📌 TOPINDIATOURS Hot ai: World’s largest nuclear fusion reactor gets critical 4-ton

The US Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) will provide measurement equipment for the JT-60SA experimental fusion system in Japan, which is scheduled to begin operations in 2026. 

A new agreement facilitates the collaboration between PPPL, Japan’s National Institutes for Quantum Science and Technology (QST), and Europe’s Fusion for Energy (F4E).

“PPPL is among the first US institutions to have its equipment installed directly into JT-60SA,” said Luis Delgado-Aparicio, head of advanced projects at PPPL.

Challenge of plasma control

Fusion experiments like JT-60SA work with plasma, a state of matter heated to extreme temperatures. A primary technical challenge is controlling this plasma. Scientists require precise measurements of the plasma’s temperature, speed, and the presence of any particles that could cool the reaction. 

Inaccurate data can lead to instability, allowing the plasma to escape its magnetic confinement and potentially cause damage to the machine.

“Because JT-60SA will be such a powerful machine, we will access operating conditions that we have never achieved before,” added Delgado-Aparicio. 

“The measurements need to be very accurate for us to learn the science of those new regimes.”

A specialized instrument

PPPL will provide a diagnostic tool called an X-ray imaging crystal spectrometer (XICS). The instrument, which weighs four tons, will be installed in early 2026 to measure X-rays emitted from the plasma. 

According to the research team, the XICS is equipped with an advanced calibration system designed to provide accurate measurements through changes in plasma density and temperature. This level of precision is needed to achieve the stable conditions required for a potential commercial fusion power plant.

PPPL was selected for this task because the lab developed the XICS diagnostic tool over the last two decades and has installed similar systems in other fusion facilities.

“XICS is essential. You need something like it to get the data from plasma and do the physics,” said PPPL Principal Research Physicist Masayuki Ono.

A step in fusion research

The JT-60SA project is a significant step in fusion energy research. It will be the most powerful tokamak in operation until the ITER facility in France is completed, allowing scientists to test concepts for future power plants.

“Despite being smaller than ITER, JT-60SA’s power density — or power per unit volume — will be exceptionally high, allowing scientists to explore new plasma behaviors and test concepts for future power plants,” said the PPPL in a press release.

The collaboration involves more than just providing the equipment. PPPL scientists will assist in operating the diagnostic, analyzing the data, and sharing the findings. 

The information gained will be used to inform the design and operation of diagnostics on ITER and other future fusion projects.

Earlier, the superconducting plasma experimental device JT-60SA achieved a plasma volume of 160 cubic meters. This achievement was officially certified by Guinness World Records.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Breaking ai: Adobe Research Unlocking Long-Term Memory in Video Wo

Video world models, which predict future frames conditioned on actions, hold immense promise for artificial intelligence, enabling agents to plan and reason in dynamic environments. Recent advancements, particularly with video diffusion models, have shown impressive capabilities in generating realistic future sequences. However, a significant bottleneck remains: maintaining long-term memory. Current models struggle to remember events and states from far in the past due to the high computational cost associated with processing extended sequences using traditional attention layers. This limits their ability to perform complex tasks requiring sustained understanding of a scene.

A new paper, “Long-Context State-Space Video World Models” by researchers from Stanford University, Princeton University, and Adobe Research, proposes an innovative solution to this challenge. They introduce a novel architecture that leverages State-Space Models (SSMs) to extend temporal memory without sacrificing computational efficiency.

The core problem lies in the quadratic computational complexity of attention mechanisms with respect to sequence length. As the video context grows, the resources required for attention layers explode, making long-term memory impractical for real-world applications. This means that after a certain number of frames, the model effectively “forgets” earlier events, hindering its performance on tasks that demand long-range coherence or reasoning over extended periods.

The authors’ key insight is to leverage the inherent strengths of State-Space Models (SSMs) for causal sequence modeling. Unlike previous attempts that retrofitted SSMs for non-causal vision tasks, this work fully exploits their advantages in processing sequences efficiently.

The proposed Long-Context State-Space Video World Model (LSSVWM) incorporates several crucial design choices:

1. Block-wise SSM Scanning Scheme: This is central to their design. Instead of processing the entire video sequence with a single SSM scan, they employ a block-wise scheme. This strategically trades off some spatial consistency (within a block) for significantly extended temporal memory. By breaking down the long sequence into manageable blocks, they can maintain a compressed “state” that carries information across blocks, effectively extending the model’s memory horizon.
2. Dense Local Attention: To compensate for the potential loss of spatial coherence introduced by the block-wise SSM scanning, the model incorporates dense local attention. This ensures that consecutive frames within and across blocks maintain strong relationships, preserving the fine-grained details and consistency necessary for realistic video generation. This dual approach of global (SSM) and local (attention) processing allows them to achieve both long-term memory and local fidelity.

The paper also introduces two key training strategies to further improve long-context performance:

• Diffusion Forcing: This technique encourages the model to generate frames conditioned on a prefix of the input, effectively forcing it to learn to maintain consistency over longer durations. By sometimes not sampling a prefix and keeping all tokens noised, the training becomes equivalent to diffusion forcing, which is highlighted as a special case of long-context training where the prefix length is zero. This pushes the model to generate coherent sequences even from minimal initial context.
• Frame Local Attention: For faster training and sampling, the authors implemented a “frame local attention” mechanism. This utilizes FlexAttention to achieve significant speedups compared to a fully causal mask. By grouping frames into chunks (e.g., chunks of 5 with a frame window size of 10), frames within a chunk maintain bidirectionality while also attending to frames in the previous chunk. This allows for an effective receptive field while optimizing computational load.

The researchers evaluated their LSSVWM on challenging datasets, including Memory Maze and Minecraft, which are specifically designed to test long-term memory capabilities through spatial retrieval and reasoning tasks.

The experiments demonstrate that their approach substantially surpasses baselines in preserving long-range memory. Qualitative results, as shown in supplementary figures (e.g., S1, S2, S3), illustrate that LSSVWM can generate more coherent and accurate sequences over extended periods compared to models relying solely on causal attention or even Mamba2 without frame local attention. For instance, on reasoning tasks for the maze dataset, their model maintains better consistency and accuracy over long horizons. Similarly, for retrieval tasks, LSSVWM shows improved ability to recall and utilize information from distant past frames. Crucially, these improvements are achieved while maintaining practical inference speeds, making the models suitable for interactive applications.

The Paper Long-Context State-Space Video World Models is on arXiv

The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced.

🔗 Sumber: syncedreview.com