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Xiuyu Li
@sheriyuo
Researcher @StepFun_ai | Working on long-horizon tasks | Opinions are my own
1.9K Following    15.3K Followers
ICLR 2027 takeaway: Fully automated AI > Human + AI > Human-only Humanity can go extinct now.
“A powerful new model from StepFun might be just around the corner 👀👀”
I visited @StepFun_ai's Shanghai office yesterday and had such a great time. Everyone was so welcoming, and they handled even my tougher questions with a smile. A powerful new model from StepFun might be just around the corner 👀👀 Thanks to @cherry_gong3 for inviting me!
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Let me share why I’m bullish on @StepFun_ai too. We’ve got the best homies here! @liulicheng10 and @MikaStars39 Btw, @liulicheng10 was supposed to publish a technical… blog? Still waiting lol. Someone get licheng to post it! Okay, jokes aside, I love it here. Our whole post-training team is one big family.
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Time to share why I am bullish on @StepFun_ai (not “Step” though): 1. Deep research root from ResNet to scaling Laws, and many works on multimodality 2. Years of experience building AI infra and training intelligent and efficient models at scale 3. Models already powering AI on phones and cars So potential has solid foundation. Proud of the whole team!
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DeepSeek is finally cooking up their own merch. Previously: DeepSeek pens, and cultural shirts with the DeepSeek logo printed on the reverse side (note: not the back, but the reverse/inside). Now: the Big Fat Fish. (Note: AIGC-generated characters have no copyright, so it’s actually that logo.)
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Got a chance to visit the @deepseek_ai office. The team is cooking!
DeepSleep 😴 But no way it’s V4.2F. It wouldn’t be the kind of upgrade V3.2 was.
I think we'll see V4.1F-1125 or 1210, or maybe even V4.2F, before we see V4.1 Pro or any other DeepSeek-Pro. They probably are scaling it up with their new compute.
Introducing SceneBound: How Much Should a Streaming 3D Reconstruction Model Remember? SceneBound is a training-free rule that decides what a streaming 3D reconstruction model keeps in memory. Models such as CUT3R, StreamVGGT, STream3R and ZipMap read a video frame by frame and carry the past in a recurrent state, fast weights or a key-value (KV) cache, and every frame written there shifts the coordinates of later cameras or takes GPU memory. SceneBound stores a frame only when the camera reaches a new place: its predicted pose has to lie at least a radius M from every stored keyframe. Memory then grows with the places visited and stays flat when the camera returns. One radius serves four released models and three memory types. Against the best published training-free rules we test: • in one pass, 9% lower error in CUT3R's recurrent state • in one pass, 18% lower error in StreamVGGT's KV cache • after nine passes of a scene, 32% lower error with 11× less writing in the state • after nine passes, 11% lower error with 2.2× less GPU memory in the cache On 11 KITTI drives, its submaps cut the error of one state by 71%. Paper: Delighted to work with Chuyue @artrix_fsc , Chutian, Jingmin @KleinJoe219384 , Hongliang @0xHongliang , Yuheng @eiaton84855 and Xiuyu @sheriyuo. The discussion with Lilo @EpiphanyFall was also very helpful.
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Shanghai office 🥰
I visited Kimi’s Shanghai office yesterday and had such a great time. I could see why places like this are called “labs.” There was a sense of youthful energy and freedom grounded in discipline. Everyone was so warm and welcoming. I’m truly grateful for their hospitality. And a special thank you to @crystalsssup for inviting me.
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waiting for another step 😇
Okay so basically this tells Step 5 Preview tends to have fewer hallucination and more reliability. That's good.
So who’s gonna win this one? AA Index or Vals Index? Oh wait, who is user?
this tb4 story keeps getting wilder. both vals ai and aa use mini-swe-agent harness, but the ranking is completely different: vals ai: opus 5.5 > sonnet 5.5 > kimi k3.1 pro > gemini 4 carbon aa: kimi k3.1 pro > gemini 4 carbon> sonnet 5.5 > opus 5.5 you can't make this up.
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I have a fun theory. Just speculation, so don’t take it as actual model info. If you knew how big Sol and Astra were, you could probably work out Bel’s size too. Then you’d see why I don’t buy rumors like “it’s over 10T parameters and as big as GPT-4.5.” You’d also have a better idea of whether OpenAI could actually release Bel.
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Dougtrio for three loops? These rumors are so goofy, might well be true (I am still skeptical)
from an AA guy 👀
I’m not worried about “benchmaxxing” Structurally, you are at a disadvantage. You’ll never outpace us; we will always have newer tasks.
you won and I’m the #1# fan of gemini 🥰
Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.
