Baize Tongjing, a Hefei-based company founded less than six months ago by a team from USTC, has published a paper titled "Abductive World Modeling via Causal Representation Learning" and open-sourced its code, saying its AWM model not only predicts future states but also asks what caused them, decomposing a scene into entities, dynamics and relations. The company says AWM reached a 75% success rate on the Push-T robotic manipulation task with only 2,000 training trajectories, versus 14,728 for the comparison method Causal-JEPA, and about 92% when given the same 14,728 trajectories. Its companion edge inference engine BAIZ-RUN quantizes weights to 2 bit and activations to 4 bit; the team reports a 2.56x reduction in memory use, a 94.6% increase in inference frequency and under 1% accuracy loss on an AMD Radeon edge integrated GPU with the SSV2 dataset. The company also says it has signed and validated three industrial scenarios — loading/unloading, quality inspection and plant patrol — and closed a first funding round of tens of millions of RMB within three months of founding, with backers including SMIC Capital and a market-oriented fund whose LP is Ke Da Silicon Valley.