TREE NEWS update: Daxiao Robot released ACE-Brain-0.6, an embodied foundation model built by supervised fine-tuning on Qwen3.5-9B, focused on improving object recognition, spatial localization and interaction-region judgment. The model was trained on roughly one million spatial anchoring samples and beat OpenAI’s GPT-6 Astra on six of eight grounding evaluations, Daxiao disclosed. It scored 74.53 on average across seven public benchmarks versus 73.68 for GPT-6 Astra, and 94.13 on its own RobotGrounding evaluation.
Daxiao Robot releases ACE-Brain-0.6, tops GPT-6 Astra on six spatial tests
The notable point is not the benchmark win but the method: a 9B model fine-tuned on roughly one million spatial anchoring samples edging out a frontier system on grounding, which suggests embodied spatial reasoning may reward targeted data curation more than raw scale. The margin is thin, and the strongest number comes from Daxiao's own RobotGrounding evaluation, so independent replication is the open question. Watch whether grounding-focused, smaller models become the default for robotics teams and whether GPT-6 Astra's camp responds with comparable spatial benchmarks.
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