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

📌 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 Hot ai: Archaeologists find 5,000-year-old winepress, solving mystery

Archaeologists in Israel just unearthed the oldest winepress ever discovered at an early Canaanite settlement, offering direct proof that winemaking was fundamental to the birth of the first cities.

The first urban inhabitants drank wine, a recent archaeological survey found, after discovering the rare wine press near Tel Megiddo in Israel’s north.

The Israel Antiquities Authority (IAA) announced on Facebook that during an excavation, they found evidence that a Canaanite cult occupied this land before the arrival of the Israelites—and they made wine.

Excavation director Barak Tzin called the find nothing short of “unique,” stating in a Facebook post that the winepress, linked to the beginnings of urbanization, shows that winemaking and ritual were intrinsically tied at the dawn of civilization.

A very important wine press

During an excavation in the Jezreel Valley, archaeologists discovered evidence of early settlement and rapid expansion coinciding with the onset of urbanization. The IAA launched its archaeological investigation because a new highway, initiated and financed by Netivei Israel, is currently under development. The extensive archaeological study covered an area of over three-quarters of a mile.

They uncovered an artifact of singular importance: a 5,000-year-old winepress, one of the oldest ever found in the country, according to a Facebook post.

Dr. Amir Golani and Barak Tzin, the IAA’s Excavation Directors, said the winepress is “one of the very few known from such an ancient period when urbanization first took place in our region.”

They continued to explain that winepresses were “…very common throughout the country, but it is very difficult to date them. Until now, indirect evidence indicated that wine could have been produced 5,000 years ago, but we did not have conclusive proof of this – a ‘smoking gun’ that would clearly show when this happened in our area. This winepress finally provides new and clear evidence that early wine production actually took place here.”

However, archaeologists unearthed far more than just a wine press. They found residential buildings, along with evidence of a 3,300-year-old Canaanite folk cult, including ritual objects such as a ceramic shrine model, an intact ceremonial zoomorphic utensil kit, and an impressive set of vessels.

Typically, these artifacts are found only in fragments, making it difficult for researchers to understand their complete form and how they functioned together.

A new look at local Canaanite folk culture

Furthermore, the ritual vessels were located in alignment with a large temple area visible in the distance. This suggests a Canaanite folk cult operated outside the city, possibly indicating that special accommodations were made for local farmers who might not have entered the city.

“Megiddo has been excavated for over a century,” the researchers concluded in a Facebook post.

“While it is long-recognized as a key site in the study of ancient urbanism and Canaanite worship, the excavations we conducted east of the tel have revealed a new part of the matrix between the known settlement in the city – evidence of which has been revealed upon the tel – and the activities taking place in the area around and outside the city.”

“The 5,000-year-old hewn winepress places the beginnings of the local wine industry in a very early urban-settlement context, while the offerings from the period about 3,300 years ago indicate the continuity of ritual consecration and libations outside the sacred complex within the tell, possibly expressing aspects of the local Canaanite folk cult.”

🔗 Sumber: interestingengineering.com