X日报 · AI科技

Added to prinzbench: GPT-5.6 Sol Pro.

As previewed a few days ago, this model has saturated my benchmark, with a total score of 91/99.

For context, prinzbench contains two questions that no model tested to date has ever been able to solve (one requires extremely thorough 50-state research that probably requires /goal mode to solve, and another has a really tricky regulatory approval that no model has ever been able to find). Putting these two questions (which are worth 6 points) aside, GPT-5.6 Sol Pro provided correct responses to 91 out of 93 prinzbench questions.

prinzbench performance for OpenAI's Pro models:

GPT-5.4 Pro (Extended): 79/99
GPT-5.5 Pro (Extended): 82/99
GPT-5.6 Sol Pro: 91/99

My benchmark was released in January 2026 and was saturated in June 2026. The acceleration is real!

As a result of this model's performance, future OpenAI Pro models will no longer be tested on prinzbench (there is no point in testing them).

Benchmarking for other GPT-5.6 models to follow soon(TM).

@gdb our benchmark shows that Sol ranks #1 is 6x more cost efficient than Fable across React/frontend work

GPT-5.6 Sol, Terra, and Luna from @OpenAI are now generally available on Amazon Bedrock. Three tiers of intelligence, from flagship reasoning to fast inference, running on Bedrock's next-generation inference engine built for high-performance, security, scale, and reliability. https://t.co/Hn2z5M2Jp2

AI has helped resolve an important question in statistics. In the area of multiple hypothesis testing, the goal of controlling the false discovery rate (FDR) has been introduced in a seminal paper by Benjamini and Hochberg (1995). They also introduced a method (the Benjamini-Hochberg or BH method) and proved it controls the FDR. This method has been widely adopted in modern high-throughput science, including in genomics, astronomy, economics, etc. The paper has has garnered more than 130,000 citations to date.

However Benjamini and Hochberg showed FDR control only when the data for the individual tests are *independent*. In practice, these data are often dependent; a good example is data on genetic variants due to linkage disequilibrium. Later work has focused on extending the validity of the BH procedure, e.g., to a form of positive dependence by Benjamini and Yekutieli (2001).

The question of when the BH procedure controls the FDR has remained open. Over the last twenty years, many authors, including Reiner-Benaim (2007), Kim and van de Wiel (2008), Benjamini (2010), Sarkar (2023), Sarkar and Zhang (2025), have conjectured that the BH procedure controls the FDR for two-sided tests using any correlated Gaussian data. These authors have presented both theoretical and empirical evidence supporting, but not directly showing, the conjecture.

With the help of AI (specifically GPT-5.6 Sol Pro), I have settled the question in the negative: The Benjamini-Hochberg procedure does *not* generally control the false discovery rate at the desired level for correlated two-sided Gaussian tests. This was done by exhibiting a Gaussian factor model for which, at a nominal level alpha=0.01, the false discovery rate is proved to be FDR>0.0104.

There is a lot of interesting commentary to be made:

1. This result should be of interest to everybody in the field of statistics. Emmanuel Candes of Stanford University once called the false discovery rate and the Benjamini-Hochberg procedure "one of the two most important developments in statistics after 1950" (the other being James-Stein shrinkage). The present conjecture is probably the most central question about FDR/BH that was unresolved to date.

2. GPT-5.6 one-shot the problem after 90 minutes of reasoning, whereas with 5.5 I was not able to solve it even after iterating with multiple parallel agents for perhaps 20 hours. So the capability improvement is quite real. Exciting times to live in!

3. The argument is not especially surprising, but it does combine an asymptotic approach (standard for FDR analysis, see e.g., Genovese and Wasserman, Efron, etc) with a numerical certificate in a way that would be pretty non-standard in the field. Once we have the specific example, then straightforward simulations also support that the false discovery rate is indeed higher than the nominal value (see attached fig).

4. The current degree of violation over the nominal level is relatively small (0.104 vs 0.1). So the importance of this result is mainly conceptual. The practical implications remain to be determined.

Overall, an exciting development! Preprint is available here (https://t.co/YgiwgDF2qr) and will be on arxiv tonight; supporting code is here (https://t.co/KZhj15qDXC).

Today, we are introducing Inkling.

Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.

https://t.co/Ghebq5mG30

Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

Today, we are introducing Inkling.

Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.

https://t.co/Ghebq5mG30

Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

first open weight thinking machine model!! 975B total, 41B active trained on 45T tokens, 1M context, multimodal in

sliding window with a 5:1 ratio and 512 size, deepseek aux-free load balancing and 2 shared experts (usually people only use 1), actually curious why the model is less sparse than kimi (~4.2% vs 3.2%). they use a short convolution after k and v, output and ffn (see plot), muon (they cite manifold muon but mention weight decay so not sure), muP, and have a very nice RL scaling curve and chain of thought!

one very cool part of the release imo is how well their small variant (276B total, 12B active) performs compared to the big one. they mention they changed the pre-training data mix and recipe, very curious about those changes and to see them scaled up to ~1T (or more?) soon 👀

> "It is not the most performant model available today, closed or open. We trained Inkling for solid capabilities across the board rather than state-of-the-art performance in a single area, to serve as a foundation for the models we will train in the future."

also this is really refreshing to see in a model release, huge congrats :)

Inkling by @thinkymachines is now available on Modal, backed by a custom DFlash speculator for 67% higher throughput and interactivity.

Running on Modal Auto Endpoints with SGLang today. https://t.co/OxN7aJ9ieW

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

i really like openai's new chatgpt "Work" feature

not everything involves programming, as sometimes you need help with project planning, content review, research, PDFs, or spreadsheets

so i'd summarize this:

need an answer? chat
need to code? codex
need to plan, research, or review? work

a lot of people haven’t realized how much more capable Chat has become in the last few days

how much of Codex is now available directly inside @ChatGPTapp on mobile and the web.

the Codex desktop app is great. I obviously live there.

but now, inside ChatGPT, you can tap Work on mobile or the web to hand off a task that runs in the cloud, no computer needed.
you can also tap Remote to pick up and continue work already running on your computer.

start at your desk, keep things moving on your phone, or kick off something new wherever you are.

agents shouldn’t care which screen you started from.

Or… what if we gave you $100 in Codex credits if you tell us what you love about GPT-5.6 Sol or why you switched?

Tweet it, claim your gift, enjoy more usage. First 10k get the free tokens!

https://t.co/8mU93eA13i

Thank you to the 7M active users who are now using Codex and ChatGPT Work.

We have added a banked reset to everyone's account to celebrate the milestone. You can apply the reset in the desktop app or on web and it will replenish the weekly usage for you.

Have fun out there.

There’s hope in hard questions. https://t.co/rDYjqIlH9l

Try out the Visualize plugin (in preview) in Codex.

Some ideas click faster when you can see and interact with it.

Here’s a fun example where I asked Codex to build a planet simulator with different controls. 🌎 https://t.co/Ezzi9YJJve

Tell Codex to open Microsoft Paint and try to draw you https://t.co/6NL1sAVvQ0

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos

then, told it to find new outfits for me and render them on me with gpt-image!

its kinda cool to see your entire wardrobe in a collection like this https://t.co/SV796uScrB