MAROKO133 Eksklusif ai: GPT-5.2 first impressions: a powerful update, especially for busin

📌 MAROKO133 Update ai: GPT-5.2 first impressions: a powerful update, especially fo

OpenAI has officially released GPT-5.2, and the reactions from early testers — among whom OpenAI seeded the model several days prior to public release, in some cases weeks ago — paints a two toned picture: it is a monumental leap forward for deep, autonomous reasoning and coding, yet potentially an underwhelming "incremental" update for casual conversationalists.

Following early access periods and today's broader rollout, executives, developers, and analysts have taken to X (formerly Twitter) and company blogs to share their first testing results.

Here is a roundup of the first reactions to OpenAI’s latest flagship model.

"AI as a serious analyst"

The strongest praise for GPT-5.2 centers on its ability to handle "hard problems" that require extended thinking time.

Matt Shumer, CEO of HyperWriteAI, did not mince words in his review, calling GPT-5.2 Pro "the best model in the world."

Shumer highlighted the model's tenacity, noting that "it thinks for **over an hour** on hard problems. And it nails tasks no other model can touch."

This sentiment was echoed by Allie K. Miller, an AI entrepreneur and former AWS executive. Miller described the model as a step toward "AI as a serious analyst" rather than a "friendly companion."

"The thinking and problem-solving feel noticeably stronger," Miller wrote on X. "It gives much deeper explanations than I’m used to seeing. At one point it literally wrote code to improve its own OCR in the middle of a task."

Enterprise gains: Box reports distinct performance jumps

For the enterprise sector, the update appears to be even more significant.

Aaron Levie, CEO of Box, revealed on X that his company has been testing GPT-5.2 in early access. Levie reported that the model performs "7 points better than GPT-5.1" on their expanded reasoning tests, which approximate real-world knowledge work in financial services and life sciences.

"The model performed the majority of the tasks far faster than GPT-5.1 and GPT-5 as well," Levie noted, confirming that Box AI will be rolling out GPT-5.2 integration shortly.

Rutuja Rajwade, a Senior Product Marketing Manager at Box, expanded on this in a company blog post, citing specific latency improvements.

"Complex extraction" tasks dropped from 46 seconds on GPT-5 to just 12 seconds with GPT-5.2.

Rajwade also noted a jump in reasoning capabilities for the Media and Entertainment vertical, rising from 76% accuracy in GPT-5.1 to 81% in the new model.

A "serious leap" for coding and simulation

Developers are finding GPT-5.2 particularly potent for "one-shot" generation of complex code structures.

Pietro Schirano, CEO of magicpathai, shared a video of the model building a full 3D graphics engine in a single file with interactive controls. "It’s a serious leap forward in complex reasoning, math, coding, and simulations," Schirano posted. "The pace of progress is unreal."

Similarly, Ethan Mollick, a professor at the Wharton School of Business at the University of Pennsylvania and longtime LLM and AI power user and writer, demonstrated the model's ability to create a visually complex shader—an infinite neo-gothic city in a stormy ocean—via a single prompt.

The Agentic Era: Long-running autonomy

Perhaps the most functional shift is the model's ability to stay on task for hours without losing the thread.

Dan Shipper, CEO of thoughtful AI testing newsletter Every, reported that the model successfully performed a profit and loss (P&L) analysis that required it to work autonomously for two hours. "It did a P&L analysis where it worked for 2 hours and gave me great results," Shipper wrote.

However, Shipper also noted that for day-to-day tasks, the update feels "mostly incremental."

In an article for Every, Katie Parrott wrote that while GPT-5.2 excels at instruction following, it is "less resourceful" than competitors like Claude Opus 4.5 in certain contexts, such as deducing a user's location from email data.

The downsides: Speed and Rigidity

Despite the reasoning capabilities, the "feel" of the model has drawn critique.

Shumer highlighted a significant "speed penalty" when using the model's Thinking mode. "In my experience the Thinking mode is very slow for most questions," Shumer wrote in his deep-dive review. "I almost never use Instant."

Allie Miller also pointed out issues with the model's default behavior. "The downside is tone and format," she noted. "The default voice felt a bit more rigid, and the length/markdown behavior is extreme: a simple question turned into 58 bullets and numbered points."

The Verdict

The early reaction suggests that GPT-5.2 is a tool optimized for power users, developers, and enterprise agents rather than casual chat. As Shumer summarized in his review: "For deep research, complex reasoning, and tasks that benefit from careful thought, GPT-5.2 Pro is the best option available right now."

However, for users seeking creative writing or quick, fluid answers, models like Claude Opus 4.5 remain strong competitors. "My favorite model remains Claude Opus 4.5," Miller admitted, "but my complex ChatGPT work will get a nice incremental boost."

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif 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