Yeah TB-Sci and HLE-Diamond are both pretty solid benchmarks. They show where Chinese models still lag. Multimodal stuff is getting more valuable too, and v5 is at least a solid improvement. Really though, O/A has been prepping for these benchmarks for a while lol. This is basically their optimization direction. Chinese models will catch up soon enough.
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open models took the big hit from the new benchmarks in aa index v5: tb-sci hle-diamond and frontiercode.
We are hosting the tutorial Failure Modes in Agentic AI at #EMNLP2026# in room P3 As agents start to challenge human intelligence, the least to do is to know when they fail before we cannot understand how they succeed So sad I can't join in person but I'll present via zoom. Come join us @ZoeyLi20 @HennyJieCC @ManlingLi_ @qiancheng1231 !
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Cool. Stay excited! 🫡
Really enjoyed “Are we running too many agents” hosted by @exedev, with great talks from Josh Synder from Josh Wills from Datology AI, and @beyang from @AmpCode. A major topic was about the human side of working with agents (sometimes with too many agents lol) I resonate with the main point by Josh from - running more agents usually means more context switching and giving more attention to them instead of focusing more on understanding your work. We definitely need to understand the agent systems we’re using with more attention, and take full responsibility for what it does. Also learnt a lot from Josh Wills’ advice: use agents to complement our bad habits at work. For example, we can ask for simplicity from our agents when we often overcomplicate tasks. Then @beyang’s talk about Amp shares some interesting finding about the agent workflow: making parallel work effective means improving the whole agent setup, including managed sandboxes and better ways for users to interact with Agents workflow. Agents could generate multiple options so users can compare at once, and they can show diagrams to help us understand the changes. These are all parts that make human–agent collaboration more useful in practice. Excited to explore how StepFun can support workflows like these.
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FrontierCS 2 really is a solid benchmark. Step 5 Preview looks… rough, but we’ll focus on improving that! Thanks for the support. btw the harness does affect the results a bit too lol.
Kimi K3.1 Pro ties GLM 5.3 on pass rate (12/48) and edges it out on continuous score (50.9 vs 48.9), making it the best open-source model on FrontierCS 2's open-ended CS research tasks 🌙
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Inside Step 5 Preview: Architecture and Training for Agentic Work Blog:
Have a try and cook step by step 🥳
That's what makes synthetic data so fascinating, even with the performance drop. Curious how this would play out beyond Qwen.
My account is finally back. First, a security reminder: please use a unique, randomly generated strong password for every important account. Do not rely on 2FA alone. 2FA can be bypassed, and I strongly recommend using a passkey. Here’s what happened: On Sep 30, I received an email about a new login to my X account. Unfortunately, I didn’t pay enough attention to it at the time. On Oct 1, my account was compromised despite having 2FA enabled. My Gmail account remained under my control the entire time, and I have never entered my credentials on any phishing/scam website. As far as I can determine, the attacker only had my password. How they managed to bypass 2FA and take over the account is still unclear to me. About 10 minutes after the takeover — while I was still on vacation — I received an email from Kimbo Chen telling me that my account appeared to have been hacked. Huge thanks to him for noticing and reaching out so quickly. When I checked, my password had already been changed and a large number of scam links had been sent through my Chats. What made the situation even worse was that the Chat passcode could apparently be reset directly, while I myself could no longer access my own Chats. I immediately opened my first ticket with X Support. The initial response essentially said that because I could still access the account at that point, they would not take further action. Later that same day, I lost all access to the account. On Oct 6, I submitted another hacked-account ticket. The suspension on this account was subsequently lifted, but during this period @GenAI_is_real was targeted by the scam and nearly had their account compromised as well. This whole situation was crazy. Through the new support ticket, I was eventually able to reset the password and regain control of the account. The account is now back to normal. I sincerely apologize for the impact caused by my poor security practices. More than 100 accounts were affected or contacted by the attacker, including @MIT_CSAIL (which has now blocked me), and @GenAI_is_real, who thankfully avoided losing their account. I’m extremely grateful to everyone who warned me, helped report the account, or offered assistance during the past week. Special thanks again to Kimbo Chen, and to my colleague @frankyouchill for taking time out of an already busy schedule to help me deal with this mess. Again: use unique random passwords, and use passkeys whenever possible. Treat 2FA as an additional layer of protection, not something that makes an account impossible to compromise. Everything on this account should now be back to normal. Thank you all for your help, and I hope everyone stays safe and has a great day.
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