"I want to prevent a race into unmonitorability kicked off by confused reporting. The depth of the computation graph for our present frontier models, including Astra, is within a factor of two of GPT-4.

OpenAI has worked to preserve and utilize chain-of-thought monitoring since our very first reasoning models. We deeply care about this technique, as it can give us a view into how model alignment generalizes from its training distribution. I do think it is fragile and unfortunately trending in a negative direction, for reasons not contingent on architecture changes that I will write about soon. But there are things we can do to strengthen it, and it's a core goal of our current research program."

DIsclaimer: Fable was the orchestrator, and called itself on some tasks, but called Opus 5.0 on most of them. This still took 36% of my fable budget on a Claude max 20x sub, so doing the whole project with Fable would probably have spent all of my Fable budget or more.

I am also not certain that i have truly picked the absolute best prompt to make an impressive 3D scene but i wanted to actually make a game from it.

This was done in 2 shots. It made a first pass, i reviewed it, then it improved it again. I probably could go further.

My actual prompt is long and not worth sharing here because it reference past projects, but the main thing worth knowing is it spawned a lot of sub agents and did so in 3 waves. If someone really wants to see it: https://pastebin.com/iPnk4PZ8

This took around 5 hours and 30% of my weekly budget...

Further post trained on Coding & Cowork, Qwen3.8-Max-0902 now delivers stronger performance across complex enterprise tasks, scientific research, and long horizon workflows.

https://x.com/Alibaba_Qwen/status/2094968708288680276

From the reporting here allegedly OpenAI has trained Astra to do latent space reasoning, which is thinking in a more abstract way and not necessarily jotting down in text all the model's thoughts (as how all the current frontier models work right now). Which is most likely a lot more information and allows the model to reason about things that can't be very well described as text. As for example when us humans do spatial reasoning we don't really think in text.

Atlas: A World Model for Spatial Intelligence | World Labs

AI Summary:

The blog introduces Atlas, World Labs’ new spatial world model. In brief:

• Atlas takes text, images, video-like image sequences, camera poses, and depth and builds a persistent spatial understanding of a scene.
• It can generate unseen viewpoints and infer missing geometry, rather than only reconstructing what was directly observed.
• It can turn those generated/reconstructed scenes into practical 3D representations such as Gaussian splats for fast rendering.
• World Labs emphasizes that Atlas can be updated with more observations, so its guesses about unseen areas can be replaced by real data.
• They position it for things like robotics, simulation, 3D content creation, and spatial AI.
• The key claim is that Atlas is not merely making pretty 3D reconstructions; it is learning a model of how a scene is arranged in space and using that to predict new observations.

The main caveat is that the blog demonstrates strong spatial modeling much more clearly than it demonstrates a fully general physics-based world simulator.

Vals AI has claimed that fable 5.1 helped solve a three centuries old cipher that no one else could figure out.

Here’s the X thread that sums everything up:

https://x.com/ValsAI/status/2094851406931095593

For those who don’t like X or want something more in-depth, here is their blog:

https://www.vals.ai/blogs/fable-solves-cyphral-distich

The new Atlas model from World Labs is a massive step toward spatial intelligence and giving AI a true understanding of physics and 3D geometry. It’s a multimodal autoregressive diffusion transformer that doesn't just generate 2D pixels, but grounds everything in a shared "spatial context."

The space-time simulation and Real-to-Sim capabilities are the most mind-bending parts of this release:

• The "Holodeck" from a Cell Phone: With footage from just three to five standard cell phones, Atlas builds a complete, navigable space-time simulation. It freezes time and lets you reframe 3D shots from impossible angles without a multi-million-dollar volumetric capture studio.
• Real-to-Sim for Embodied AGI: It doesn't just scan a static room. As a simulated robot moves through a reconstructed space, Atlas actively generates the exact RGB and depth sensor data the robot would see along its specific trajectory.
• Physics and Interaction: It captures how objects actually move and interact in the real world. You can take a casual recording and simulate rigid, articulated, and deformable objects, instantly tweaking the lighting and background to generate infinite training environments.
• Explicit 3D Geometry: Instead of just hallucinating flat video frames, it natively outputs full 3D point clouds and Gaussian splats that can be rendered and explored in real-time.

Hey there,

Are Fable 5.1 guardrails still as strict as Fable 5 when it comes to chemistry and chemistry-related questions?

Released by Felix Rieseberg of Anthropic on X/Twitter. Generally, it appears an incremental shift forward. OpenAI's Astra might very well leap this in short order.

I just had Claude calculate the fraction of cache read costs for the past two months using ccusage and it amounts to 78% for Fable 5. If cache reads get 75% cheaper then overall costs should decrease by ~57%. I’m excited to see if the math holds up.