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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 · AI for science

No career moves match these filters yet.

Papers · AI for science

Paper2026-10-22 · Science

Artificial intelligence achieved higher accuracy than doctors in iterative diagnosis processes

Sequential diagnosis with language models
What
Researchers developed a large language model based AI system and an interactive evaluation benchmark that mimic the clinical diagnosis process.
Key result
This AI system achieved accuracies of 83.2 percent and 80.3 percent respectively in medical case evaluations, showing superior performance compared to general practitioners.
Why it matters
Although in a controlled environment, this demonstrates the potential for language models to support more accurate and cost-effective diagnoses.AI summary
Paper2026-10-22 · BMJ digital health & AI

Definition and Evaluation Criteria for Medical Superintelligence in Artificial Intelligence Proposed

Medical superintelligence: foundations and aspirations
What
Discussed how artificial intelligence technology contributes to more effective medical services and introduced six interdependent dimensions of medical intelligence.
Key result
Revealed that improving healthcare quality requires not only outstanding performance in individual tasks, but also systems that collaborate with humans to perform integrated medical reasoning throughout the entire care process.
Why it matters
Proposed a direction to combine these capabilities with existing medical intelligence to enhance the quality and accessibility of medical services.AI summary
Paper2026-10-20 · Zenodo (CERN European Organization for Nuclear Research)

Release of Structural Data Used in AI-Based Enzyme Design Paper

Supplementary Data 2. Designed and predicted structures of all designs
What
The research team has released all structural data files covered in the paper on AI-driven enzyme design for novel chemical reactions.
Key result
The compressed file contains structures generated by the AI model, redesigned structures, and additional predicted structural data.
Why it matters
The released data allows related researchers to verify the results of AI-based enzyme design and conduct further research.AI summary
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
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
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"
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
Paper2026-10-08 · Science

Automatically proving unsolved math problems with AI

Advancing mathematics research with AI-driven formal proof search
What
We developed an AI agent that searches for mathematical proofs at scale by utilizing a language where a compiler verifies the proof steps.
Key result
This AI agent successfully and autonomously solved several unsolved math problems and conjectures.
Why it matters
It demonstrated the potential for AI to drive autonomous mathematical discoveries.AI summary
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
Paper2026-10-04 · arXiv (Cornell University)

Proposal of a method where multiple AI agents stack work in a Git repository to solve problems

GitSwarm: Decentralized Compounding Inference
Rob Fergus +13 more
What
Proposed and evaluated GitSwarm, a 'compounding inference' system where multiple identical AI agents work independently in a shared Git repository, continuously saving, referencing, and combining intermediate results.
Key result
Reported solving all 30 math proof problems in a single run, outperforming the strongest baseline under a fixed budget (65.1 points) with an average of 79.4 points on the programming benchmark ProgramBench, and improving starting architectures through iterative experiments across three neural network structure tasks.
Why it matters
Demonstrates that AI can accumulate intermediate achievements rather than discarding results after each attempt, reusing them for subsequent tasks, thereby suggesting a direction for long-horizon problem solving and AI design for scientific research. However, since these are results at the abstract level, actual industrial impact is difficult to judge from this material alone.AI summary
Paper2026-10-02 · bioRxiv (Cold Spring Harbor Laboratory)

New enzyme chemistry technology combining artificial intelligence protein design and evolution

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry
What
Developed an enzyme that performs carbon-hydrogen amination reactions not found in nature by combining artificial intelligence-based protein design and evolution techniques.
Key result
Succeeded in synthesizing a new enzyme with programmable positional and enantioselectivity, high catalytic efficiency, and superior enzyme stability.
Why it matters
Demonstrates that protein design technology can be utilized to create enzymes that promote demanding chemical reactions.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
Paper2026-09-30 · Nature

Development of Technology to Invisibly Mark the Origin of AI-Generated Proteins

Function-preserving watermarking of AI-generated proteins.
What
Researchers have developed 'SynthIDBio', a technology that leaves marks on AI-generated protein sequences and structures without loss of function.
Key result
It has been demonstrated that watermarks can be detected with near-perfect accuracy while maintaining protein functions similar to existing ones.
Why it matters
It demonstrates the potential of provenance tracking tools in the field of AI-driven bioengineering to address biosecurity and information authenticity issues.AI summary
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
Paper2026-07-25

Derivation of ideal mathematical measures via many-body quantum states

$\mathcal{P}(\Phi)_2$ Theory from many-body quantum Gibbs states
Yang Zhilin +2 more
What
Derived measures on the two-dimensional unit torus as rigorous limits of many-body quantum Gibbs states.
Key result
Systematically organized lower-order interactions arising from the Wick renormalization process using a diagrammatic formalism to handle higher-order interactions.
Why it matters
This study provides a fundamental analytical method connecting quantum statistical mechanics models and probabilistic mathematical theory.AI summary
Paper2026-05-31 · arXiv.org

Physics AI successfully predicts laboratory turbulence

Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Kyunghyun Cho +22 more
What
Applied a physics foundation model trained on simulations to a real laboratory environment to predict complex fluid instability phenomena.
Key result
Successfully predicted actual laboratory growth ranges and new physical environments that were absent from the training data.
Why it matters
Demonstrates the potential for AI-based models to extend beyond simulations to real experimental data.AI summary
Paper2026-04-27 · arXiv.org

Development of an AI model handling diverse biomolecular information together

MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Kyunghyun Cho +30 more
What
Researchers have introduced MIMIC, a generative artificial intelligence model capable of simultaneously learning and generating multiple biological types of information such as nucleic acids, proteins, evolution, and structures.
Key result
By utilizing multiple data modalities together, MIMIC demonstrated improved performance over existing single-data-based models and performed various biological tasks.
Why it matters
This study demonstrates the potential for biomolecular representation learning, conditional prediction, and customized molecular design through a single unified model.AI summary
Paper2026-04-22 · Clinical Endoscopy

Real-time artificial intelligence improved the gastric lesion detection rate in endoscopy

Impact of real-time artificial intelligence integration on detection of gastric lesions: an exploratory single-center before-and-after study using low-definition routine endoscopy
Ha Jung-woo +10 more
What
The researchers evaluated the effects of introducing a real-time artificial intelligence (AI) detection system for low-definition endoscopy exams performed in resource-limited settings.
Key result
After the introduction of artificial intelligence, the overall lesion detection rate per patient increased slightly, the detection rate of small lesions of 0.5 cm or less also increased, and the biopsy rate decreased.
Why it matters
This technology can be utilized to help detect small gastric lesions and reduce unnecessary biopsies without compromising cancer detection, even in resource-limited settings.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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