Introducing dots3-note preview â a small but mighty step toward long-horizon agency in real life.
đš 280B MoE with 16B active parameters, a 512K context window, and multimodal understanding across text, vision, and audio
đš Introduces TEMPO, a new RL approach for long-horizon agent training through self-critiquing and test-time-scaled value estimation
đš Built to reason, explore unfamiliar environments, update memory over time, and combine multimodal perception with coding and tool use to solve complex tasks
đš Open weights on Hugging Face, alongside two open benchmarks for real-life agents: VibeSearchBench and VibeLifeBench
Competitive with much larger models across reasoning, agentic, and multimodal evaluations.
đ Tech blog:
đ Model weights:
đ Github: