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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

Paper2026-10-30
Development of a Hierarchical Model Surpassing Limitations in Complex Robot Control
Yann LeCun
Paper2026-10-29
A technology for jointly training robot data using diverse controllers
Chelsea Finn
Paper2026-10-29
Proving that artificial intelligence models learn the same world even across different information modalities
Yann LeCun
Paper2026-10-29
Improving Psychiatric Intake AI Performance through Simulated Environment Training
Mark Chen
People All Frontier labsNew labsAcademiaAI hardwareKoreaChinaExecutives
Field All HBM & memoryGPUs & AI acceleratorsInference efficiencyTraining & scalingAgents & codingRobotics & physical AIWorld models & multimodalData centers & powerAI for scienceSafety & policy

Career moves · Inference efficiency

No career moves match these filters yet.

Papers · Inference efficiency

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
Inference efficiency Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-10-11 · arXiv (Cornell University)

Reducing Prediction Errors in AI World Models by Applying Physical Laws

LeHami: Symplectic Latent Dynamics for World Models
Yann LeCun +2 more
What
Researchers developed LeHami, a new world model training method that combines the geometric properties of physical systems with artificial intelligence.
Key result
Unlike conventional approaches, this method prevents information from compressing and disappearing during long-term prediction and significantly improves velocity prediction performance.
Why it matters
This research can contribute to AI understanding and simulating physical spaces more accurately.AI summary
Inference efficiency World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-10-07 · arXiv (Cornell University)

Development of Long Visual Memory Processing Technology for Real-Time Robot Control

Long-WAM: Scaling the Context of World-Action Models
Song Han +15 more
What
Researchers have developed Long-WAM, a model that efficiently utilizes past visual information of robots under real-time constraints.
Key result
When pre-training the foundational model that processes visual information using an autoregressive (AR) method, the utilization of past memory is maximized, and robot task success rates are improved.
Why it matters
It demonstrated that real-time operation is possible in various hardware environments, which can contribute to future complex long-horizon robot control tasks.AI summary
Inference efficiency Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-10-06

OTel releases AI datasets and models dedicated to the telecommunications sector

OTel: Open Telco AI Datasets, Benchmarks, and Models
Ashish Vaswani +17 more
What
Researchers have released 'Open Telco (OTel)', a collection of resources including datasets, evaluation benchmarks, and models trained with them, necessary for AI training dedicated to the telecommunications sector.
Key result
The models based on the released OTel achieved performances of 93.1%, 0.947, and 87.8% in telecommunications data retrieval, document reranking, and language model accuracy evaluation, respectively.
Why it matters
This release provides a reproducible starting point that promotes artificial intelligence research and applications in the telecommunications field.AI summary
Inference efficiency
Full summary Read paper ↗
Paper2026-09-29 · arXiv (Cornell University)

SoL-Refiner Upscales Low-Resolution Videos to 4K Quality in a Single Step

SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video
Song Han +11 more
What
The researchers proposed SoL-Refiner, a refiner model that transforms low-resolution videos into high-resolution ones through a single denoising step.
Key result
At 2K resolution, it achieved higher average scores on VBench and UniPercept than comparable external refiners. At 3840x2176 resolution, it improved upon both metrics compared to the three-step LTX-2.3 refiner. It was reported that under 2K latency settings, refining latency became 8.91 times faster compared to the same baseline model.
Why it matters
High-resolution video generation significantly increases computational load and cost burdens. This approach shows that increasing quality in a single step instead of multiple steps can be a direction to reduce this burden. However, since the results presented in the abstract were measured under conditions set by the authors, they cannot be generalized as overall effects in actual services.AI summary
Inference efficiency
Full summary Read paper ↗ PDF ↗
Paper2026-09-29 · arXiv (Cornell University)

LeapQuant: Accelerating AI Inference by Compressing Linear Attention States to 8 Bits

LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
Song Han +12 more
What
The paper proposes LeapQuant, a training-free method that compresses the recurrent states, which large language models repeatedly read and write when processing long contexts, into low precision.
Key result
According to the abstract, 8-bit compression maintains accuracy comparable to the FP32 baseline while achieving an average speedup of 2.05 to 3.70 times at the GPU kernel level and 1.47 times in overall inference.
Why it matters
This research can contribute to improving operational efficiency for AI services handling long contexts by reducing memory and computational burdens during inference. However, since the abstract only presents results from specific models and GPUs, it is difficult to conclude that the same effects will appear across all actual commercial services.AI summary
Inference efficiency
Full summary Read paper ↗ PDF ↗
Paper2026-09-29 · arXiv (Cornell University)

