MAROKO133 Hot ai: Adobe Research Unlocking Long-Term Memory in Video World Models with Sta

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

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

📌 MAROKO133 Hot ai: Sensor smaller than a paperclip lasts 20,000 cycles, boosts ro

Robots are now capable of seeing and moving, but touch remains a major challenge for them. The difference between how robots and humans sense touch has slowed progress in prosthetics, surgical robots, and other tasks that need careful handling.

Now, a research team at Penn State University has created a pressure sensor that could help close this gap. Their sensor performs better than traditional designs in almost every way.

The sensor uses reduced graphene oxide aerogel (rGOA). This lightweight, oxygen-rich material is shaped into a specific structure via freeze casting.

This process turns a mix of liquids and solids into a material that is stronger in some directions than others. Because of this, the sensor can be very sensitive, detect a wide range of pressures, and stay stable over time. Achieving all these features together has been difficult in the past.

Paperclip-sized sensors

Each sensor is only 8 millimeters wide, about the size of a paperclip, but it can handle about 3 ounces of force and survive over 20,000 cycles of use without losing performance.

The sensor is made by placing the rGOA between a plastic-like film with silver ink electrodes and a thin silicone layer. This layered design keeps electrical contact stable, strengthens the sensor, and allows it to bend for use on curved or uneven surfaces.

Tests showed these sensors are almost twice as sensitive as traditional ones. They react to pressure changes in just over 100 milliseconds and reset in 40 milliseconds, finishing a full cycle in under 150 milliseconds.

Standard sensors can take over 250 milliseconds to perform the same process, which makes a big difference in tasks like robotic gripping, where quick feedback is important.

From skin to array

These sensors become much more powerful when connected together in arrays. With a microcontroller, the system can collect and display pressure data in real time, showing where and how much pressure is applied across a surface.

In tests, the system could recognize object shapes, distinguish food items by weight and texture, track hand movements wirelessly, and monitor grip pressure to avoid breaking delicate items such as tofu, cotton, and steamed buns.

The team also found another use for the sensor: spotting early signs of battery swelling in electric vehicles.

When pressure builds up inside lithium-ion batteries, it can lead to damage, overheating, or even fire.

Because these sensors can detect small pressure changes on complex surfaces, they are well-suited for monitoring battery health and detecting swelling before it becomes dangerous.

The team has filed a provisional patent and plans to keep developing the technology for commercial use.

They want to make the sensors smaller and lighter for use in implants or wearables, add temperature and strain sensing, and create arrays that can sense very light touches in one area while handling heavy pressure in another.

The paper was first published in Nano-Micro Letters.

🔗 Sumber: interestingengineering.com