MAROKO133 Breaking ai: Adobe Research Unlocking Long-Term Memory in Video World Models wit

📌 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 Update ai: Terminal-Bench 2.0 launches alongside Harbor, a new framewo

The developers of Terminal-Bench, a benchmark suite for evaluating the performance of autonomous AI agents on real-world terminal-based tasks, have released version 2.0 alongside Harbor, a new framework for testing, improving and optimizing AI agents in containerized environments.

The dual release aims to address long-standing pain points in testing and optimizing AI agents, particularly those built to operate autonomously in realistic developer environments.

With a more difficult and rigorously verified task set, Terminal-Bench 2.0 replaces version 1.0 as the standard for assessing frontier model capabilities.

Harbor, the accompanying runtime framework, enables developers and researchers to scale evaluations across thousands of cloud containers and integrates with both open-source and proprietary agents and training pipelines.

“Harbor is the package we wish we had had while making Terminal-Bench," wrote co-creator Alex Shaw on X. "It’s for agent, model, and benchmark developers and researchers who want to evaluate and improve agents and models."

Higher Bar, Cleaner Data

Terminal-Bench 1.0 saw rapid adoption after its release in May 2025, becoming a default benchmark for evaluating agent performance across the field of AI-powered agents operating in developer-style terminal environments. These agents interact with systems through the command line, mimicking how developers work behind the scenes of the graphical user interface.

However, its broad scope came with inconsistencies. Several tasks were identified by the community as poorly specified or unstable due to external service changes.

Version 2.0 addresses those issues directly. The updated suite includes 89 tasks, each subjected to several hours of manual and LLM-assisted validation. The emphasis is on making tasks solvable, realistic, and clearly specified, raising the difficulty ceiling while improving reliability and reproducibility.

A notable example is the download-youtube task, which was removed or refactored in 2.0 due to its dependence on unstable third-party APIs.

“Astute Terminal-Bench fans may notice that SOTA performance is comparable to TB1.0 despite our claim that TB2.0 is harder,” Shaw noted on X. “We believe this is because task quality is substantially higher in the new benchmark.”

Harbor: Unified Rollouts at Scale

Alongside the benchmark update, the team launched Harbor, a new framework for running and evaluating agents in cloud-deployed containers.

Harbor supports large-scale rollout infrastructure, with compatibility for major providers like Daytona and Modal.

Designed to generalize across agent architectures, Harbor supports:

• Evaluation of any container-installable agent

• Scalable supervised fine-tuning (SFT) and reinforcement learning (RL) pipelines

• Custom benchmark creation and deployment

• Full integration with Terminal-Bench 2.

Harbor was used internally to run tens of thousands of rollouts during the creation of the new benchmark. It is now publicly available via harborframework.com, with documentation for testing and submitting agents to the public leaderboard.

Early Results: GPT-5 Leads in Task Success

Initial results from the Terminal-Bench 2.0 leaderboard show OpenAI's Codex CLI (command line interface), a GPT-5 powered variant, in the lead, with a 49.6% success rate — the highest among all agents tested so far.

Close behind are other GPT-5 variants and Claude Sonnet 4.5-based agents.

Top 5 Agent Results (Terminal-Bench 2.0):

1. Codex CLI (GPT-5) — 49.6%

2. Codex CLI (GPT-5-Codex) — 44.3%

3. OpenHands (GPT-5) — 43.8%

4. Terminus 2 (GPT-5-Codex) — 43.4%

5. Terminus 2 (Claude Sonnet 4.5) — 42.8%

The close clustering among top models indicates active competition across platforms, with no single agent solving more than half the tasks.

Submission and Use

To test or submit an agent, users install Harbor and run the benchmark using simple CLI commands. Submissions to the leaderboard require five benchmark runs, and results can be emailed to the developers along with job directories for validation.

harbor run -d [email protected] -m "<model>" -a "<agent>" –n-attempts 5 –jobs-dir <path/to/output>

Terminal-Bench 2.0 is already being integrated into research workflows focused on agentic reasoning, code generation, and tool use. According to co-creator Mike Merrill, a postdoctoral researcher at Stanford, a detailed preprint is in progress covering the verification process and design methodology behind the benchmark.

Aiming for Standardization

The combined release of Terminal-Bench 2.0 and Harbor marks a step toward more consistent and scalable agent evaluation infrastructure. As LLM agents proliferate in developer and operational environments, the need for controlled, reproducible testing has grown.

These tools offer a potential foundation for a unified evaluation stack — supporting model improvement, environment simulation, and benchmark standardization across the AI ecosystem.

🔗 Sumber: venturebeat.com