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Dobot, CAS institutes' MoE paper accepted by EMNLP 2026

A research paper co-authored by Dobot, the Institute of Computing Technology of the Chinese Academy of Sciences, the Nanjing College of the University of Chinese Academy of Sciences, the Nanjing Information High-Speed Rail Research Institute, and two other CAS-affiliated schools has been accepted for publication at the Findings of EMNLP 2026, the top-tier international conference on computational linguistics and natural language processing, which will take place in Budapest from 24–29 October. The study, titled “Meta-Learning Where to Allocate Experts: Task-Conditioned Layer-Wise Compression for MoEs,” proposes a novel compression method for mixture-of-experts models, marking a collaborative research achievement among the Chinese academic and industrial partners.

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  • Building data base, Maniformer joins robotics standards push

    During the 2026 China International Big Data Industry Expo, the Shanghai Robotics Industrial Technology Research Institute, together with Maniformer and other industry leaders, issued a joint initiative on data quality and standards for the robotics sector, aiming to improve data quality, refine industry norms, and foster a shared ecosystem. The initiative targets key challenges in embodied intelligence and humanoid robotics, rallying consensus around three pillars: establishing unified data standards, sharing compliant high-quality data resources, and driving innovative commercial applications.

  • Dobot, CAS institutes' MoE paper accepted by EMNLP 2026

    A research paper co-authored by Dobot, the Institute of Computing Technology of the Chinese Academy of Sciences, the Nanjing College of the University of Chinese Academy of Sciences, the Nanjing Information High-Speed Rail Research Institute, and two other CAS-affiliated schools has been accepted for publication at the Findings of EMNLP 2026, the top-tier international conference on computational linguistics and natural language processing, which will take place in Budapest from 24–29 October. The study, titled “Meta-Learning Where to Allocate Experts: Task-Conditioned Layer-Wise Compression for MoEs,” proposes a novel compression method for mixture-of-experts models, marking a collaborative research achievement among the Chinese academic and industrial partners.

  • OpenAI unveils GPT‑6 Astra, claiming arrival of the AGI era with record‑breaking benchmarks

    OpenAI officially launched GPT‑6 Astra in the early hours of Wednesday, a name derived from the Latin word for “star”, following its prior teaser line “The stars are almost aligned”. At the close of the launch event, OpenAI president Greg Brockman stated “Welcome to the AGI era”, an unusually bold claim rarely voiced by an AI developer for its product. The new model delivers sweeping gains across leading evaluation benchmarks.

  • Gongzhi Marine wraps nearly 100‑mln‑yuan angel round for underwater robots

    Suzhou Gongzhi Marine Equipment Technology Co., Ltd., a developer of underwater robotics, has closed an angel financing round of nearly 100 million yuan with joint investment from Zhongxin Zhidi, Lihe Finance, Yinfeng Capital and HEU Asset Management. The proceeds will fund product iteration and mass production, order delivery and market expansion, R&D and patent deployment, plus recruitment and development of top‑tier technical specialists and management talent.

  • Maniformer hits 20k MEgo units, 1m hrs of data in industry first

    Maniformer on Aug 31 rolled out its 20,000th MEgo untethered data‑collection unit, the industry’s first mass‑production milestone, while also surpassing 1 million hours of high‑quality untethered data, making it the first data‑service provider to commercially sell such a dataset at that scale.

  • Ant's Zero-WAM robot beats baseline by 29.5% on unseen tasks

    Ant's Lingbo Lab launches Zero-WAM robotics model, which learns tasks from human demonstration videos without task-specific training. It hit 46.95% average success on 7 unseen RoboTwin 2.0 tasks, beating baseline LingBot-VA by 29.5 percentage points. Dataset HumanGen pairs 74,200 human videos with robot trajectories across 8,600 tasks.

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