Base Date 2026-11-03
Last Run 2026-11-03 22:30:03
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%
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 · Academia · Safety & policy

No career moves match these filters yet.

Papers · Academia · Safety & policy

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
Paper2026-10-03 · 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
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
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?
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
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
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
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

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.

🐩 Support Kobong's Treats
코봉이
If this page was helpful, feel free to support Kobong's treat fund too 🦴
💬 Community Comments
Anonymous nicknames based on IP · Abuse may result in restrictions
Loading...