We are still recruiting for closed beta testing, with a maximum reward of ¥200.

Real-world experiment sharing from HyperAI users

"DeepSeek-OCR 2: Visual Causal Flow" is now available in the "Tutorials" section of the HyperAI website. Simply upload your image to get accurate OCR text parsing. Give it a try!

The Polymathic AI research team has proposed Walrus, a fundamental model based on the Transformer architecture and primarily geared towards fluid-like continuum dynamics. Walrus covers 19 highly diverse physical scenarios during its pre-training phase, encompassing multiple fields including astrophysics, earth sciences, rheology, plasma physics, acoustics, and classical fluid dynamics. Results show that Walrus outperforms previous foundational models in both short-term and long-term predictions for downstream tasks.

2.2-2.6 Selected AI Papers

Inspired by the new model of DeepSeek, the Genos team, composed of researchers from BGI Genomics and Zhejiang Zhijiang Laboratory, has launched a dedicated "plug-in" for genome modeling—Gengram (Genomic Engram). With only about 20 million parameters, it has broken the state-of-the-art (SOTA) records for multiple genome tasks, providing a revolutionary solution to overcome the bottleneck of genome modeling.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from January 26th to 30th, covering multiple fields such as intelligent agents, computer vision, and TTS.

Currently, large-scale, multi-band, wide-field-of-view, and high-depth sky surveys are propelling astronomy into an unprecedented data-intensive era. With the commissioning of next-generation facilities such as the Euclid Space Telescope, the Rubin Observatory, and the Roman Space Telescope, the universe is being systematically mapped on an unprecedented scale and with unprecedented precision. These observations are expected to [...]

Selected AI Papers (January 26-30)

Robotics startup Skild AI has raised $1.4 billion in Series C funding, valuing the company at over $14 billion. The round was led by Japan's SoftBank Group, with participation from strategic investors including Nvidia's NVentures, Macquarie Capital, and Bezos Expeditions (founded by Amazon founder Jeff Bezos). Samsung, LG, Schneider Electric, and Salesforce Ventures also participated.

A joint research team comprised of Basecamp Research, NVIDIA, and several top academic institutions has jointly constructed the EDEN series of metagenomic basic models. By learning from massive amounts of natural evolutionary data that cross species and are related to environmental information, they have for the first time systematically extracted the deep "grammar" and universal principles of biological design.

The "TRELLIS.2 3D Generation Demo" is now available on the HyperAI website (hyper.ai) in the "Tutorials" section. Come and experience the efficient 3D model generation!

A research team at the University of California designed a special photon trapping texture (PTST) structure on the surface of a standard silicon photodiode and introduced a highly noise-resistant fully connected neural network. This network can intelligently calculate and reconstruct the original spectrum directly from the photocurrent signal measured by the device. This method not only enables the spectrometer to achieve a higher signal-to-noise ratio at longer wavelengths, but its overall performance also surpasses that of traditional silicon-based spectrometers.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from version 1.12 to 1.16, covering multiple fields such as intelligent agents, computer vision, and TTS.

Selected AI Papers (January 19-23)

This article systematically compiles a batch of high-quality datasets, online tutorials, and papers related to embodied intelligence, providing a reference for further learning and research. Welcome to visit hyper.ai to explore more high-quality resources!

A research team from Goethe University in Germany has classified the "human E3 ligase genome" using metric learning, integrating multi-level data including protein sequences, domain composition, three-dimensional structure, function, and expression patterns. This method expands the traditional classification of E3 enzymes (RING, HECT, and RBR classes) to include atypical mechanisms, successfully elucidates functional partitions, distinguishes between multi-subunit complexes and monomeric enzymes, and maps E3 enzymes to substrates and potential drug targets.

A research team at Yale University recently proposed the MOSAIC model, which transforms a generalized big language model into a collaborative system composed of numerous specialized chemistry experts. Through professional division of labor, it effectively suppresses model illusion, provides quantifiable uncertainty assessment, and realizes the systematic generation from reaction description to complete experimental scheme. It is expected to substantially improve scientific research efficiency in fields such as drug discovery and materials development.

The "GLM-Image Precise Semantic High-Fidelity Image Generation Model" is now available on the HyperAI website (hyper.ai) tutorial section. Come and unleash your boundless creativity!

A research team from Tsinghua University and the University of Chicago systematically assessed the real impact of AI tools on scientific research using a massive dataset of 41.3 million natural science papers and 5.37 million scientists from 1980 to 2025. The study found that while AI significantly enhances individual research output and academic influence, it leads to a contraction of knowledge space and a concentration of academic interaction at the collective level. By identifying AI research through language models and introducing innovation indicators such as "knowledge breadth," the paper reveals the overlooked structural costs behind AI for Science, providing crucial evidence for understanding how AI is reshaping the research ecosystem.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from version 1.12 to 1.16, covering multiple fields such as intelligent agents, computer vision, and TTS.