T2Mem: A Technique for Training Robots to Remember Past Scenes and Perform Tasks

T$^2$Mem: Learning Test-Time Memory for Robotics
Fei-Fei Li +6 more
What
For robot manipulation tasks that require remembering past observation information, we propose a framework (T2Mem) that adds memory capabilities through test-time training to pretrained robot policies that previously lacked memory functions.
Key result
The average success rate across 16 RoboMME tasks increased from 17.93% for the baseline policy without memory to 56.83%, outperforming recurrent memory methods reported in the benchmark.
Why it matters
This is significant in showing that robots' ability to utilize past information not currently visible on screen can be fostered using only demonstration data without external memory devices, though the abstract only covers research-stage results, so commercialization or market effects cannot be confirmed.AI summary
Inference efficiency Robotics & physical AI
Full summary Read paper ↗ PDF ↗
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
Inference efficiency Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-09-28 · arXiv (Cornell University)

Proposal of a Technique to Reduce Speed and Memory Usage in Video Generation AI

Sol-H3: Recursive Self-Improvement for MiniMax-H3 Inference Acceleration on Sol-Engine across Cloud and Edge
Song Han +8 more
What
Created a complete execution pipeline to run the 33-billion parameter video generation AI model MiniMax-H3 faster and with less memory on both cloud servers and small devices.
Key result
According to the abstract, up to a 30x end-to-end speedup and about 20% memory reduction were achieved, generating a 5-second, 1344x768 resolution video with audio 3.5x faster than real-time on 8 GB200 server nodes.
Why it matters
This is significant as it demonstrates a way to lower the generation latency and memory limits that are the biggest obstacles when applying video generation AI to real services, though the commercialization effect and scale of cost reduction are difficult to judge from the abstract alone.AI summary
GPUs & AI accelerators Inference efficiency
Full summary Read paper ↗ PDF ↗
Paper2026-09-28 · arXiv (Cornell University)

Research improving block diffusion language model training by incorporating future context

d-OPD: Future-Aware On-Policy Distillation for Block Diffusion Language Models
Song Han +2 more
What
The authors propose d-OPD, a distillation training method that converts conventional autoregressive language models (which generate one token at a time) into block diffusion language models that generate multiple tokens in parallel.
Key result
Experiments with Qwen3 models (0.6B to 8B) showed that d-OPD improved the average score across six benchmarks by up to 4.0 points compared to the existing OPDLM method, while reducing training time by 1.35 to 1.58 times.
Why it matters
The abstract suggests that generating multiple tokens simultaneously in blocks has the potential to increase text generation speed, and this study demonstrates that the efficiency of the training process for creating such models can be improved.AI summary
Inference efficiency Training & scaling
Full summary Read paper ↗ PDF ↗
Paper2026-09-28 · arXiv (Cornell University)

JEPA research makes autonomous driving world models 100 times faster

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
Yann LeCun +3 more
What
Evaluated predictive world models (JEPA family) used in autonomous driving and proposed an improved architecture, AD-E2E-JEPA, that significantly reduces computational load.
Key result
The proposed architecture reduced the number of patches used for planning by 1/16 and the embedding dimension by 1/4, increasing inference speed by about 100 times while maintaining planning performance.
Why it matters
This research shows a direction for reducing the computational cost of autonomous driving AI, though the actual in-vehicle application effect and commercialization potential are difficult to judge from the abstract alone.AI summary
Inference efficiency Robotics & physical AI World models & multimodal
Full summary Read paper ↗ PDF ↗
Paper2026-09-12

Cohere Unveils Cost-Effective Large Language-Based Translation Model

North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
Aidan N. Gomez +22 more
What
Researchers have introduced 'North Small Translate', a cost-effective large language model-based machine translation model that delivers outstanding performance across 50 languages.
Key result
This model achieved state-of-the-art machine translation performance across 50 languages among model groups with fewer than 1 trillion parameters.
Why it matters
It demonstrates the potential to efficiently perform diverse language translation and post-editing tasks without expensive computations during the inference process.AI summary
Inference efficiency
Full summary Read paper ↗ PDF ↗
Paper2026-08-24

Learning to Act While the Robot Thinks

Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Sergey Levine +19 more
What
Researchers have developed ARLI, a latency-aware reinforcement learning framework that solves problems caused by the thinking time (inference latency) of robot models.
Key result
This new method enables robots to successfully continue learning even in environments with latency, showing performance equal to or better than an ideal latency-free environment.
Why it matters
It opens up the possibility for complex and massive robot AI to autonomously improve performance in real-world fields without freezing or stuttering.AI summary
Inference efficiency Robotics & physical AI
Full summary Read paper ↗ PDF ↗
Paper2026-07-02 · arXiv.org

Google Releases Open-Source AI 'Gemma 4' with Improved Performance and Efficiency

Gemma 4 Technical Report
Demis Hassabis · Koray Kavukcuoglu +298 more
What
Google has developed 'Gemma 4', a next-generation open-source multimodal language model with enhanced computational efficiency and reasoning capabilities.
Key result
Performance has significantly improved across various evaluation benchmarks, including Science, Technology, Engineering, and Mathematics (STEM) and multimodality.
Why it matters
This indicates a broadened range of open-source AI model options capable of solving complex problems with fewer resources.AI summary
Inference efficiency Training & scaling
Full summary Read paper ↗ PDF ↗

