MAROKO133 Hot ai: China’s bizarre-looking submarine could work as surface vessel, house la

📌 MAROKO133 Eksklusif ai: China’s bizarre-looking submarine could work as surface

China has recently revealed an unusual naval vessel that blends characteristics of both a surface ship and a submarine, sparking considerable speculation among defense analysts.

Photographs from the Huangpu shipyard in Guangzhou show a dark, slender trimaran-style craft with a small sail-like structure and markings that resemble immersion or depth indicators, suggesting it may be capable of operating partially or fully below the surface, according to reports.

Because its superstructure is minimal and no clear missile tubes, hangars, or crew spaces are visible, experts believe it could be an unmanned or minimally crewed platform. The vessel’s purpose remains unclear, but several theories have emerged.

Semi-submersible arsenal ship

It might be a semi-submersible arsenal ship capable of launching missiles before slipping underwater, a drone-deployment platform for aerial or underwater systems, a covert transport for special-operations missions in littoral environments, or simply a testbed for new hybrid naval technologies.

The lack of official information adds to the mystery, but the craft’s appearance underscores China’s willingness to experiment with unconventional designs that could complicate detection and tracking by rival navies. As more imagery and operational details emerge, observers will be watching for hints about whether this is an isolated prototype or the first step toward a new class of stealthy, adaptive maritime platforms.

Unique propulsion system

The vessel’s submarine-like attributes are perhaps even more pronounced. These features may also include a propulsor at the rear, which would point to a pump-jet being fitted, a possible feature noted by Alex Luck, a journalist who closely follows the People’s Liberation Army Navy (PLAN). Pump-jets offer an array of advantages over traditional propellers, above all the ability to reach higher speeds without noisy cavitation — this means they can transit long distances around much more stealthily, reported The War Zone.

More obvious in this view is the submarine-like sail, which is fitted with a snorkel or possibly an antenna mast.

Weeks ago, the vessel was first widely observed in satellite imagery of the People’s Liberation Army Navy’s Huangpu Shipyard in Guangzhou. The hull appears to be around 65 metres in length. Its hull design is a trimaran — a slender central hull with outriggers — which is uncommon for traditional submarines or surface warships.

The vessel is painted in a dark grey or black color, reminiscent of submarine camouflage or low-visibility coatings. Its profile combines elements typical of both submarines and surface ships.

The superstructure is minimal and narrow, suggesting little space for a traditional crew, large radar/sensor arrays, or living quarters, according to the report.

Some analysts believe the vessel may use a pump-jet propulsor rather than a conventional open-propeller. Pump-jets are quieter (less cavitation), which would support stealthy movement, especially underwater or near-surface.

A leading theory is that this is the semi-submersible arsenal ship which has been speculated would be built. Rumours of this emerged on the Chinese internet in 2017. Although hung from published scientific research, much of the reporting at the time was highly speculative. Stories blended fan art with rehashed U.S. Navy ideas of arsenal ships, the concept of a vessel designed solely to carry a large quantity of land-attack missiles. Possibly China has finally built one, reported Naval News.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Update ai: Adobe Research Unlocking Long-Term Memory in Video World Mo

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