Meituan (LongCat)
Top signals
Agent answer
Meituan (LongCat) has 58 loaded public signals: 0 hiring, 6 forks, 20 releases or model cards, 0 talking, and 32 repos. Latest signal: meituan-longcat/LongCat-DeepResearch. Data-business radar is currently scoped to frontier labs, so this category does not expose radar lanes. The standing analysis was generated with deepseek-v4-pro and 92 evidence refs.
has loaded 58 public signals
has hiring signal count 0
has fork signal count 6
has release signal count 20
{"content":"## Thesis\n\nMeituan's LongCat lab is a full-stack frontier AI organization: it ships frontier-scale MoE language models (LongCat-2.0 at 1.6T total / ~48B activated parameters) while simultaneously building the inference systems, sparse-attention kernels, and evaluation suites those models require P5P6P24. Its most distinctive strategic bet is compute sovereignty — LongCat-2.0's training and deployment run entirely on AI ASIC / domestic "国产" compute clusters, claimed as the industry's first trillion-parameter model trained and served end-to-end on ~50k domestic accelerator cards P5W1W6. Around that core, the lab spans multimodal generation (video, image, audio, omni), reasoning/formal-math models, agentic deep-research systems, and an unusually broad public benchmark portfolio (VitaBench, AMO-Bench, Meeseeks, R-HORIZON, UNO-Bench, MineExplorer, SOP-Maze, PIE_bench, WBench, General365, LARYBench) P13P17P8P14P15P7P12P21E32E38E34. The pattern is "model–system co-design": LongCat releases models alongside the kernels and engines needed to serve them efficiently P24P3P4.