"Qwen-Image-2512: Generating More Realistic Portraits and Natural Landscapes" is now available on the HyperAI website (hyper.ai) tutorial section. Come and unleash your boundless creativity!

A joint research team from Princeton University and the Colorado School of Mines has proposed an efficient prediction method based on machine learning. This method uses a large language model to directly predict the free energy from the structural sequence of MOFs, thereby significantly reducing computational costs and enabling high-throughput, scalable thermodynamic assessment of MOFs.

To further refine HyperAI's product experience and core capabilities, we are officially launching a new round of internal testing. We hope to invite a select group of real users to experience the platform's capabilities and contribute to polishing product details. 💻 If you have a long-term need for cloud platforms and GPU computing power, 🙋‍♀️ if you have a technical background [...]

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from December 8th to 12th, covering multiple fields such as intelligent agents, computer vision, and TTS. (Dates and representative fields will be adjusted according to actual circumstances.)

In 2025, the narrative logic of the AI industry is undergoing a violent dismantling. With hundreds of billions of dollars in capital expenditures (CapEx) colliding with sluggish revenue growth, the seats for judging whether it's a "bubble" are already full. From Alphabet's evaporated $200 billion market capitalization to ChatGPT's staggering loss black hole, technology seems to be forced to bow to pragmatism.

CleaveNet, an AI-based end-to-end design process proposed by a joint team from MIT and Harvard University, is designed to address this challenge. This process, through the collaborative work of predictive and generative models, aims to revolutionize the existing paradigm of protease substrate design, providing entirely new solutions for related basic research and biomedical development.

The "HY-MT1.5-1.8B: Multilingual Neural Machine Translation Model" is now available on the HyperAI website (hyper.ai) in the tutorial section. Come and experience its lightning-fast translation capabilities!

The 8th Meet AI Compiler technical salon in 2025 successfully concluded on December 27th at the Shanghai Innovation Academy.

Founded in 2023, FieldAI, an embodied intelligence company, has raised over $405 million in less than two years, with investors including Jeff Bezos, Intel, Nvidia, Bill Gates, and Samsung. Its core team members come from leading companies such as NASA JPL, DeepMind, Tesla, and SpaceX, and the company is dedicated to creating a "universal robotic intelligent brain" capable of working across different types of robots and adapting to various environments.

We are still recruiting for closed beta testing, with a maximum reward of ¥200.

Real-world experiment sharing from HyperAI users

"DeepSeek-OCR 2: Visual Causal Flow" is now available in the "Tutorials" section of the HyperAI website. Simply upload your image to get accurate OCR text parsing. Give it a try!

The Polymathic AI research team has proposed Walrus, a fundamental model based on the Transformer architecture and primarily geared towards fluid-like continuum dynamics. Walrus covers 19 highly diverse physical scenarios during its pre-training phase, encompassing multiple fields including astrophysics, earth sciences, rheology, plasma physics, acoustics, and classical fluid dynamics. Results show that Walrus outperforms previous foundational models in both short-term and long-term predictions for downstream tasks.

2.2-2.6 Selected AI Papers

Inspired by the new model of DeepSeek, the Genos team, composed of researchers from BGI Genomics and Zhejiang Zhijiang Laboratory, has launched a dedicated "plug-in" for genome modeling—Gengram (Genomic Engram). With only about 20 million parameters, it has broken the state-of-the-art (SOTA) records for multiple genome tasks, providing a revolutionary solution to overcome the bottleneck of genome modeling.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from January 26th to 30th, covering multiple fields such as intelligent agents, computer vision, and TTS.

Currently, large-scale, multi-band, wide-field-of-view, and high-depth sky surveys are propelling astronomy into an unprecedented data-intensive era. With the commissioning of next-generation facilities such as the Euclid Space Telescope, the Rubin Observatory, and the Roman Space Telescope, the universe is being systematically mapped on an unprecedented scale and with unprecedented precision. These observations are expected to [...]

Selected AI Papers (January 26-30)

Robotics startup Skild AI has raised $1.4 billion in Series C funding, valuing the company at over $14 billion. The round was led by Japan's SoftBank Group, with participation from strategic investors including Nvidia's NVentures, Macquarie Capital, and Bezos Expeditions (founded by Amazon founder Jeff Bezos). Samsung, LG, Schneider Electric, and Salesforce Ventures also participated.

A joint research team comprised of Basecamp Research, NVIDIA, and several top academic institutions has jointly constructed the EDEN series of metagenomic basic models. By learning from massive amounts of natural evolutionary data that cross species and are related to environmental information, they have for the first time systematically extracted the deep "grammar" and universal principles of biological design.

