Expired50% confidenceFactExact time
The Lingbot World model has a scale of 14 billion (14B) parameters, adopts a causal autoregressive architecture, and has been optimized for inference speed
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/11/2026
First Seen
Valid until: 10/9/2026(expired)
Sources
Lingbot World: A 14B Open-Weight World Model with Causal Architecture and Inference Acceleration
redditr/StableDiffusion7/9/2026
Related Claims
UnverifiedWhen model scale crosses from billions of parameters to the hundreds-of-billions level, non-linear emergent capability jumps manifest on code tasks.74% similarUnverifiedThe number of model parameters (e.g., 120B meaning 120 billion parameters) is a key indicator of large language model scale; more parameters generally means greater knowledge capacity and reasoning ability, but higher inference computation costs and slower response speeds.72% similarUnverifiedThinking Machines Lab has released an open-weight large language model with a scale of 975 billion parameters.70% similarUnverifiedCombining knowledge distillation with quantization can enable a billion-parameter-scale model to retain 70–85% of the original large model's capabilities while achieving a 5–10× improvement in inference speed.70% similarUnverified思维链提示与少样本示例可提升7B至14B参数量级模型在复杂编码任务上的成功率69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/477990API
curl https://kongchang.com/api/v1/knowledge/claims/477990MCP
get_claim(id=477990)