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

📌 MAROKO133 Breaking 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 Breaking ai: New color-changing gel that stretches 4600%, heals itself

Researchers in Taiwan have developed a stretchable, self-healing gel that changes color when pulled or heated. It combines strength and built-in sensors in a single material that could have interesting applications in wearable devices and soft robotics.

In short, the new material can be thought of as a smart, rubbery material that tells you when it’s stressed out by literally changing color. This breakthrough is important because most soft or stretchable materials either stretch well but break easily, or stay tough but don’t heal themselves or sense stress.

This new gel, however, manages to combine strength, healing, and sensing capabilities in one material, which is a rare feat. The secret sauce behind the breakthrough is a clever manipulation of its molecular design.

The researchers used mechanically interlocked molecules called rotaxanes, which are ring-shaped molecules that slide along a “rod.” These are linked together in daisy chains with two rotaxanes linked together, which can expand/contract more like a spring.

Self-healing, strong, and dual-sensing

Using these, the team also attached a special fluorescent unit called DPAC to these molecules. When free to move, DPAC glows orange, but when restricted (like when stretched or bent), DPAC glows blue.

So, when you pull the gel, the rings slide and restrict DPAC’s movement, making the gel visibly shift from orange to blue under UV light. The interlocked molecules were chemically bonded into a polyurethane gel reinforced with cellulose nanocrystals (tiny, strong fibers).

The cellulose helps the gel self-heal by forming reversible hydrogen bonds across the network. Because the sliding molecules are built into the gel (not just mixed in), their motion directly couples with the gel’s stretching.

When tested, the team found that the new material is extremely stretchy, able to safely handle ~4600% strain (like stretching 1 cm of gel to 46 cm without breaking). It is also extremely tough, exhibiting a toughness of 142 MJ/m³, which is approximately 2.6 times tougher than the same gel without these molecules.

Since the material also changes color under strain (shifts from orange to blue), you can map the stress distribution of the gel simply by observing the coloration. It also benefits from dual sensing as it also changes color with heat (orange at higher temps, blue when cool/strained).

But one of the main benefits of the material is its self-healing properties. This means it can damage heals at room temperature in hours, or faster with mild heating.

Interesting potential applications

This kind of material could prove very useful for wearable devices that monitor stress and strain in real-time. The gel could also have some interesting applications in soft robotics, where parts need to be both strong and responsive.

It could also, theoretically, be used to make artificial skin or biomedical implants that can sense and self-repair. The gel could also open the door for damage-tolerant electronics that don’t fail suddenly but show visible signs of strain.

In short, this is a smart gel where tiny sliding molecules act like both shock absorbers and stress indicators. Stretch it, and it heals itself while lighting up with a color change that tells you how much it’s being strained or heated.

The study is available in the journal Advanced Functional Materials.

🔗 Sumber: interestingengineering.com