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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 · New labs

Recent career records (from Wikidata; may include unverified information)

Papers · New labs

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
Agents & coding World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-10-29 · arXiv (Cornell University)

A technology for jointly training robot data using diverse controllers

Cross-Controller Transfer via In-Context Behavior Adaptation
Chelsea Finn +2 more
What
Developed a technology to utilize data from robots using different control methods for learning without conflicts.
Key result
The average success rate improved by 33 percentage points compared to conventional methods.
Why it matters
Demonstrates the potential to more effectively utilize robot data collected from diverse environments.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Training & scaling
Full summary Read paper ↗ PDF ↗
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
Inference efficiency Agents & coding
Full summary Read paper ↗ PDF ↗
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
Inference efficiency World models & multimodal
Full summary Read paper ↗ PDF ↗
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
Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-10-07 · arXiv (Cornell University)

AI technology that transforms virtual robot videos into realistic ones

RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer
Fei-Fei Li +8 more
What
Developed a technology that enhances robot control capabilities by converting robot data from virtual environments into videos that look like the real world.
Key result
Robots trained with this technology achieved an average success rate of 71 percent in real-world environments, showing superior performance compared to existing methods.
Why it matters
This can help alleviate the visual problem where data created in virtual environments was difficult to directly apply to real robots.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Ashish Vaswani +21 more
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
Agents & coding
Full summary Read paper ↗
Paper2026-10-06 · arXiv (Cornell University)

Development of a new artificial neural network architecture separating short-term and long-term memory

Continuous Memory Machines
Llion Jones +5 more
What
Researchers have developed a new artificial neural network architecture called Continuous Memory Machines (CMM) that separates matrix-shaped short-term and long-term memory states.
Key result
It showed superior performance and generalization capabilities compared to existing models in algorithmic and reasoning tasks.
Why it matters
It can contribute to improving the memory and reasoning capabilities of artificial intelligence across various tasks.AI summary
HBM & memory
Full summary Read paper ↗ PDF ↗
Paper2026-10-06 · arXiv (Cornell University)

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

The Geometry of Empowerment
Sergey Levine +3 more
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
Agents & coding
Full summary Read paper ↗ PDF ↗
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
Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-10-06

OTel releases AI datasets and models dedicated to the telecommunications sector

OTel: Open Telco AI Datasets, Benchmarks, and Models
Ashish Vaswani +17 more
What
Researchers have released 'Open Telco (OTel)', a collection of resources including datasets, evaluation benchmarks, and models trained with them, necessary for AI training dedicated to the telecommunications sector.
Key result
The models based on the released OTel achieved performances of 93.1%, 0.947, and 87.8% in telecommunications data retrieval, document reranking, and language model accuracy evaluation, respectively.
Why it matters
This release provides a reproducible starting point that promotes artificial intelligence research and applications in the telecommunications field.AI summary
Inference efficiency
Full summary Read paper ↗
Paper2026-10-05 · arXiv (Cornell University)

Development of a new benchmark to study the phenomenon of AI behavioral cloning technology

Behavioral Cloning Mystery
Sergey Levine +1 more
What
Proposed OCBench, a robot manipulation evaluation tool with characteristics similar to human demonstrations.
Key result
Showed that this tool enables scientific research on various phenomena related to behavioral cloning and hypothesis testing in graphics processing unit (GPU) based environments.
Why it matters
Provides a foundation for analyzing previously unclear phenomena in robot learning technology within a controlled environment.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-10-05 · arXiv (Cornell University)

A Performance-Guided Data Collection Method to Enhance Robot Learning Efficiency

Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Chelsea Finn +6 more
What
Researchers proposed Mulligan, a technique that improves efficiency in robot manipulation learning by collecting data centered around failed and untried states.
Key result
The Mulligan technique improved the robot's final task success rate from 10 percent to 34 percent compared to conventional methods.
Why it matters
It demonstrates a technological advancement that helps human trainers use time and effort more efficiently when teaching robots.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
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
Agents & coding
Full summary Read paper ↗ PDF ↗
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
Agents & coding AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-09-29 · arXiv (Cornell University)