The "TRELLIS.2 3D Generation Demo" is now available on the HyperAI website (hyper.ai) in the "Tutorials" section. Come and experience the efficient 3D model generation!

A research team at the University of California designed a special photon trapping texture (PTST) structure on the surface of a standard silicon photodiode and introduced a highly noise-resistant fully connected neural network. This network can intelligently calculate and reconstruct the original spectrum directly from the photocurrent signal measured by the device. This method not only enables the spectrometer to achieve a higher signal-to-noise ratio at longer wavelengths, but its overall performance also surpasses that of traditional silicon-based spectrometers.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from version 1.12 to 1.16, covering multiple fields such as intelligent agents, computer vision, and TTS.

Selected AI Papers (January 19-23)

This article systematically compiles a batch of high-quality datasets, online tutorials, and papers related to embodied intelligence, providing a reference for further learning and research. Welcome to visit hyper.ai to explore more high-quality resources!

A research team from Goethe University in Germany has classified the "human E3 ligase genome" using metric learning, integrating multi-level data including protein sequences, domain composition, three-dimensional structure, function, and expression patterns. This method expands the traditional classification of E3 enzymes (RING, HECT, and RBR classes) to include atypical mechanisms, successfully elucidates functional partitions, distinguishes between multi-subunit complexes and monomeric enzymes, and maps E3 enzymes to substrates and potential drug targets.

A research team at Yale University recently proposed the MOSAIC model, which transforms a generalized big language model into a collaborative system composed of numerous specialized chemistry experts. Through professional division of labor, it effectively suppresses model illusion, provides quantifiable uncertainty assessment, and realizes the systematic generation from reaction description to complete experimental scheme. It is expected to substantially improve scientific research efficiency in fields such as drug discovery and materials development.

The "GLM-Image Precise Semantic High-Fidelity Image Generation Model" is now available on the HyperAI website (hyper.ai) tutorial section. Come and unleash your boundless creativity!

A research team from Tsinghua University and the University of Chicago systematically assessed the real impact of AI tools on scientific research using a massive dataset of 41.3 million natural science papers and 5.37 million scientists from 1980 to 2025. The study found that while AI significantly enhances individual research output and academic influence, it leads to a contraction of knowledge space and a concentration of academic interaction at the collective level. By identifying AI research through language models and introducing innovation indicators such as "knowledge breadth," the paper reveals the overlooked structural costs behind AI for Science, providing crucial evidence for understanding how AI is reshaping the research ecosystem.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from version 1.12 to 1.16, covering multiple fields such as intelligent agents, computer vision, and TTS.

"Qwen-Image-2512: Generating More Realistic Portraits and Natural Landscapes" is now available on the HyperAI website (hyper.ai) tutorial section. Come and unleash your boundless creativity!

A joint research team from Princeton University and the Colorado School of Mines has proposed an efficient prediction method based on machine learning. This method uses a large language model to directly predict the free energy from the structural sequence of MOFs, thereby significantly reducing computational costs and enabling high-throughput, scalable thermodynamic assessment of MOFs.

To further refine HyperAI's product experience and core capabilities, we are officially launching a new round of internal testing. We hope to invite a select group of real users to experience the platform's capabilities and contribute to polishing product details. 💻 If you have a long-term need for cloud platforms and GPU computing power, 🙋‍♀️ if you have a technical background [...]

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from December 8th to 12th, covering multiple fields such as intelligent agents, computer vision, and TTS. (Dates and representative fields will be adjusted according to actual circumstances.)

In 2025, the narrative logic of the AI industry is undergoing a violent dismantling. With hundreds of billions of dollars in capital expenditures (CapEx) colliding with sluggish revenue growth, the seats for judging whether it's a "bubble" are already full. From Alphabet's evaporated $200 billion market capitalization to ChatGPT's staggering loss black hole, technology seems to be forced to bow to pragmatism.

CleaveNet, an AI-based end-to-end design process proposed by a joint team from MIT and Harvard University, is designed to address this challenge. This process, through the collaborative work of predictive and generative models, aims to revolutionize the existing paradigm of protease substrate design, providing entirely new solutions for related basic research and biomedical development.

The "HY-MT1.5-1.8B: Multilingual Neural Machine Translation Model" is now available on the HyperAI website (hyper.ai) in the tutorial section. Come and experience its lightning-fast translation capabilities!

The 8th Meet AI Compiler technical salon in 2025 successfully concluded on December 27th at the Shanghai Innovation Academy.

Founded in 2023, FieldAI, an embodied intelligence company, has raised over $405 million in less than two years, with investors including Jeff Bezos, Intel, Nvidia, Bill Gates, and Samsung. Its core team members come from leading companies such as NASA JPL, DeepMind, Tesla, and SpaceX, and the company is dedicated to creating a "universal robotic intelligent brain" capable of working across different types of robots and adapting to various environments.