Overview
On August 14 at 7:25 PM (Shanghai time), Xiaohongshu's dots model lab released the weights of dots3-note Preview on Hugging Face and GitHub. It is the first open-source release in the dots3 series and the first publicly available weights from the model family that earned an official 42/42 gold score at the International Mathematical Olympiad (IMO) 2026.
Key specifications:
- 280B total parameters, 16B active parameters
- 512K context length
- Tri-modal understanding (text, vision, speech)
- Apache 2.0 license
- Model architecture submitted to Transformers
- Each task is split into multiple macro-steps, with multiple model–environment turns inside each macro-step.
- At the end of each macro-step, the same agent switches from actor to critic and uses test-time scaling to estimate the expected remaining return.
- +31.5% over the baseline checkpoint
- +20.6% over GRPO
- Cross-stage self-evaluation
- Long-horizon credit assignment
- Handling real-world constraints that arrive mid-task
- Reinforcement-learning training is still insufficient
- Hallucination suppression
- Balance between text and multimodal capabilities
- Stability
What was released
The open-source drop ships three components as a unified stack:
1. dots3-note Preview weights — the model itself. 2. VibeSearchBench and VibeLifeBench — two real-task evaluation environments. VibeSearchBench covers 20 domains across 200 tasks with a search / visit / code toolset. VibeLifeBench covers 10 domains with 20 cross-stage tasks (each spanning 20–30 stages) and 1,247 atomic checks that directly assess cross-stage state consistency, tool execution results, and final deliverables. 3. TEMPO — a training framework (Test-time-scaled Value Estimation with Macro-step Policy Optimization).
The three pieces reflect one thesis: long-horizon capability requires an evaluation substrate, a training paradigm, and a model — none alone is enough.
The problem it targets
Frontier models perform strongly on closed-loop, quickly-verifiable tasks (math, coding, engineering). Real-life long-horizon tasks — travel planning, renovation preparation, wedding organization — share four traits: no single correct answer, durations of hours to days, constraints that surface incrementally, and ongoing disruptions such as price and weather changes.
The release is positioned as pushing the model from "math competitor" to "executor of long-horizon life tasks."
TEMPO methodology
TEMPO trains long-horizon agents without relying on terminal sparse rewards:
This design makes "evaluation is easier than generation" actually hold for long-horizon settings.
Reported empirical results
On ARC-AGI-3, TEMPO averaged:
Notably, the strongest domestic and international closed models do not yet cross the pass line on VibeSearchBench and VibeLifeBench, which the authors frame as evidence that these benchmarks measure capabilities not captured by mainstream closed-form evaluations.
Ecosystem impact
Huawei announced same-day (0-Day) full compatibility of the vLLM Ascend engine on Atlas 800 A3 / 900 A3 SuperPoD, layering FlashComm communication optimization, FUSED_MC2 operator reconstruction, and MTP speculative decoding to push MoE inference throughput. This makes dots3-note a rare case of a domestic model paired with a domestic compute stack at the same generation and release window.
Why it matters
While IMO gold certifies the ceiling of single-shot reasoning, this release shifts the competitive axis toward deployment-relevant capabilities:
For the "self-controlled" narrative in government and enterprise scenarios, synchronized model-and-compute availability is arguably more consequential than another SOTA number.
Caveats and next steps
The team promises a full technical report within one week, disclosing training data scale, training compute, and item-by-item comparisons. Stated limitations include:
Sources
Xiaohongshu tech WeChat account (dots.llm), lifestyle.onebyfourstudio.com, aibase.com, ModelScope community.