HyperAI's "Tutorials" section has launched online tutorials for running popular open-source models such as Qwen, DeepSeek, Gemma, Llama, and GLM using free CPUs. It provides a complete deployment process from environment preparation and model download to inference and execution, allowing users to complete model inference experience and basic development testing without having to deploy a complex local environment.

Researchers from the Swiss Federal Institute of Technology in Lausanne (EPFL) have proposed a novel model architecture, DYNAMI-CAL GraphNet, which explicitly guarantees the conservation of linear momentum and angular momentum by directly embedding these laws into the model structure. Experimental results demonstrate that DYNAMI-CAL GraphNet offers significant advantages in fields requiring accurate, interpretable, and real-time modeling of complex multibody dynamical systems, such as robotics, aerospace engineering, and materials science.

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 [...]

"Qwen3-TTS: High-Quality Controllable Multilingual Speech Synthesis Demo" is now available on the "Tutorials" section of the HyperAI website (hyper.ai). Come and experience 3-second speech cloning!

A research team from Telecom Sud-Paris and Paris-Saclay University in France has proposed a machine learning framework that integrates ensemble learning with SHAple Additive exPlanations (SHAP) analysis, providing a new solution for assessing the mortality risk of HCC liver transplant candidates.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from versions 3.2 to 3.6, covering multiple fields such as speech generation, text-to-image processing, and large-scale models.

A joint research team from MIT and ETH Zurich has proposed a computational framework called APOLLO, which is an autoencoder that learns partially overlapping latent spaces through latent variable optimization. By explicitly modeling shared information and modality-specific information, APOLLO provides a feasible technical path for more comprehensive and accurate analysis of cell states and their regulatory logic.

A research team from MIT has proposed a deep learning-based language model, Pichia-CLM, for codon optimization in the industrial host Pichia pastoris to improve the yield of recombinant proteins. The researchers experimentally validated Pichia-CLM on six protein classes of varying complexity and consistently observed higher expression yields compared to four commercial codon optimization tools.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from February 22nd to 27th, covering multiple fields such as OCR, multimodal, and large language models.

A joint research team comprised of the University of Helsinki in Finland, the Mediterranean Climate Change Research Centre, and the University of Salento in Italy has developed SeaCast, a graph neural network model specifically designed for regional ocean forecasting. Once trained, this model can generate a 15-day forecast across 18 vertical levels at a 1/24° grid in just 20 seconds on a single GPU, significantly faster than physical base models running on CPU clusters.

A research team from Cornell University has developed a robust, interpretable, and data-efficient framework, SCAN, for modeling and interpreting salt-solvent chemistry. This framework effectively handles long-tailed data and captures the complete spectrum of salt-solvent formulations. The researchers applied SCAN to non-aqueous electrolyte (NAE) systems, achieving a baseline error of 0.372 mS·cm⁻¹ in conductivity prediction, reducing the prediction error by 65.31 TP³T compared to the baseline model.

Professor Tzu-Yu Song of the University of Michigan, Ann Arbor, in collaboration with Wei-Ran Jiang, Vice President of R&D at Farasis Energy, has innovatively proposed a scientific machine learning method called "discovery learning." Inspired by educational psychology, this method organically integrates active learning, physically constrained learning, and zero-shot learning to construct a human-like closed-loop learning framework for reasoning.

A quick overview of the most noteworthy cutting-edge papers in AI for Science in 2025.

WorldArena, proposed by institutions such as Tsinghua University, Peking University, University of Hong Kong, Princeton University, Chinese Academy of Sciences, Shanghai Jiao Tong University, University of Science and Technology of China, and National University of Singapore, is the first to integrate video generation quality with embodied task functionality, constructing a complete evaluation framework from "looks real" to "is truly usable".

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!

A research team from the Department of Computer Science at University College London (UCL) has proposed a federated learning framework for leukocyte morphology analysis, enabling institutions to collaboratively train without exchanging training data. Utilizing blood smears from multiple clinical sites, the federated model learns robust and domain-invariant feature representations while maintaining complete data privacy. Compared to centralized training, federated training demonstrates superior performance across sites and its ability to generalize to unknown institutions.

A research team comprised of Microsoft Research, the University of Washington, and Providence Genomics has proposed GigaTIME, a multimodal artificial intelligence framework. This framework, based on advanced multimodal learning techniques, can generate virtual mIF maps from conventional H&E slices. The research team applied it to a cohort of over 14,000 cancer patients at Providence Medical Center, covering 24 cancer types and 306 subtypes, ultimately generating nearly 300,000 virtual mIF images, achieving systematic modeling of the tumor immune microenvironment in a large and diverse population.

A research team from MIT, the Technical University of Munich, and the Polytechnic University of Valencia has innovatively proposed DiffSyn—a generative diffusion model trained on more than 23,000 generative recipes from literature spanning over 50 years.

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.

Tencent's WeChat AI team has proposed WeDLM, the first diffusion language model to outperform comparable AR models in inference speed under industrial-grade inference engine (vLLM) optimization. The "WeDLM High-Efficiency Large Language Model Decoding Framework" is now available on the HyperAI website's "Tutorials" section; this article provides a detailed tutorial.

Scientists from Oak Ridge National Laboratory of the U.S. Department of Energy have proposed a distributed cross-channel hierarchical aggregation method (D-CHAG) for basic models. This method distributes the tokenization process and uses a hierarchical strategy for channel aggregation, enabling extremely large-scale models to run on multi-channel datasets.

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 versions 2.2 to 2.6, covering multiple fields such as intelligent agents, computer vision, and TTS.

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.

