📌 TOPINDIATOURS Hot ai: Adobe Research Unlocking Long-Term Memory in Video World M

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

📌 TOPINDIATOURS Update ai: Fire Breaks Out at UN Climate Summit Hari Ini

The United Nations global climate summit, COP30, only delivered modest progress on its campaign to slow global warming. The unintentional symbolism, though, was extraordinary: on Thursday, the convention location itself had to be evacuated after a massive fire broke out.

The event, hosted in Belém, Brazil at the threshold of the Amazon rainforest, was in its second to last day of proceedings when the flames prompted an evacuation, according to Heatmap. In a feat of cosmic irony, the “blue zone” — where only official delegates, journalists, and world leaders were allowed — was forced to evacuate at around 2pm local time.

“We’ve been evacuated due to a fire — not exactly sure how the day is going to continue,” a journalist with Heatmap was told via text.

Video uploaded by the New York Times shows flames billowing out of a long booth, burning a massive hole through the ceiling. In all, 13 people were treated for smoke inhalation, and meetings were delayed until much later Thursday night.

Unlike the “green zone” which was open to the public, all official UN proceedings took place in the blue zone, making the fire a pretty severe setback. Per the NYT, the fire was just one of a handful of disruptions at COP30, coming after food shortages, water leaks, and dizzying heat left the summit attendees reeling.

In all, over 56,000 delegates from some 200 countries had registered to attend the proceedings in-person. Notably absent was any federal delegation from the United States, though state-level governors Gavin Newsom of California and Michelle Lujan Grisham of New Mexico were present for at least some of the summit.

As journalist Aayushi Gour reported, COP30 concluded with a “sharp split” over the inclusion of language addressing a transition away from fossil fuels. After Brazil circulated a final draft of the UN climate document with no mention of fossil fuels, a group of 30 countries from Asia, Africa, and Europe issued a rebuke stating they would refuse to support an outcome which ignored the UN’s previous commitments to ending reliance of fossil fuels.

In all, it was a fiery end to a critical summit which left much to be desired — both inside and outside the convention hall.

More on climate: Elon Musk Announces Plan to Control Climate by Surrounding Earth in Adjustable Satellites

The post Fire Breaks Out at UN Climate Summit appeared first on Futurism.

🔗 Sumber: futurism.com