T2Mem: A Technique for Training Robots to Remember Past Scenes and Perform Tasks

T$^2$Mem: Learning Test-Time Memory for Robotics
Fei-Fei Li +6 more
What
For robot manipulation tasks that require remembering past observation information, we propose a framework (T2Mem) that adds memory capabilities through test-time training to pretrained robot policies that previously lacked memory functions.
Key result
The average success rate across 16 RoboMME tasks increased from 17.93% for the baseline policy without memory to 56.83%, outperforming recurrent memory methods reported in the benchmark.
Why it matters
This is significant in showing that robots' ability to utilize past information not currently visible on screen can be fostered using only demonstration data without external memory devices, though the abstract only covers research-stage results, so commercialization or market effects cannot be confirmed.AI summary
Inference efficiency Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Agents & coding Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Inference efficiency Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-09-12

Cohere Unveils Cost-Effective Large Language-Based Translation Model

North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
Aidan N. Gomez +22 more
What
Researchers have introduced 'North Small Translate', a cost-effective large language model-based machine translation model that delivers outstanding performance across 50 languages.
Key result
This model achieved state-of-the-art machine translation performance across 50 languages among model groups with fewer than 1 trillion parameters.
Why it matters
It demonstrates the potential to efficiently perform diverse language translation and post-editing tasks without expensive computations during the inference process.AI summary
Inference efficiency
Full summary Read paper ↗ PDF ↗
Paper2026-08-24

Learning to Act While the Robot Thinks

Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Sergey Levine +19 more
What
Researchers have developed ARLI, a latency-aware reinforcement learning framework that solves problems caused by the thinking time (inference latency) of robot models.
Key result
This new method enables robots to successfully continue learning even in environments with latency, showing performance equal to or better than an ideal latency-free environment.
Why it matters
It opens up the possibility for complex and massive robot AI to autonomously improve performance in real-world fields without freezing or stuttering.AI summary
Inference efficiency Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-08-17

Release of Open Source Tools and Performance Comparison Platform for Relational Learning

Advancing Open and Reproducible Relational Learning: RelArena-$\alpha$, TabPFN-Rel and RPI
Yann LeCun +46 more
What
Researchers have open-sourced an integrated comparison platform and two related softwares to promote research in the relational learning field.
Key result
Among the newly released tools, TabPFN-Rel, a model for relational databases, recorded the highest rank on the comparison platform.
Why it matters
This release can serve as a foundation to establish reliable comparison standards in relational learning and assist its application in research and industrial settings.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-08-03

Uncovering Why Action Chunking Improves Performance in Robotic Control

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Sergey Levine +5 more
What
Through experiments, the study analyzed the causes of performance improvement in 'action chunking', a technique where robotic control predicts and executes multiple actions at once.
Key result
It showed that existing hypotheses are incorrect, revealed that the core success factor of action chunking is the 'implicit ensembling' effect which learns various temporal relationships, and proposed a new policy utilizing this.
Why it matters
By uncovering the key factors that enhance the performance of robotic control technology, it provides a theoretical foundation for developing efficient robot learning models in the future.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-06-11 · arXiv.org

Development of a new technology to more accurately guide robot behavior

Improving Robotic Generalist Policies via Flow Reversal Steering
Sergey Levine +4 more
What
To improve the performance of robotic generalist policies trained on diverse robot datasets, a method called Flow Reversal Steering (FRS), which transforms suboptimal actions into better ones, was evaluated in control settings.
Key result
Using FRS can translate rough instructions from humans or vision-language models into good robot behavior, showing an absolute task success rate improvement of up to 95% with less than 1 minute of training.
Why it matters
It demonstrates the potential to help robots perform better in new tasks by utilizing human instructions or existing techniques.AI summary
Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Ashish Vaswani +1 more
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
Training & scaling
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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