Xudong Guo

Researcher @Alibaba_Qwen | Ph.D. @Tsinghua_Uni | Prev. @Microsoft @UN @Princeton | Test-Time Scaling of AI Agent

[Xudong Guo @_traceur__]

Qwen3.7-Max brings major leaps in long-horizon & self-evolving capabilities. We tasked it with optimizing a kernel on Alibaba’s T-Head PPUs—hardware unseen in training. After ~35 hrs of autonomous execution, it reached 10x speedup across 1,158 tool calls.
#Qwen #AI #Agent #LLM

[Xudong Guo @_traceur__]

Try Qwen3.8-Max to design chips, analyze docs, leave the work to it, and enjoy life! ☕️

[Qwen @Alibaba_Qwen]

Meet Qwen3.8-Max: A New Bar for Coding and Cowork.

[Xudong Guo @_traceur__]

Qwen3.8-Max autonomously handled full-chip design — RTL editing, simulation debugging, synthesis, layout — for a crypto accelerator

500 rounds, 71 evaluations, 13 milestones. Gate count: 8,298 → 678. Area: 106×106µm² → 46×46µm² (-81%). Timing: -4.46ns slack → +0.66ns @500MHz

[Xudong Guo @_traceur__]

Long-horizon RL generalization never stops surprising us

500 rounds of chip design iteration — using EDA tool feedback like a real engineer, shrinking chip area to 1/5

Also runs e-commerce stores for a year — handling dynamic demands, negotiations, real-world business decisions

[Qwen @Alibaba_Qwen]

📢Meet Qwen3.8-Max — our most capable model to date.

Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉

Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:

- Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub：https://t.co/iVHZWQoeSo
- Real work, real results: Production-quality deliverables across hundreds of professions.
- Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
- Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.

💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens

Start building with Qwen3.8-Max! 🚀

📖 Blog: https://t.co/iwjmQxLBof
✅ Qwen Studio: https://t.co/4V2pFvDovG
⚡ API: https://t.co/gAGqaLQGbN

[Xudong Guo @_traceur__]

Imagination is something that AI is still not good at, at least for now.

[Junyang Lin @JustinLin610]

ai making ai better recursively is an impressive idea but sometimes depressing as human researchers seem to become less and less significant in this process. i do believe that there is great potential in this direction while human supervision will become more principled and higher-level. then imagination and vision matters more than ever.

[Qwen @Alibaba_Qwen]

👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one versatile agent foundation.

✅ Multimodal interactive hybrid agent: unified GUI & CLI operation across visual and text tasks
✅ Versatile coding agent & productivity assistant with full-modality input
✅ Visual Agent: perception, reasoning, grounding, and search-augmented QA
✅ Cross-harness generalization across diverse agent frameworks

One model. Sees, thinks, codes, acts.🙌🙌

Now available via API on Alibaba Cloud Model Studio. Try it — let us know what you build.😎

🔗🔗⬇️⬇️
Blog：https://t.co/pVYf0h3NNa
Qwen Studio：https://t.co/HUYgFW4cYf
API：https://t.co/viL0cXrMzW

[Xudong Guo @_traceur__]

Qwen3.7-Max pushes the Pareto frontier of in-context learning to a new level 🚀 Even beats Opus 4.7 and GPT 5.5 with less cost

[atomic.chat @atomic_chat_hq]

Qwen 3.7-max beats Opus 4.7 and GPT-5.5

We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head.

Qwen 3.7-Max: training cost $1.32, bot improvement +56%
Claude Opus 4.7: training cost $12.15, bot improvement +28%
GPT-5.5: training cost $2.85, bot improvement +7%

Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

[Xudong Guo @_traceur__]

@kimmonismus Also, we got the 10x speedup on Alibaba's brand new PPU chips, never seen during training

[Xudong Guo @_traceur__]

@rohanpaul_ai we got the 10.0x speedup on unseen PPUs instead of the training GPUs. Qwen 3.7 was learning while exploring.