People

Frontier labs
Noam ShazeerGoogle Gemini Co-LeadDario AmodeiAnthropic CEOLast activity 2026-10-27Demis HassabisGoogle DeepMind CEOLast activity 2026-10-20Mark ChenOpenAI Chief Research OfficerLast activity 2026-10-29Alexandr WangMeta Superintelligence Labs LeaderLast activity 2026-06-10Jakub PachockiOpenAI Chief ScientistLast activity 2026-09-28Jared KaplanAnthropic Co-founderLast activity 2026-10-09Jeff DeanGoogle Chief ScientistNat FriedmanMeta Superintelligence LabsNoam BrownOpenAIRob FergusHead of Meta FAIRLast activity 2026-10-20Mustafa SuleymanCEO of Microsoft AILast activity 2026-10-22Shane LeggGoogle DeepMind Co-founderLast activity 2026-09-28Jan LeikeAnthropicOriol VinyalsGoogle DeepMindShengjia ZhaoChief Scientist, Meta Superintelligence LabsJason WeiMeta Superintelligence LabsKoray KavukcuogluGoogle Chief AI ArchitectLast activity 2026-07-02Chris OlahAnthropicLast activity 2026-05-28Pushmeet KohliGoogle DeepMind Science AILast activity 2026-10-20
New labs
Mira MuratiCEO of Thinking Machines LabAndrej KarpathyFounder of Eureka LabsYann LeCunFounder of AMI LabsLast activity 2026-10-30Ilya SutskeverCEO of SSILlion JonesSakana AILast activity 2026-10-06Lilian WengCo-founder of Thinking Machines LabBarret ZophFrequent moves (reports of returning to OpenAI in 2026 and moving again)Sergey LevinePhysical Intelligence and UC BerkeleyLast activity 2026-10-06Aravind SrinivasPerplexity CEOArthur MenschMistral AI CEOAshish VaswaniEssential AILast activity 2026-10-28Aidan N. GomezAidan GomezCohere CEOLast activity 2026-09-12John SchulmanChief Scientist, Thinking Machines LabChelsea FinnPhysical Intelligence and Stanford UniversityLast activity 2026-10-29Fei-Fei LiCEO of World Labs and Stanford UniversityLast activity 2026-10-07
Academia
Richard SuttonUniversity of AlbertaStuart RussellUC BerkeleyAndrew Y. NgAndrew NgDeepLearning.AI and AI FundYoshua BengioUniversité de Montréal and LawZeroLast activity 2026-10-12Geoffrey HintonProfessor Emeritus, University of TorontoLast activity 2026-09-28Christopher D. ManningChristopher ManningStanford UniversityLast activity 2026-07-20Percy LiangStanford University and Together AILast activity 2026-10-22Pieter AbbeelUC BerkeleyLast activity 2026-10-06Kaiming HeMITLast activity 2026-10-01
AI hardware
Bill DallyNVIDIA Chief ScientistTri DaoTogether AI and PrincetonLast activity 2026-08-12Song HanMIT and NVIDIALast activity 2026-10-07Andrew L FeldmanAndrew FeldmanCerebras CEOJonathan RossGroqJim KellerTenstorrent CEOJim FanNVIDIA Robotics
Korea
Ha Jung-wooVice Chairman of the Presidential Committee on National AI Strategy (Resigned as Senior Presidential Secretary for AI in 2026)Last activity 2026-04-22Kim Sung-hoonKim Sung HunCEO of UpstageKyunghoon BaeBae KyunghoonMinister of Science and ICT (Former Head of LG AI Research)Alice H. OhOh Alice HaeyunKAISTLee Hae-minSenior Presidential Secretary for AI Future Planning (Appointed in 2026)Kyunghyun ChoCho KyunghyunNYU and GenentechLast activity 2026-10-04
China
Liang WenfengDeepSeek FounderLast activity 2026-10-12Yang ZhilinMoonshot AI FounderLast activity 2026-07-27Kai-Fu Lee01.AI
Executives
Kwak Noh-jungSK hynix CEOMark ZuckerbergMeta CEOSam AltmanOpenAI CEOYoung-Hyun JunJun Young-hyunSamsung Electronics Head of DS DivisionJensen HuangNVIDIA CEOC.C. WeiTSMC Chairman and CEOGreg BrockmanOpenAI PresidentLisa SuAMD CEOLip-Bu TanIntel CEOSatya NadellaMicrosoft CEOSanjay MehrotraMicron CEOMasayoshi SoneMasayoshi SonSoftBank ChairmanSundar PichaiAlphabet CEOAndy JassyAmazon CEOElon MuskxAI and Tesla CEOKevin ScottMicrosoft CTOTan Hock EngHock TanBroadcom CEO

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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