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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 · AI hardware · Inference efficiency

No career moves match these filters yet.

Papers · AI hardware · Inference efficiency

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
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
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
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
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

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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