[Xudong Guo @_traceur__]

Scaling still works

[Artificial Analysis @ArtificialAnlys]

Alibaba’s new Qwen3.7 Max model scores 56.6 on the Artificial Analysis Intelligence Index, 4.8 points higher than Qwen3.6 Max Preview (51.8). While Alibaba still trails models from OpenAI, Anthropic and Google, Qwen3.7 Max is the closest they have been to the frontier

Qwen3.7 Max is @Alibaba_Qwen's latest proprietary flagship, scoring 56.6 on the Intelligence Index, a 4.8 point gain over Qwen3.6 Max Preview (51.8) released in April. Qwen3.7 Max continues Alibaba's pattern, in place since Qwen2.5 Max (January 2025), of releasing Max and Plus models as closed weights while the rest of the Qwen line remains open weights. The leading open weights Qwen on the Intelligence Index is Qwen3.6 27B (Reasoning, 45.8) released in April 2026, and the leading open weights MoE Qwen is Qwen3.5 397B A17B (Reasoning, 45.0) released in February 2026

Key takeaways for the reasoning variant:
➤ The Intelligence Index gains over Qwen3.6 Max Preview are concentrated in scientific reasoning, agentic capability and coding. CritPt +9.7 p.p (3.7% to 13.4%), HLE +9.2 p.p (28.9% to 38.1%), TerminalBench Hard +6.9 p.p (43.9% to 50.8%) and GDPval-AA +42 Elo (1504 to 1546). Scores on other benchmarks in the Intelligence Index are flat compared to Qwen3.6 Max Preview

�� A significant share of the Intelligence Index gain is driven by higher abstention on AA-Omniscience, not higher accuracy. Qwen3.7 Max's accuracy on AA-Omniscience dropped 7.6 p.p (37.7% to 30.1%), while its hallucination rate dropped 21.3 p.p (44.2% to 22.9%). The model is choosing not to answer more questions rather than recalling more facts. Because hallucination rate and accuracy both feed into the Intelligence Index, the hallucination reduction is one of the larger single contributors to the +4.8 point gain on the Intelligence Index

➤ Qwen3.7 Max used 96.7M output tokens to run the Intelligence Index, ~31% more than Qwen3.6 Max Preview (73.9M). It sits mid-pack on frontier token usage: above GPT-5.5 (high, 44.5M) and Gemini 3.1 Pro Preview (57.3M), below Claude Opus 4.7 (Adaptive Reasoning, Max Effort, 112M), Kimi K2.6 (166M) and DeepSeek V4 Pro (Reasoning, Max Effort, 187M)

Key model details:
➤ Context window: 1M tokens (up from 256K on Qwen3.6 Max Preview)
➤ Multimodality: Text input and output only
➤ Pricing: Yet to be announced (Qwen3.6 Max Preview is priced at $1.30/$7.80 per 1M input/output tokens on the @alibaba_cloud first-party API)
➤ Licensing: Proprietary, closed weights

[Xudong Guo @_traceur__]

@0xsachi Yes! And we are working on more long-horizon real-world tasks. Stay tuned for the next version!

[Xudong Guo @_traceur__]

Qwen3.7-Max can also power embodied agents: controlling robot dogs via tool calls w/ real-time perception, planning, memory & decision-making in physical worlds, to finish long-term tasks more than 20 mins.

[barry (xiong-hui) chen @xiong_hui_chen]

One more thing 🚀

Qwen’s agentic capability is no longer limited to the digital world — we’re bringing it into physical world.

With our in-house robotic agentic system and navigation model, Qwen can now control a robot to execute tasks in real-world.
#qwen #embodied #robotics

[Qwen @Alibaba_Qwen]

📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era.

