Base Date 2026-11-03
Last Run 2026-11-03 21:00:25
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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 · Agents & coding

No career moves match these filters yet.

Papers · Agents & coding

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
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
Paper2026-10-28

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
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
Paper2026-10-07

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
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
Paper2026-10-06 · arXiv (Cornell University)

Humanize technique to improve the reliability of AI coding

Humanize: Judgement Engineering for Agentic Coding
Song Han +8 more
What
Introduced Humanize, a multi-AI collaborative workflow that applies clear, mechanical decisions between the planning, implementation, and review stages.
Key result
Production AIs and review AIs from different providers worked in alternating turns, achieving high performance in competitive problem-solving and performance evaluation.
Why it matters
Demonstrates that independent reviews help catch errors in AI-assisted programming processes.AI summary
Paper2026-10-06 · arXiv (Cornell University)

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

The Geometry of Empowerment
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
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)

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
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
Paper2026-10-01 · arXiv (Cornell University)

VISTA: Research on improving multimodal AI agent performance with visual memory

VISTA: A Visual Harness for Reasoning in an Interactive World
Kaiming He +4 more
What
Researchers proposed 'VISTA', a visual harness designed to help multimodal AI models that handle both images and text solve tasks better in interactive visual environments.
Key result
They reported that applying VISTA on ARC-AGI-3 increased the relative human action efficiency score from 40.68 to 100.00, completing all 25 public games with 57.4% fewer actions than the initial participating human.
Why it matters
This demonstrates that adding visual memory and retrieval functions to existing models was effective in specific visual game tasks. However, since the abstract presents only a few benchmark results, it is premature to conclude that it can be applied broadly across actual industrial settings.AI summary
Paper2026-10-01 · arXiv (Cornell University)

An RPG framework where robots improve their own skills through simulation practice

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
What
Proposed an RPG framework that autonomously improves robotic task systems without additional human intervention.
Key result
In new initial condition tests across 22 manipulation tasks, the success rate increased from 28.6% after the first practice to 95.0% after 15 rounds, and all 30 real-world robot trials succeeded.
Why it matters
This study demonstrates the potential to improve robot control software with less human effort, although actual industrial application or commercialization levels are difficult to judge from the abstract alone.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
Paper2026-09-29 · 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
Paper2026-09-28 · 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
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
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.

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