Base Date 2026-11-03
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 07:41 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 · Agents & coding

No career moves match these filters yet.

Papers · Frontier labs · Agents & coding

Paper2026-10-29 · arXiv (Cornell University)

Improving Psychiatric Intake AI Performance through Simulated Environment Training

Training language models in simulation for psychiatric intake
Mark Chen +55 more
What
Researchers demonstrated that language models trained through reinforcement learning in a simulated environment significantly improve structured text-based psychiatric initial intake capabilities.
Key result
In evaluations involving psychiatrists, the trained artificial intelligence outperformed the base model across multiple metrics, including clinical score, patient-centered communication, and differential diagnosis appropriateness.
Why it matters
This shows that simulated environments can serve as a useful testbed for training conversational artificial intelligence in psychiatry and verifying its safety.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
Paper2026-10-06 · arXiv (Cornell University)

A new method for evaluating the self-improvement capabilities of AI agents through experience

Agent Plasticity: Measuring Self-Improvement Through Experience
Rob Fergus +10 more
What
Introduced the concept of agent plasticity and studied evaluation methods to measure the self-improvement efficiency of AI agents that evolve on their own through experience.
Key result
Despite similar learning opportunities, state-of-the-art models showed significant differences in their improvement trajectories, and the agent with the best final performance did not necessarily improve the most efficiently.
Why it matters
Demonstrates that when evaluating the potential of artificial intelligence, it is important to measure not only fixed-point capabilities but also how effectively they improve through experience.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
Paper2026-09-29 · arXiv (Cornell University)

Proposed meta-reasoning technique for AI agents to determine 'how to proceed' before performing tasks

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
Rob Fergus +11 more
What
Proposed an 'agentic meta-reasoning' approach that systematically treats execution control decisions—such as how much to reuse previous task results, whether to start fresh, and when to stop—for AI agents performing long-running tasks.
Key result
On the ProgramBench program reconstruction benchmark, the GPT-5.5-based version recorded 71.5%, which is higher than Codex's 58.0%, and the Opus 4.8-based version recorded 67.2%, higher than Claude Code's 65.5%.
Why it matters
According to the abstract, this suggests that as agents perform longer tasks, allocating computational resources to structured execution control may become more important. However, since the results in the abstract were obtained under specific benchmarks and experimental conditions, it is difficult to conclude that they directly translate to real-world product performance.AI summary
Paper2026-06-10 · arXiv.org

Development of an Evaluation Method to Pinpoint Errors in AI Agents

Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness
What
Researchers have developed an evaluation system that divides the structure of AI agents into multiple layers to independently and rapidly inspect errors in each layer.
Key result
They confirmed that even when the overall success rate barely changes, the score of a specific troubled layer drops significantly, enabling accurate identification of the root cause.
Why it matters
This can contribute to enhancing the stability of AI systems and quickly finding the cause when problems occur.AI summary
Paper2026-06-09 · arXiv.org

Analysis of Limitations in Conversational Agent Evaluation by LLM-as-Judge

Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents
What
Using production food-ordering conversational agents, we measured how effectively LLM-based judges detect actual defects compared to comprehensive human inspection.
Key result
LLM judges failed to catch even a quarter of the systematic issues identified by humans, and in certain batches recorded zero detections.
Why it matters
This suggests that automated evaluation methods serve merely as a minimum baseline and cannot completely replace human review.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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