HyperAI's "Tutorials" section has launched online tutorials for running popular open-source models such as Qwen, DeepSeek, Gemma, Llama, and GLM using free CPUs. It provides a complete deployment process from environment preparation and model download to inference and execution, allowing users to complete model inference experience and basic development testing without having to deploy a complex local environment.

Researchers from the Swiss Federal Institute of Technology in Lausanne (EPFL) have proposed a novel model architecture, DYNAMI-CAL GraphNet, which explicitly guarantees the conservation of linear momentum and angular momentum by directly embedding these laws into the model structure. Experimental results demonstrate that DYNAMI-CAL GraphNet offers significant advantages in fields requiring accurate, interpretable, and real-time modeling of complex multibody dynamical systems, such as robotics, aerospace engineering, and materials science.

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 [...]

"Qwen3-TTS: High-Quality Controllable Multilingual Speech Synthesis Demo" is now available on the "Tutorials" section of the HyperAI website (hyper.ai). Come and experience 3-second speech cloning!

A research team from Telecom Sud-Paris and Paris-Saclay University in France has proposed a machine learning framework that integrates ensemble learning with SHAple Additive exPlanations (SHAP) analysis, providing a new solution for assessing the mortality risk of HCC liver transplant candidates.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from versions 3.2 to 3.6, covering multiple fields such as speech generation, text-to-image processing, and large-scale models.

A joint research team from MIT and ETH Zurich has proposed a computational framework called APOLLO, which is an autoencoder that learns partially overlapping latent spaces through latent variable optimization. By explicitly modeling shared information and modality-specific information, APOLLO provides a feasible technical path for more comprehensive and accurate analysis of cell states and their regulatory logic.

A research team from MIT has proposed a deep learning-based language model, Pichia-CLM, for codon optimization in the industrial host Pichia pastoris to improve the yield of recombinant proteins. The researchers experimentally validated Pichia-CLM on six protein classes of varying complexity and consistently observed higher expression yields compared to four commercial codon optimization tools.

HyperAl has compiled a series of highly valuable and widely applicable tutorials and datasets from February 22nd to 27th, covering multiple fields such as OCR, multimodal, and large language models.

A joint research team comprised of the University of Helsinki in Finland, the Mediterranean Climate Change Research Centre, and the University of Salento in Italy has developed SeaCast, a graph neural network model specifically designed for regional ocean forecasting. Once trained, this model can generate a 15-day forecast across 18 vertical levels at a 1/24° grid in just 20 seconds on a single GPU, significantly faster than physical base models running on CPU clusters.

A research team from Cornell University has developed a robust, interpretable, and data-efficient framework, SCAN, for modeling and interpreting salt-solvent chemistry. This framework effectively handles long-tailed data and captures the complete spectrum of salt-solvent formulations. The researchers applied SCAN to non-aqueous electrolyte (NAE) systems, achieving a baseline error of 0.372 mS·cm⁻¹ in conductivity prediction, reducing the prediction error by 65.31 TP³T compared to the baseline model.

Professor Tzu-Yu Song of the University of Michigan, Ann Arbor, in collaboration with Wei-Ran Jiang, Vice President of R&D at Farasis Energy, has innovatively proposed a scientific machine learning method called "discovery learning." Inspired by educational psychology, this method organically integrates active learning, physically constrained learning, and zero-shot learning to construct a human-like closed-loop learning framework for reasoning.

A quick overview of the most noteworthy cutting-edge papers in AI for Science in 2025.

WorldArena, proposed by institutions such as Tsinghua University, Peking University, University of Hong Kong, Princeton University, Chinese Academy of Sciences, Shanghai Jiao Tong University, University of Science and Technology of China, and National University of Singapore, is the first to integrate video generation quality with embodied task functionality, constructing a complete evaluation framework from "looks real" to "is truly usable".

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!

A research team from the Department of Computer Science at University College London (UCL) has proposed a federated learning framework for leukocyte morphology analysis, enabling institutions to collaboratively train without exchanging training data. Utilizing blood smears from multiple clinical sites, the federated model learns robust and domain-invariant feature representations while maintaining complete data privacy. Compared to centralized training, federated training demonstrates superior performance across sites and its ability to generalize to unknown institutions.

A research team comprised of Microsoft Research, the University of Washington, and Providence Genomics has proposed GigaTIME, a multimodal artificial intelligence framework. This framework, based on advanced multimodal learning techniques, can generate virtual mIF maps from conventional H&E slices. The research team applied it to a cohort of over 14,000 cancer patients at Providence Medical Center, covering 24 cancer types and 306 subtypes, ultimately generating nearly 300,000 virtual mIF images, achieving systematic modeling of the tumor immune microenvironment in a large and diverse population.

A research team from MIT, the Technical University of Munich, and the Polytechnic University of Valencia has innovatively proposed DiffSyn—a generative diffusion model trained on more than 23,000 generative recipes from literature spanning over 50 years.

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.

Tencent's WeChat AI team has proposed WeDLM, the first diffusion language model to outperform comparable AR models in inference speed under industrial-grade inference engine (vLLM) optimization. The "WeDLM High-Efficiency Large Language Model Decoding Framework" is now available on the HyperAI website's "Tutorials" section; this article provides a detailed tutorial.

Scientists from Oak Ridge National Laboratory of the U.S. Department of Energy have proposed a distributed cross-channel hierarchical aggregation method (D-CHAG) for basic models. This method distributes the tokenization process and uses a hierarchical strategy for channel aggregation, enabling extremely large-scale models to run on multi-channel datasets.

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 versions 2.2 to 2.6, covering multiple fields such as intelligent agents, computer vision, and TTS.

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.