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

📌 MAROKO133 Update ai: Adobe Research Unlocking Long-Term Memory in Video World Mo

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: Attention ISN'T all you need?! New Qwen3 variant Bru

When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence.

Every major large language model (LLM) — from OpenAI's GPT series to Anthropic's Claude, Google's Gemini, and Meta's Llama — has been built on some variation of its central mechanism: attention, the mathematical operation that allows a model to look back across its entire input and decide what information matters most.

Eight years later, the same mechanism that defined AI’s golden age is now showing its limits. Attention is powerful, but it is also expensive — its computational and memory costs scale quadratically with context length, creating an increasingly unsustainable bottleneck for both research and industry. As models aim to reason across documents, codebases, or video streams lasting hours or days, attention becomes the architecture’s Achilles’ heel.

On October 28, 2025, the little-known AI startup Manifest AI introduced a radical alternative. Their new model, Brumby-14B-Base, is a retrained variant of Qwen3-14B-Base, one of the leading open-source transformer models.

But while many variants of Qwen have been trained already, Brumby-14B-Base is novel in that it abandons attention altogether.

Instead, Brumby replaces those layers with a novel mechanism called Power Retention—a recurrent, hardware-efficient architecture that stores and updates information over arbitrarily long contexts without the exponential memory growth of attention.

Trained at a stated cost of just $4,000, the 14-billion-parameter Brumby model performs on par with established transformer models like Qwen3-14B and GLM-4.5-Air, achieving near-state-of-the-art accuracy on a range of reasoning and comprehension benchmarks.

From Attention to Retention: The Architectural Shift

The core of Manifest AI’s innovation lies in what they call the Power Retention layer.

In a traditional transformer, every token computes a set of queries (Q), keys (K), and values (V), then performs a matrix operation that measures the similarity between every token and every other token—essentially a full pairwise comparison across the sequence.

This is what gives attention its flexibility, but also what makes it so costly: processing a sequence twice as long takes roughly four times the compute and memory.

Power Retention keeps the same inputs (Q, K, V), but replaces the global similarity operation with a recurrent state update.

Each layer maintains a memory matrix S, which is updated at each time step according to the incoming key, value, and a learned gating signal.

The process looks more like an RNN (Recurrent Neural Network) than a transformer: instead of recomputing attention over the entire context, the model continuously compresses past information into a fixed-size latent state.

This means the computational cost of Power Retention does not grow with context length. Whether the model is processing 1,000 or 1,000,000 tokens, the per-token cost remains constant.

That property alone—constant-time per-token computation—marks a profound departure from transformer behavior.

At the same time, Power Retention preserves the expressive power that made attention successful. Because the recurrence involves tensor powers of the input (hence the name “power retention”), it can represent higher-order dependencies between past and present tokens.

The result is an architecture that can theoretically retain long-term dependencies indefinitely, while remaining as efficient as an RNN and as expressive as a transformer.

Retraining, Not Rebuilding

Perhaps the most striking aspect of Brumby-14B’s training process is its efficiency. Manifest AI trained the model for only 60 hours on 32 Nvidia H100 GPUs, at a cost of roughly $4,000 — less than 2% of what a conventional model of this scale would cost to train from scratch.

However, since it relied on a transformer-based model, it's safe to say that this advance alone will not end the transformer AI-era.

As Jacob Buckman, founder of Manifest AI, clarified in an email to VentureBeat: “The ability to train for $4,000 is indeed only possible when leveraging an existing transformer model,” he said. “Brumby could not be trained from scratch for that price.”

Still, Buckman emphasized the significance of that result: “The reason this is important is that the ability to build on the weights of the previous generation of model architectures is a critical accelerant for the adoption of a new modeling paradigm.”

He argues this demonstrates how attention-free systems can catch up to transformer performance “for orders-of-magnitude less” investment.

In the loss curves released by Manifest AI, Brumby’s training loss quickly converges to that of the Qwen3 baseline within 3,000 training steps, even as the architecture diverges significantly from its transformer origins.

Although Brumby-14B-Base began life as Qwen3-14B-Base, it did not remain identical for long. Manifest AI fundamentally altered Qwen3’s architecture by removing its attention layers—the mathematical engine that defines how a transformer model processes information—and replacing them with their new “power retention” mechanism. This change restructured the model’s internal wiring, effectively giving it a new brain while preserving much of its prior knowledge.

Because of that architectural swap, the existing Qwen3 weights no longer fit perfectly. They were trained to operate within a transformer’s attention dynamics, not the new retention-based system. As a result, the Brumby model initially “forgot” how to apply some of its learned knowledge effectively. The retraining process—about 3,000 steps of additional learning—served to recalibrate those weights, aligning them with the power retention framework without having to start from zero.

A helpful way to think about this is to imagine taking a world-class pianist and handing them a guitar. They already understand rhythm, harmony, and melody, but their hands must learn entirely new patterns to produce the same music. Similarly, Brumby had to relearn how to use its existing knowledge through a new computational instrument. Those 3,000 training steps were, in effect, its crash course in guitar lessons.

By the end of this short retraining phase, Brumby had regained its full performance, reaching the same accuracy as the original Qwen3 model. That quick recovery is what makes the result so significant: it shows that an attention-free system can inherit and adapt the capabilities of a transformer model with only a fraction of the training time and cost.

The benchmark progression plots show a similar trend: the model rapidly approaches its target accuracy on core evaluations like GSM8K, HellaSwag, and MMLU after only a few thousand steps, matching or even slightly surpassing Qwen3 on several tasks.

Benchmarking the Brumby

Across standard evaluation tasks, Brumby-14B-Base consistently performs at or near parity with transformer baselines of comparable scale.

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🔗 Sumber: venturebeat.com

Author: timuna