A versatile foundation for agents that actually get things done:
🧑‍💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it.
🗂️ A reliable office and productivity assistant. Get your work done through MCP integrations and multi-agent orchestration.
⏱️ Long-horizon autonomy. 35 hours straight on a kernel optimization task — 1,000+ tool calls, zero hand-holding.
🔌 Scaffold-agnostic. Claude Code, OpenClaw, Qwen Code, or your own stack. Consistent reliability everywhere.

API's up on Alibaba Model Studio. You can also take it for a spin on Qwen Studio.

Go build something wild!🏃🏃‍♂️

📖 Blog: https://t.co/y3AupX3Pa0
✅ Qwen Studio: https://t.co/qpTnrCBjWt
⚡️ API：https://t.co/0sys00osKn

[Xudong Guo @_traceur__]

Blog: https://t.co/uBX3kGngch

[Xudong Guo @_traceur__]

Qwen3.7-Max brings major leaps in long-horizon & self-evolving capabilities. We tasked it with optimizing a kernel on Alibaba’s T-Head PPUs—hardware unseen in training. After ~35 hrs of autonomous execution, it reached 10x speedup across 1,158 tool calls.
#Qwen #AI #Agent #LLM

[Qwen @Alibaba_Qwen]

🚀🚀Qwen3.7 Preview lands on Arena ！

Here come Qwen3.7-Max-Preview & Qwen3.7-Plus-Preview. Alibaba now #6 lab in Text, #5 in Vision.⚡️⚡️

Can't wait to release Qwen3.7 series models！Stay tuned! @arena

[Xudong Guo @_traceur__]

Try evaluate your model here!

[Xudong Guo @_traceur__]

Planning + Reflection + Memory -> Long-Horizon Decision-Making

[Qwen @Alibaba_Qwen]

🚀 Introducing DeepPlanning — a new benchmark for long-horizon agent planning in real-world scenarios.
Unlike step-by-step reasoning tasks, we focus on verifiable global constraints: time budgets, cost limits, and combinatorial optimization that must hold across the entire plan.
✈️ Multi-day travel w/ minute-level scheduling + hard time/budget caps
🛒 Complex shopping w/ coupon stacking & item bundling
🧠 Requires active info gathering, local constraint satisfaction & global optimality
Even GPT-5.2, Claude 4.5, Gemini & Qwen3 struggle significantly.
Perfect for evaluating Agent Planning / Tool Use / Long-Horizon Reasoning.
Paper: https://t.co/5tmVFtoFT7
Leaderboard: https://t.co/SxUh1TBXFq
Hugging Face Dataset: https://t.co/I804bZsXxN
ModelScope Dataset:
https://t.co/NIjVSOfWlE

[Xudong Guo @_traceur__]

@Shawnhhhhh There is no separate memory module, but we introduce the memory capacity to both the new model Max and the Qwen-Chat platform.

[Xudong Guo @_traceur__]

Try agentic memory here: https://t.co/lwBDIc3Ygf

[Qwen @Alibaba_Qwen]

🚀 Introducing Qwen3-Max-Thinking, our most capable reasoning model yet. Trained with massive scale and advanced RL, it delivers strong performance across reasoning, knowledge, tool use, and agent capabilities.
✨ Key innovations:
✅ Adaptive tool-use: intelligently leverages Search, Memory & Code Interpreter without manual selection
✅ Test-time scaling: multi-round self-reflection beats Gemini 3 Pro on reasoning
✅ From complex math (98.0 on HMMT Feb) to agentic search (49.8 on HLE)—it just thinks better.

🧠 Think deeper. Solve harder.
Try the adaptive reasoning experience now: https://t.co/V7RmqMaVNZ

Completions API:  https://t.co/Eo8DZdw4ac
Responses API:  https://t.co/ocUfhvT3M8

blog:  https://t.co/l7MYH3pgWm

[Qwen @Alibaba_Qwen]

Merry Qwristmas! 🎄🎁
Huge thanks for all the love and support this year.
Get ready for Qwen’s New Year surprises! 🎆✨
See you next year — with even more Qwen magic. ✨