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

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

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: US unlocks cheaper jet fuel with new catalyst that converts et

Researchers in the US have designed a catalyst that could significantly lower the cost of producing sustainable aviation fuel (SAF), by converting ethanol into jet fuel precursors in a single step.

Colorado-based advanced biofuels company Gevo licensed two patented catalyst technologies from the US Department of Energy’s Oak Ridge National Laboratory (ORNL), to accelerate the commercial production of sustainable aviation fuel.

The innovation relies on a streamlined method that turns ethanol (also known as ethyl alcohol), commonly sourced from plant or waste feedstocks, into olefins (ETO). These are essential precursors used to produce jet fuel.

While this conversion typically involves multiple steps, the newly licensed catalyst supports a single-step ethanol-to-olefins pathway. This significantly improves the production process.

“This partnership will streamline the transition of ORNL’s catalyst technologies from lab scale to pilot-scale reactors,” Andrew Sutton, PhD, a senior scientist in the manufacturing science division at ORNL, explained.

Single-step fuel production

SAF is a cleaner, non-petroleum-based alternative to conventional jet fuel. It is produced from renewable waste, fats, oils and agricultural residues. It is widely regarded as a critical solution in the push to decarbonize air travel.

The International Air Transport Association, which represents over 80 percent of global air traffic, has signaled strong interest in SAF. Many airlines have already committed to large-scale purchases.

Nevertheless, production efficiencies still remain an issue, predominantly due to high production costs, limited feedstock availability, and complex infrastructure requirements.

To address this challenge, ORNL built a catalyst technology capable of improving carbon efficiency and cutting the cost of converting ethanol into fuel precursors.

At the same time, the olefins produced through this process can also be utilized in the production of plastics, solvents and surfactants. For perspective, the global plastics market is set to surpass USD 1.3 trillion by 2033.

“Gevo’s collaboration with Oak Ridge National Laboratory focuses on evaluating a novel catalytic process that converts ethanol into valuable fuel precursors and alternative chemicals like butadiene,” Andrew Ingram, PhD, director of process chemistry and catalysis at Gevo, said.

Unlocking cheaper SAF

The project is supported by a three-year cooperative research and development (R&D) agreement under the DOE’s Technology Commercialization Fund. Under the program, ORNL will develop and test catalyst pellets in advanced chemical reactors.

It will also build computational models to predict performance at industrial scale. “This work complements our broader ethanol conversion portfolio but is distinct from both our commercial deployment of Axens’ alcohol-to-jet process and our next-generation ETO platform,” Ingram continued.

In turn, Gevo will contribute process design and operational expertise, and guide how the technology is integrated in the pilot reactor.

“If the economics prove out, this pathway could provide a flexible, cost-effective option to scale US bio-based solutions, driven by American innovation that creates new markets and demand for farmers producing feedstocks for energy and materials,” Ingram concluded in a press release.

ORNL will also leverage advanced materials analysis capabilities at its Center for Nanophase Materials Sciences to better understand catalyst performance in large reactors.

Global demand for jet fuel is expected to surge to 230 billion gallons by 2050. Expanding SAF use could help the aviation industry meet this demand while advancing US energy independence and security, and reducing emissions.

🔗 Sumber: interestingengineering.com