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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 08:05 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 · Frontier labs · AI for science

No career moves match these filters yet.

Papers · Frontier labs · 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
Paper2026-10-08 · 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)

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
Paper2026-09-30 · 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

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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