Based on our previous discovery on JustGoal, we redesigned how we do kernel optimisation and launched a self-evolving kernel optimisation harness called ForgeKernel, a part of our Forge series.
We tried ForgeKernel on SOL-ExecBench(FlashInfer-Bench) from NVIDIA and CANN-Bench from HUAWEI. It performed well and easily reached #1 in every list we tested.
Here are the ranks:
research.nvidia.com/benchmar…
cannbench.com/leaderboard
We'll release another paper on ForgeKernel soon.
Do we still need specialized agents for GPU kernel optimization?
We asked the question, and ran the experiment to answer it.
New paper with @THU_NLP: Agents under Goal mode are now able to beat systems deliberately built for kernel optimization. We named it JustGoal.
On FlashAttention, Gated DeltaNet, and DeepSeek Sparse Attention, a general frontier model (in our case, using Fable 5.1) delivers 1.08× to 1.58× speedup over expert-written references. Against the specialized KDA-0.5 (1.45× and 1.28×), Fable 5.1 hits 1.58× on both.
Frontier models are really burying the talent of kernel optimization experts in the past.
Paper: github.com/OpenBMB/JustGoal-…
At the frontier, the model capacity has caught up in optimising kernels.
The full paper and code will be available very soon. And huge props to the team — a creative collaboration between @THU_NLP and ModelBest's AI for AI Lab.
Sometimes the simplest questions lead to the most interesting answers.
Honestly, the results surprised us too.
Fable 5.1 performs the mission at or above on all three — 1.08× to 1.58× over the expert references. KDA-0.5, which is built specifically for this job, scores 1.45× and 1.28× on the same operators, while Fable 5.1, carrying zero kernel-specific machinery, hits 1.58× on both.
This means the general agents easily passed the best implementation within hours.
The default answer of kernel optimisation has been "specialised": each complex system wraps an LLM in elaborate domain machinery like curated knowledge bases, profiler integrations, and prescriptive workflows.
Now we have deleted the whole framework that may limit models.
A goal-mode agent, a correctness gate, and a four-line instruction telling the model to surpass the best implementation. That's all about Justgoal.
Testbed: FlashAttention, Gated DeltaNet, and DeepSeek Sparse Attention.
Yuxuan Li retweeted
China’s drone control system has gone insanely next-level. 33,615 drones completed high-precision flight simultaneously, looking like they were building a parallel world in the sky.
This has also refreshed the Guinness World Record that China set before.
Yuxuan Li retweeted
This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc.
More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage:
1) raw text (hard/effortful to read)
2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default
3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default
...4,5,6,...
n) interactive neural videos/simulations
Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral xitter.cf/zan2434/status/2046982…
There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen.
TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.
Yuxuan Li retweeted
📁 Fei-Fei Li, former Google Chief Scientist, says the industry is dangerously fixated on language models.
Most of the real economy is physical, perceptual and spatial.
Once AI fully understands the visual world, it stops being a chatbot and starts becoming infrastructure.
Introducing Daybreak: frontier AI for cyber defenders.
Daybreak brings together the most capable OpenAI models, Codex, and our security partners to accelerate cyber defense and continuously secure software.
A step toward a future where security teams can move at the speed defense demands.
Yuxuan Li retweeted
Watch Starship's eleventh flight test → spacex.com/launches/starship…
xitter.cf/i/broadcasts/1mrGmBjyE…
Yuxuan Li retweeted
OpenAI plans a new build with Oracle that would add 4.5 gigawatts of data-center capacity, an outgrowth of their “Stargate” program.
The Wall Street Journal reported OpenAI will pay Oracle $30 billion annually. The plan follows a 1.2-gigawatt site in Abilene, Texas. Selection of other data center sites is underway, with OpenAI and Oracle teaming with SoftBank and other partners including Microsoft and Nvidia.
Find out all the details in The Batch: hubs.la/Q03FZ5mc0
Yuxuan Li retweeted
Google is quietly reshaping how devs work.
These 5 AI agents automate BigQuery, Looker, GitHub, DB migrations & more, handling everything from data ingestion to PR reviews! 🔥
Here’s what each one can do 🧵 ↓
Yuxuan Li retweeted
Insane.
`Open Lovable` is an open-source Lovable OUT NOW! 🔥
Just paste a website URL, and AI agents will instantly create a working clone you can build on top of, all 100% open source.
Powered by GPT-5, plus models from Anthropic, Groq, and more!
Github repo in 🧵↓
Yuxuan Li retweeted
the task length an ai can reliably finish (conservatively) doubles every 7 months
when i'm the age my mom was when she watched me graduate, ai will be able to do tasks that would take someone ~1000 millennia
Yuxuan Li retweeted
Good news! GPT-5 should now be significantly faster in Cursor.
Big thank you to OpenAI for rolling out improvements to caching and API latency! P95 is ~2x faster versus before.
Yuxuan Li retweeted
We’re making GPT-5 warmer and friendlier based on feedback that it felt too formal before. Changes are subtle, but ChatGPT should feel more approachable now.
You'll notice small, genuine touches like “Good question” or “Great start,” not flattery. Internal tests show no rise in sycophancy compared to the previous GPT-5 personality.
Changes may take up to a day to roll out, more updates soon.


















