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 Hot ai: Anthropic valued at $350B as Google commits up to $40B in mass

Alphabet is doubling down on its AI ambitions with a massive new bet on Anthropic, even as the two compete in the same market.

The parent of Google plans to invest up to $40 billion in Anthropic, strengthening a partnership centered on computing infrastructure. The move highlights how access to chips and data centers is becoming the defining factor in the global AI race.

Anthropic confirmed that Google will commit $10 billion upfront at a $350 billion valuation. Another $30 billion will follow if performance targets are met.

Compute race intensifies

The deal comes as AI firms scramble for computing power to train and deploy increasingly complex models. Anthropic has seen surging demand for its Claude family, especially among developers using its coding-focused tools.

Its annualized revenue has already crossed $30 billion, up sharply from about $9 billion at the end of 2025. Investor interest has also surged, with recent funding valuing the company at $380 billion post-money and reports suggesting offers as high as $800 billion.

To support this growth, Anthropic has locked in major infrastructure agreements. It recently signed multi-year deals with Broadcom and CoreWeave. The company is also set to secure nearly one gigawatt of compute capacity using chips from Amazon by the end of the year.

Earlier plans outlined a $50 billion investment to build U.S. data centers, reinforcing its long-term infrastructure strategy.

Google backs rival partner

Despite competing in AI models, Google plays a critical role as Anthropic’s infrastructure partner. Anthropic relies heavily on Google Cloud, particularly its tensor processing units, or TPUs, which offer an alternative to NVIDIA’s high-demand GPUs.

The new investment expands that relationship significantly. Google Cloud will provide an additional five gigawatts of compute capacity over five years, with room for further scaling.

This builds on earlier collaborations. Anthropic recently partnered with Google and Broadcom to access TPU-based capacity starting in 2027. A Broadcom filing later pegged that capacity at 3.5 gigawatts.

New model raises stakes

The investment follows the limited release of Anthropic’s latest model, Mythos. The company describes it as its most powerful system yet, with strong cybersecurity applications.

However, Anthropic has restricted access due to misuse risks. The model has already appeared in unsanctioned environments, raising concerns about control and safety. It is also expected to be expensive to run at scale, adding further pressure on infrastructure.

Meanwhile, Amazon has deepened its own ties with Anthropic. The company recently announced plans to invest up to $25 billion more, part of a broader agreement that could involve up to $100 billion in compute spending over time.

The broader AI landscape shows similar trends. Competitors like OpenAI continue to secure massive infrastructure deals across cloud providers, chipmakers, and energy firms.

In this environment, capital alone is not enough. Access to reliable, large-scale computing power has become the real battleground and partnerships like Google and Anthropic’s may define who leads the next phase of AI development.

🔗 Sumber: interestingengineering.com