risks of AI Agents

DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
Percy Liang +16 more
What
Developed a platform and automated attack tools to evaluate the security risks of AI agents in various real-world environments.
Key result
This revealed systematic vulnerability patterns across various AI agents and provided insights into security.
Why it matters
It can be used as useful foundational data for developing next-generation safe AI agents.AI summary
Agents & coding Safety & policy
Full summary Read paper ↗ PDF ↗

People

Frontier labs
Noam ShazeerGoogle Gemini Co-LeadDario AmodeiAnthropic CEOLast activity 2026-10-27Demis HassabisGoogle DeepMind CEOLast activity 2026-10-20Mark ChenOpenAI Chief Research OfficerLast activity 2026-10-29Alexandr WangMeta Superintelligence Labs LeaderLast activity 2026-06-10Jakub PachockiOpenAI Chief ScientistLast activity 2026-09-28Jared KaplanAnthropic Co-founderLast activity 2026-10-09Jeff DeanGoogle Chief ScientistNat FriedmanMeta Superintelligence LabsNoam BrownOpenAIRob FergusHead of Meta FAIRLast activity 2026-10-20Mustafa SuleymanCEO of Microsoft AILast activity 2026-10-22Shane LeggGoogle DeepMind Co-founderLast activity 2026-09-28Jan LeikeAnthropicOriol VinyalsGoogle DeepMindShengjia ZhaoChief Scientist, Meta Superintelligence LabsJason WeiMeta Superintelligence LabsKoray KavukcuogluGoogle Chief AI ArchitectLast activity 2026-07-02Chris OlahAnthropicLast activity 2026-05-28Pushmeet KohliGoogle DeepMind Science AILast activity 2026-10-20
New labs
Mira MuratiCEO of Thinking Machines LabAndrej KarpathyFounder of Eureka LabsYann LeCunFounder of AMI LabsLast activity 2026-10-30Ilya SutskeverCEO of SSILlion JonesSakana AILast activity 2026-10-06Lilian WengCo-founder of Thinking Machines LabBarret ZophFrequent moves (reports of returning to OpenAI in 2026 and moving again)Sergey LevinePhysical Intelligence and UC BerkeleyLast activity 2026-10-06Aravind SrinivasPerplexity CEOArthur MenschMistral AI CEOAshish VaswaniEssential AILast activity 2026-10-28Aidan N. GomezAidan GomezCohere CEOLast activity 2026-09-12John SchulmanChief Scientist, Thinking Machines LabChelsea FinnPhysical Intelligence and Stanford UniversityLast activity 2026-10-29Fei-Fei LiCEO of World Labs and Stanford UniversityLast activity 2026-10-07
Academia
Richard SuttonUniversity of AlbertaStuart RussellUC BerkeleyAndrew Y. NgAndrew NgDeepLearning.AI and AI FundYoshua BengioUniversité de Montréal and LawZeroLast activity 2026-10-12Geoffrey HintonProfessor Emeritus, University of TorontoLast activity 2026-09-28Christopher D. ManningChristopher ManningStanford UniversityLast activity 2026-07-20Percy LiangStanford University and Together AILast activity 2026-10-22Pieter AbbeelUC BerkeleyLast activity 2026-10-06Kaiming HeMITLast activity 2026-10-01
AI hardware
Bill DallyNVIDIA Chief ScientistTri DaoTogether AI and PrincetonLast activity 2026-08-12Song HanMIT and NVIDIALast activity 2026-10-07Andrew L FeldmanAndrew FeldmanCerebras CEOJonathan RossGroqJim KellerTenstorrent CEOJim FanNVIDIA Robotics
Korea
Ha Jung-wooVice Chairman of the Presidential Committee on National AI Strategy (Resigned as Senior Presidential Secretary for AI in 2026)Last activity 2026-04-22Kim Sung-hoonKim Sung HunCEO of UpstageKyunghoon BaeBae KyunghoonMinister of Science and ICT (Former Head of LG AI Research)Alice H. OhOh Alice HaeyunKAISTLee Hae-minSenior Presidential Secretary for AI Future Planning (Appointed in 2026)Kyunghyun ChoCho KyunghyunNYU and GenentechLast activity 2026-10-04
China
Liang WenfengDeepSeek FounderLast activity 2026-10-12Yang ZhilinMoonshot AI FounderLast activity 2026-07-27Kai-Fu Lee01.AI
Executives
Kwak Noh-jungSK hynix CEOMark ZuckerbergMeta CEOSam AltmanOpenAI CEOYoung-Hyun JunJun Young-hyunSamsung Electronics Head of DS DivisionJensen HuangNVIDIA CEOC.C. WeiTSMC Chairman and CEOGreg BrockmanOpenAI PresidentLisa SuAMD CEOLip-Bu TanIntel CEOSatya NadellaMicrosoft CEOSanjay MehrotraMicron CEOMasayoshi SoneMasayoshi SonSoftBank ChairmanSundar PichaiAlphabet CEOAndy JassyAmazon CEOElon MuskxAI and Tesla CEOKevin ScottMicrosoft CTOTan Hock EngHock TanBroadcom CEO

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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